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AWS AI & ML Services Cheat Sheet – AIF-C01 & AIP-C01

~ Last updated on : ~ Kiro Agent

AWS AI & Generative AI Services – Cheat Sheet

This is the definitive cheat sheet covering AI, Machine Learning, and Generative AI services on AWS — designed as the anchor page for both the AWS Certified AI Practitioner (AIF-C01) and AWS Certified Generative AI Developer – Professional (AIP-C01) exams.

Related Posts:

AI/ML/Generative AI Fundamentals

AI vs ML vs Deep Learning vs Generative AI

Concept Definition Examples
Artificial Intelligence (AI) Broad field of computer science focused on creating systems that can perform tasks requiring human intelligence Rule-based systems, expert systems, robotics
Machine Learning (ML) Subset of AI where systems learn from data without being explicitly programmed Fraud detection, recommendations, forecasting
Deep Learning (DL) Subset of ML using neural networks with multiple layers (deep neural networks) to learn complex patterns Image recognition, NLP, speech recognition
Generative AI (GenAI) Subset of DL that creates new content (text, images, code, video, audio) by learning patterns from training data ChatGPT, DALL-E, Amazon Nova, Claude

Learning Paradigms

  • Supervised Learning — model learns from labeled data (input-output pairs). Used for classification (spam/not spam) and regression (price prediction).
  • Unsupervised Learning — model finds patterns in unlabeled data. Used for clustering (customer segmentation), anomaly detection, and dimensionality reduction.
  • Semi-supervised Learning — combines small amount of labeled data with large amounts of unlabeled data.
  • Reinforcement Learning (RL) — agent learns by interacting with an environment, receiving rewards/penalties. Used for game playing, robotics, and RLHF in LLMs.
  • Self-supervised Learning — model generates its own labels from input data (e.g., predicting masked tokens). Used for pre-training foundation models.

Neural Networks Basics

  • Neurons/Nodes — basic computation units that receive inputs, apply weights, add bias, and pass through an activation function.
  • Layers — Input layer (receives data), Hidden layers (process data), Output layer (produces result).
  • Weights & Biases — parameters learned during training that determine the model’s behavior.
  • Activation Functions — introduce non-linearity (ReLU, Sigmoid, Softmax, Tanh).
  • Backpropagation — algorithm to compute gradients and update weights by propagating errors backward.
  • Loss Function — measures how far the model’s predictions are from actual values.
  • Transformer Architecture — foundation of modern LLMs; uses self-attention mechanism to process entire sequences in parallel (introduced in “Attention is All You Need” paper, 2017).
  • CNNs (Convolutional Neural Networks) — specialized for image/spatial data.
  • RNNs/LSTMs — sequential data processing (largely superseded by Transformers for NLP).
  • GANs (Generative Adversarial Networks) — generator + discriminator for image generation.
  • Diffusion Models — generate images/video by learning to denoise (e.g., Stable Diffusion, Nova Canvas).

📖 Deep Dive Guides: Bedrock vs SageMaker | RAG Architecture | Prompt Engineering | Responsible AI | AI Services Decision Guide

Foundation Model Concepts

Pre-training

  • Training a model on massive datasets (trillions of tokens) to learn general language/world knowledge.
  • Extremely expensive and resource-intensive (millions of GPU hours).
  • Results in a base model with broad capabilities but no specific task alignment.
  • Common objectives: next-token prediction (GPT-style), masked language modeling (BERT-style).

Fine-tuning Techniques

  • Instruction Tuning — fine-tuning on instruction-response pairs to make the model follow instructions better.
  • RLHF (Reinforcement Learning from Human Feedback) — trains a reward model from human preferences, then uses RL (PPO) to optimize the language model against that reward. Used to align models with human values.
  • DPO (Direct Preference Optimization) — simpler alternative to RLHF that directly optimizes on preference pairs without a separate reward model. More stable training.
  • LoRA / QLoRA — Parameter-Efficient Fine-Tuning (PEFT) that freezes base model and trains small adapter layers. Reduces compute by 90%+.
  • Continued Pre-training — further pre-training on domain-specific data to teach the model new knowledge (e.g., medical, legal, financial).
  • Distillation — training a smaller “student” model to mimic a larger “teacher” model’s outputs. Reduces inference cost while retaining most capability.

RAG (Retrieval Augmented Generation)

  • Combines information retrieval with text generation to ground LLM responses in external knowledge.
  • How it works: Query → Retrieve relevant documents from knowledge base → Augment prompt with retrieved context → Generate response.
  • Benefits: Reduces hallucinations, enables up-to-date responses, no model retraining needed, source attribution.
  • Components: Document ingestion, chunking strategy, embedding model, vector database, retrieval algorithm, re-ranking.
  • AWS Implementation: Amazon Bedrock Knowledge Bases, Amazon Kendra (GenAI Index), OpenSearch vector search.

Prompt Engineering

  • Zero-shot — asking the model to perform a task without any examples. Relies on pre-trained knowledge.
  • Few-shot (In-Context Learning) — providing a few examples in the prompt to guide the model’s output format and behavior.
  • Chain-of-Thought (CoT) — asking the model to “think step by step” to improve reasoning on complex tasks.
  • System Prompts — instructions that define the model’s role, behavior, and constraints.
  • Prompt Templates — reusable prompt structures with placeholders for dynamic content.
  • Prompt Chaining — breaking complex tasks into sequential prompts where output of one feeds input of next.

Key Parameters & Concepts

  • Tokenization — splitting text into tokens (subwords/words). Models process tokens, not characters. Affects context limits and pricing.
  • Embeddings — dense vector representations of text/images in high-dimensional space. Semantically similar items have similar embeddings. Used for search, RAG, and clustering.
  • Temperature — controls randomness of output. Low (0-0.3) = deterministic/focused, High (0.7-1.0) = creative/diverse. 0 = greedy decoding.
  • Top-p (Nucleus Sampling) — considers only tokens whose cumulative probability exceeds p. Top-p 0.9 = considers top 90% probability mass.
  • Top-k — limits token selection to the k most likely next tokens.
  • Context Window — maximum number of tokens (input + output) the model can process at once. Ranges from 4K to 1M+ tokens in modern models.
  • Max Tokens — limits the length of generated output.
  • Stop Sequences — tokens that signal the model to stop generating.
  • Hallucination — when a model generates plausible-sounding but factually incorrect information.
  • Grounding — techniques to anchor model responses in factual data (RAG, tool use, citations).

Responsible AI

Core Principles

  • Fairness & Bias — ensuring models don’t discriminate based on protected attributes (race, gender, age). Types: selection bias, measurement bias, representation bias, confirmation bias.
  • Explainability — ability to understand and explain how/why a model made a specific prediction. Techniques: SHAP, LIME, attention visualization, feature importance.
  • Transparency — openly communicating model capabilities, limitations, and intended use cases to users.
  • Robustness — model performs reliably across different inputs, including adversarial examples and edge cases.
  • Privacy & Security — protecting training data, user inputs, and model outputs. Preventing data leakage and prompt injection.
  • Governance — organizational policies, processes, and controls for responsible AI development and deployment.
  • Safety — preventing harmful outputs including toxic content, misinformation, and dangerous instructions.

AWS Responsible AI Tools

  • AWS AI Service Cards — transparency documentation for AWS AI services covering intended use cases, limitations, responsible AI design choices, and deployment best practices.
  • Amazon Bedrock Guardrails — configurable safeguards for GenAI applications:
    • Content filters (hate, insults, sexual, violence, misconduct)
    • Denied topics (topic avoidance policies)
    • Word/phrase filters
    • Sensitive information filters (PII redaction)
    • Contextual grounding checks (hallucination detection)
    • Automated Reasoning Checks (logical verification)
  • SageMaker Clarify — detects bias in data and models, provides feature attributions for explainability (note: moving to maintenance July 2026 for new customers).
  • Model Cards — documentation that describes a model’s intended use, performance metrics, limitations, and ethical considerations. Supported in SageMaker Model Registry.
  • Human-in-the-Loop (HITL) — keeping humans involved in AI decision-making for high-stakes scenarios. AWS A2I (Augmented AI) provided review workflows (note: moving to maintenance July 2026 for new customers).
  • Amazon Bedrock Model Evaluation — automatic evaluation (accuracy, robustness, toxicity), human evaluation, and LLM-as-a-judge for quality assessment.

Bias Mitigation Strategies

  • Pre-processing: Balance training data, remove sensitive attributes, data augmentation.
  • In-processing: Regularization techniques, adversarial debiasing, fairness constraints during training.
  • Post-processing: Calibrate outputs, threshold adjustment, reject option classification.
  • Monitoring: Continuously track model performance across demographic groups in production.

Agentic AI

What are AI Agents?

  • AI systems that can autonomously plan, reason, and execute multi-step tasks to achieve goals.
  • Go beyond simple prompt-response by taking actions, using tools, and adapting based on outcomes.
  • Can operate for extended periods, making decisions and course-correcting without human intervention.

Key Concepts

  • Tool Use (Function Calling) — agents invoke external tools (APIs, databases, code execution) to gather information or perform actions.
  • Multi-step Reasoning — breaking complex problems into steps, executing sequentially with intermediate evaluations.
  • Orchestration — coordinating multiple agents or components to complete complex workflows. Patterns: sequential, parallel, routing, supervisor.
  • Memory — maintaining context across interactions:
    • Short-term memory (conversation context within a session)
    • Long-term memory (persistent knowledge across sessions)
    • Episodic memory (past experiences and outcomes)
  • Planning — decomposing goals into actionable sub-tasks, determining execution order, handling dependencies.
  • Reflection — agents evaluate their own outputs and self-correct errors before responding.
  • Model Context Protocol (MCP) — open standard for connecting AI agents with external tools and data sources.
  • Agent2Agent (A2A) — protocol for inter-agent communication and collaboration.

AWS Agentic AI Services

  • Amazon Bedrock Agents — create agents that can break down tasks, call APIs, and access knowledge bases (transitioning to Bedrock Agents Classic, July 2026).
  • Amazon Bedrock AgentCore (GA 2025/2026) — enterprise-grade infrastructure for deploying and operating AI agents at scale:
    • AgentCore Runtime — serverless, scalable environment to host agents
    • AgentCore Gateway — MCP-compatible tool connectivity
    • AgentCore Identity — per-agent identity and least-privilege access
    • AgentCore Observability — monitoring, tracing, and debugging
    • AgentCore Code Interpreter — secure sandboxed code execution
    • AgentCore Optimization — continuous quality evaluation and improvement
  • Amazon Nova Act — browser automation agent for web-based tasks.
  • AWS Step Functions — orchestrate multi-step agent workflows with state management.

AWS AI Service Stack

AWS organizes AI/ML services into three layers:

Layer 1: AI Infrastructure (Compute & Silicon)

Service/Chip Purpose Key Details
AWS Trainium Custom chip for ML training Trainium2 (4x perf vs gen1), Trainium3 (3nm, 4.4x vs Trn2, GA Dec 2025)
AWS Inferentia Custom chip for ML inference Inferentia2 (4x throughput, 10x lower latency vs gen1), Inf2 instances
EC2 UltraServers Multi-instance AI clusters Trn2 UltraServers (64 Trainium2 chips, NeuronLink interconnect), Trn3 UltraServers
AWS AI Factories On-premises AI infrastructure Deploy AI training/inference infrastructure in customer data centers
AWS Neuron SDK Software for Trainium/Inferentia Integrates with PyTorch, JAX, TensorFlow. Compiler, runtime, profiler
EC2 P5/P5e/P5en GPU instances (NVIDIA) H100/H200 GPUs for training and inference
EC2 G6/G6e GPU instances (NVIDIA) L4/L40S GPUs for inference and graphics
AWS Graviton Arm-based general compute Best price-performance for inference serving and general ML workloads
Amazon EFA Elastic Fabric Adapter Low-latency networking for distributed training across instances

Layer 2: ML Platform (SageMaker AI)

  • Amazon SageMaker AI (rebranded from SageMaker, late 2024) — end-to-end ML platform for building, training, and deploying models.
Component Purpose
SageMaker Unified Studio Single IDE for data, analytics, and ML/AI development (integrates Bedrock)
SageMaker Canvas No-code ML for business analysts — point-and-click model building
SageMaker HyperPod Managed clusters for large-scale distributed training with auto-recovery
SageMaker Pipelines CI/CD for ML — define, automate, and manage ML workflows
SageMaker Feature Store Centralized repository for ML features (online + offline store)
SageMaker MLflow Managed MLflow for experiment tracking, model versioning, deployment
SageMaker Model Registry Central catalog to version, manage, and deploy models with approval workflows
SageMaker JumpStart Model hub with 400+ pre-trained models, one-click deploy, fine-tuning
SageMaker Endpoints Real-time inference hosting (single model or multi-model endpoints)
SageMaker Training Managed training with built-in algorithms, distributed training, spot instances
SageMaker Processing Run data processing and evaluation jobs at scale
SageMaker Lakehouse Unified access to data lakes and warehouses for ML

Layer 3: AI Applications & Services

Amazon Bedrock (Generative AI Platform)

  • Amazon Bedrock — fully managed service for building GenAI applications with foundation models.
  • Model Providers: Amazon (Nova), Anthropic (Claude), Meta (Llama), Mistral, Cohere, AI21 Labs, OpenAI, Stability AI.
  • Key Capabilities:
    • Model Inference — Converse API, InvokeModel, streaming, batch inference, cross-region inference
    • Knowledge Bases — managed RAG with vector stores (OpenSearch, Aurora, Pinecone, etc.)
    • Managed Knowledge Base (2026) — fully managed RAG primitive (storage + retrieval + embeddings + re-ranking)
    • Agents — multi-step task execution with tool use (transitioning to AgentCore)
    • Guardrails — content filtering, topic avoidance, PII protection, grounding checks
    • Model Customization — fine-tuning, continued pre-training, distillation
    • Model Evaluation — automatic metrics, human evaluation, LLM-as-judge
    • Flows — visual workflow builder for chaining prompts, agents, and knowledge bases

Amazon Nova Models

  • Nova Micro — text-only, fastest, lowest cost (128K context). Ideal for classification, summarization.
  • Nova Lite — multimodal (text + image + video input), cost-effective (300K context).
  • Nova Pro — balanced multimodal, strong accuracy/speed/cost trade-off (300K context).
  • Nova Premier — most capable, complex reasoning, agentic workflows, teacher model (1M context).
  • Nova Canvas — image generation with editing controls and watermarking.
  • Nova Reel — video generation (1280×720, 24fps, up to 6 seconds).
  • Nova Sonic — speech-to-speech for real-time conversational AI.
  • Nova 2 (Dec 2025) — next generation with extended thinking (adjustable levels), 1M token context, built-in tools:
    • Nova 2 Lite — fast, cost-effective reasoning model
    • Nova 2 Pro — most intelligent, complex agentic tasks
    • Nova 2 Sonic — next-gen speech with async tool calling
    • Nova 2 Omni — unified multimodal I/O (text + image generation)
  • Nova Act — browser automation agent for web tasks.
  • Nova Forge — custom model building program (open training).

Amazon Q Developer & Q Business

  • Amazon Q Developer — AI-powered coding assistant (evolved from CodeWhisperer):
    • Code generation, completion, and inline suggestions (15+ languages)
    • Agentic coding (autonomous multi-step development)
    • Security vulnerability scanning
    • Code transformation and modernization (Java, .NET upgrades)
    • CLI integration (natural language → commands)
    • Debugging and troubleshooting with CloudWatch integration
  • Amazon Q Business — AI assistant for enterprise knowledge (connects to 40+ data sources):
    • Natural language answers from company data
    • Document summarization and content creation
    • Task automation with plugins
    • Access control respecting existing permissions (ACL-aware)
  • Amazon Q in Console — chat assistant in AWS Management Console for troubleshooting and guidance.
⚠️ Note (July 2026): Amazon Q Developer IDE plugins reaching end-of-support April 2027. Successor is Kiro — AWS’s agentic development environment. Amazon Q Business and Amazon Kendra entering maintenance mode for new customers July 30, 2026.

AWS AI/ML Application Services

Service Category Purpose
Amazon Comprehend NLP Sentiment analysis, entity recognition, key phrase extraction, language detection, topic modeling
Amazon Rekognition Computer Vision Object/face detection, content moderation, celebrity recognition, text in images, custom labels
Amazon Polly Speech Text-to-speech with neural voices (60+ voices, 30+ languages), SSML support
Amazon Transcribe Speech Speech-to-text (ASR), real-time and batch, custom vocabularies, speaker identification
Amazon Translate Language Neural machine translation (75+ languages), real-time and batch, custom terminology
Amazon Textract Document AI OCR + intelligent document processing, extracts text, tables, forms, and queries from documents
Amazon Lex Conversational AI Build chatbots and voice bots with automatic speech recognition and NLU
Amazon Kendra Search Enterprise search with NLP, semantic understanding, GenAI index for RAG ⚠️ Maintenance mode July 2026
Amazon Personalize Recommendations Real-time personalization and recommendations (same tech as Amazon.com)
Amazon Forecast Time Series Time series forecasting using ML (closed to new customers since 2024)
Amazon HealthScribe Healthcare Generate clinical documentation from patient-clinician conversations
Amazon Bedrock AgentCore Agentic AI Deploy, manage, and optimize AI agents at scale (GA 2025/2026)

Decision Matrix: Use Case → Recommended Service

Use Case Recommended Service Why
Build GenAI apps with FMs (no ML expertise) Amazon Bedrock Serverless, multi-model, fully managed
Custom model training from scratch SageMaker AI + Trainium Full control over training, data, and infrastructure
Enterprise Q&A over company documents Amazon Q Business / Bedrock Knowledge Bases Connects to 40+ data sources, ACL-aware
AI coding assistant Amazon Q Developer / Kiro Inline completions, security scanning, agentic coding
Build and deploy AI agents Bedrock AgentCore Serverless runtime, MCP tools, identity, observability
Chatbot / virtual assistant Amazon Lex + Bedrock Lex for structure, Bedrock for natural responses
Document processing (forms, invoices) Amazon Textract Extracts structured data from documents at scale
Content moderation (images/video) Amazon Rekognition Pre-built moderation labels, custom labels for specifics
Sentiment analysis on customer feedback Amazon Comprehend Pre-built NLP models, no training needed
Real-time product recommendations Amazon Personalize Same ML tech as Amazon.com, real-time updates
Transcribe meetings/calls Amazon Transcribe Real-time ASR, speaker diarization, custom vocab
Generate speech from text Amazon Polly Neural TTS, SSML support, multiple voices
Translate content at scale Amazon Translate 75+ languages, real-time, custom terminology
No-code ML for business users SageMaker Canvas Point-and-click, AutoML, visual interface
Fine-tune FMs on proprietary data Bedrock Custom Models / SageMaker JumpStart Bedrock for serverless; SageMaker for full control
Prevent harmful GenAI outputs Amazon Bedrock Guardrails Content filters, PII, grounding checks, topic avoidance
Cost-effective GenAI inference at scale Bedrock + Nova models (or Inferentia2/Trainium) Nova = lowest cost in class; custom silicon for self-hosted
Clinical documentation from conversations Amazon HealthScribe Purpose-built for healthcare, HIPAA eligible

Quick Reference: All AWS AI/ML Services

Service One-Liner
Amazon Bedrock Fully managed GenAI platform with multi-provider foundation models
Amazon Bedrock AgentCore Enterprise infrastructure for deploying and operating AI agents at scale
Amazon Nova Amazon’s family of foundation models (text, multimodal, speech, image, video)
Amazon Q Developer AI coding assistant with code generation, security scanning, and transformation
Amazon Q Business Enterprise AI assistant for Q&A and task automation over company data
Amazon SageMaker AI End-to-end ML platform for building, training, and deploying custom models
Amazon Comprehend NLP service for sentiment, entities, key phrases, language detection
Amazon Rekognition Computer vision for object/face detection, moderation, and custom labels
Amazon Polly Text-to-speech with neural and standard voices
Amazon Transcribe Automatic speech recognition (speech-to-text)
Amazon Translate Neural machine translation for 75+ languages
Amazon Textract Extract text, tables, and forms from documents (OCR+)
Amazon Lex Build conversational chatbots and voice bots
Amazon Kendra Intelligent enterprise search with NLP and GenAI index
Amazon Personalize Real-time ML-powered personalization and recommendations
Amazon HealthScribe Generate clinical notes from patient-clinician conversations
AWS Trainium Custom AI chip optimized for training (Trn2, Trn3 instances)
AWS Inferentia Custom AI chip optimized for inference (Inf2 instances)
AWS Neuron SDK SDK for running ML workloads on Trainium and Inferentia chips
Amazon SageMaker Canvas No-code ML model building for business analysts
Amazon SageMaker HyperPod Managed clusters for distributed training with auto fault recovery

Exam Tips

AIF-C01 — AWS Certified AI Practitioner

  • Format: 65 questions, 90 minutes, 700/1000 passing score.
  • Domains:
    • Domain 1: Fundamentals of AI and ML (20%)
    • Domain 2: Fundamentals of Generative AI (24%)
    • Domain 3: Applications of Foundation Models (28%) — largest domain, most technical
    • Domain 4: Guidelines for Responsible AI (14%)
    • Domain 5: Security, Compliance, and Governance for AI Solutions (14%)
  • Key Focus Areas:
    • Domains 2+3 = 52% of exam — master Bedrock, RAG, prompt engineering, fine-tuning
    • Know the difference between AI vs ML vs DL vs GenAI
    • Understand when to use Bedrock vs SageMaker
    • RAG architecture and when to use it vs fine-tuning
    • Responsible AI principles and Bedrock Guardrails
    • Temperature, top-p effects on output
    • Know all AWS AI services at a high level (what each does)

AIP-C01 — AWS Certified Generative AI Developer – Professional

  • Format: 85 questions, 180 minutes, 750/1000 passing score.
  • Domains:
    • Domain 1: FM Selection and Integration (26%)
    • Domain 2: Data Management and Optimization (22%)
    • Domain 3: Model Performance and Compliance (31%) — largest domain
    • Domain 4: Security and Governance (21%)
  • Key Focus Areas:
    • Deep hands-on knowledge of Bedrock APIs, agents, knowledge bases, guardrails
    • RAG implementation details (chunking strategies, embedding models, vector stores)
    • Model customization (when fine-tuning vs RAG vs prompt engineering)
    • Agentic AI patterns (tool use, multi-step, AgentCore)
    • SageMaker for custom training and deployment
    • Model evaluation and monitoring in production
    • Security: data encryption, VPC endpoints, IAM for Bedrock, prompt injection mitigation
    • Cost optimization (model selection, batch inference, provisioned throughput)

Common Exam Scenarios

  • “Least operational overhead” → Bedrock (serverless) over SageMaker (managed infrastructure)
  • “Custom model with proprietary data” → Fine-tuning on Bedrock or SageMaker depending on control needed
  • “Reduce hallucinations” → RAG with Knowledge Bases + Guardrails grounding checks
  • “Enterprise search over internal docs” → Amazon Q Business or Bedrock Knowledge Bases
  • “Control AI outputs for safety” → Bedrock Guardrails
  • “Lowest cost inference” → Nova Micro (text) or Nova Lite (multimodal) on Bedrock
  • “Deploy agents in production” → Bedrock AgentCore (serverless, scalable, observable)
  • “Train trillion-parameter model” → Trainium3 UltraServers + SageMaker HyperPod

Practice Questions

Question 1 (AIF-C01)

A company wants to reduce hallucinations in their generative AI application that answers customer questions about company policies. The application uses Amazon Bedrock. What is the MOST effective approach?

  1. Increase the model temperature to generate more diverse responses
  2. Implement Retrieval Augmented Generation (RAG) with Amazon Bedrock Knowledge Bases
  3. Fine-tune the foundation model on company documents
  4. Switch to a larger foundation model
Show Answer

Answer: B – RAG grounds responses in actual company documents, directly reducing hallucinations. Fine-tuning (C) teaches style/format but doesn’t guarantee factual accuracy for specific documents. Higher temperature (A) increases randomness. Larger models (D) don’t inherently reduce hallucinations.

Question 2 (AIF-C01)

Which combination of techniques helps ensure responsible AI in a generative AI application? (Select TWO)

  1. Increase the context window size
  2. Configure Amazon Bedrock Guardrails with content filters and denied topics
  3. Use the lowest-cost foundation model available
  4. Implement human review workflows for high-stakes decisions
  5. Maximize the temperature parameter for creative outputs
Show Answer

Answer: B, D – Bedrock Guardrails (B) provides configurable safety controls to filter harmful content. Human-in-the-loop (D) ensures human oversight for critical decisions. Context window size (A), model cost (C), and temperature (E) are not responsible AI techniques.

Question 3 (AIP-C01)

A developer is building an AI agent that needs to autonomously execute multi-step workflows, call external APIs, and maintain state across interactions. The solution must be production-grade with monitoring and minimal infrastructure management. Which AWS service should they use?

  1. Amazon Lex with Lambda fulfillment functions
  2. Amazon Bedrock AgentCore with AgentCore Runtime and Observability
  3. AWS Step Functions with SageMaker endpoints
  4. Amazon Q Business with custom plugins
Show Answer

Answer: B – Bedrock AgentCore provides serverless runtime for agents, MCP-compatible tool connectivity (Gateway), built-in observability, and identity management — purpose-built for production AI agents. Lex (A) is for chatbots, not autonomous agents. Step Functions (C) requires more infrastructure management. Q Business (D) is for enterprise knowledge, not custom agent workflows.

Question 4 (AIP-C01)

A team needs to fine-tune a foundation model on their proprietary dataset with minimal compute cost. The dataset contains 10,000 instruction-response pairs. Which approach provides the BEST balance of performance improvement and cost?

  1. Full fine-tuning of the entire model on Amazon SageMaker with P5 GPU instances
  2. Continued pre-training on Amazon Bedrock with the full dataset
  3. Parameter-efficient fine-tuning (LoRA) through Amazon Bedrock custom models
  4. Distilling the model into a smaller variant using Nova Premier as teacher
Show Answer

Answer: C – LoRA fine-tuning on Bedrock trains only small adapter layers (reduces compute by 90%+) while the base model stays frozen. It’s ideal for instruction-tuning with limited data. Full fine-tuning (A) is expensive. Continued pre-training (B) is for teaching new knowledge, not task alignment. Distillation (D) creates a smaller model but doesn’t directly fine-tune on task data.

Question 5 (AIF-C01 / AIP-C01)

A company wants to deploy a generative AI solution with the following requirements: lowest possible latency for text summarization, minimal cost, and no infrastructure management. Which combination should they choose?

  1. Amazon Nova Premier on Bedrock with provisioned throughput
  2. Amazon Nova Micro on Bedrock with on-demand pricing
  3. Claude 3 Opus on Bedrock with batch inference
  4. Self-hosted Llama model on SageMaker with Inferentia2 instances
Show Answer

Answer: B – Nova Micro is the fastest text-only model (200+ tokens/sec), lowest cost, and Bedrock provides serverless (no infrastructure). Premier (A) is more capable but slower and costlier. Batch (C) has high latency. Self-hosted (D) requires infrastructure management.

Frequently Asked Questions

What is the difference between AI, ML, and Generative AI?

AI is the broadest category — machines performing tasks that typically require human intelligence. ML is a subset that learns from data without explicit programming. Generative AI is a subset of ML that creates new content (text, images, code) using foundation models trained on vast datasets.

What is the difference between Amazon Bedrock and SageMaker?

Bedrock provides access to pre-built foundation models for generative AI applications without ML expertise. SageMaker is a full ML platform for building, training, and deploying custom models from scratch. Use Bedrock for gen AI apps; SageMaker when you need complete control over model training.

What AWS certifications cover AI and Generative AI?

AWS offers two AI-focused certifications: AIF-C01 (AI Practitioner) for foundational knowledge of AI/ML/Gen AI concepts and AWS services, and AIP-C01 (AI Professional) for practitioners building and deploying Gen AI solutions. Both require knowledge of Bedrock, SageMaker, and responsible AI.

Detailed Guides

Exam Prep: AWS AI Professional (AIP-C01) Exam Learning Path

References

AWS SageMaker Built-in Algorithms Summary

SageMaker Built-in Algorothms
~ Last updated on : ~ jayendrapatil

SageMaker AI Built-in Algorithms

📌 Naming Update (December 2024): On December 3, 2024, Amazon SageMaker was renamed to Amazon SageMaker AI. The “SageMaker” brand now refers to the next-generation unified platform for data, analytics, and AI. All built-in algorithms remain available under SageMaker AI.

  • SageMaker AI provides a suite of built-in algorithms, pre-trained models, and pre-built solution templates to help data scientists and ML practitioners get started on training and deploying ML models quickly.
  • SageMaker AI also provides SageMaker JumpStart with pre-trained foundation models (including LLMs like LLaMA, BLOOM, Falcon) for generative AI tasks such as text generation, summarization, and question answering.

SageMaker AI Built-in Algorithms

Tabular Data – Classification & Regression

AutoGluon-Tabular

  • is an open-source AutoML framework that succeeds by ensembling models and stacking them in multiple layers.
  • automatically performs data processing, model selection, and hyperparameter tuning.
  • used for both classification and regression tasks on tabular data.
  • supports CPU and GPU (single instance only) training.

CatBoost

  • is an implementation of the gradient-boosted trees algorithm that introduces ordered boosting and an innovative algorithm for processing categorical features.
  • used for both classification and regression tasks.
  • handles categorical features natively without requiring manual encoding.
  • supports CPU (single instance only) training.

LightGBM

  • is an implementation of the gradient-boosted trees algorithm that adds two novel techniques for improved efficiency and scalability.
  • uses Gradient-based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB).
  • used for both classification and regression tasks.
  • supports CPU (single instance only) training.

TabTransformer

  • is a novel deep tabular data modeling architecture built on self-attention-based Transformers.
  • converts categorical features into contextual embeddings using Transformer layers.
  • used for both classification and regression tasks.
  • supports CPU and GPU (single instance only) training.

XGBoost (eXtreme Gradient Boosting)

  • is a popular and efficient open-source implementation of the gradient boosted trees algorithm.
  • Gradient boosting is a supervised learning algorithm that attempts to accurately predict a target variable by combining an ensemble of estimates from a set of simpler, weaker models.
  • supports both classification and regression tasks.
  • supports distributed training across multiple instances.

Linear Learner

  • are supervised learning algorithms used for solving either classification or regression problems.
  • learns a linear function for regression or a linear threshold function for classification.
  • supports distributed training.

K-nearest neighbors (k-NN) algorithm

  • is an index-based algorithm.
  • uses a non-parametric method for classification or regression.
  • For classification problems, the algorithm queries the k points that are closest to the sample point and returns the most frequently used label of their class as the predicted label.
  • For regression problems, the algorithm queries the k closest points to the sample point and returns the average of their feature values as the predicted value.

Factorization Machine

  • is a general-purpose supervised learning algorithm used for both classification and regression tasks.
  • extension of a linear model designed to capture interactions between features within high dimensional sparse datasets economically, such as click prediction and item recommendation.

Text-based

BlazingText algorithm

  • provides highly optimized implementations of the Word2vec and text classification algorithms.
  • Word2vec algorithm
    • useful for many downstream natural language processing (NLP) tasks, such as sentiment analysis, named entity recognition, machine translation, etc.
    • maps words to high-quality distributed vectors, whose representation is called word embeddings
    • word embeddings capture the semantic relationships between words.
  • Text classification
    • is an important task for applications performing web searches, information retrieval, ranking, and document classification
  • provides the Skip-gram and continuous bag-of-words (CBOW) training architectures

Text Classification – TensorFlow

  • is a supervised learning algorithm that supports transfer learning with many pretrained models from the TensorFlow Hub.
  • uses deep learning networks such as BERT which are highly accurate for text classification.
  • takes text as input and outputs probability for each of the class labels.
  • useful for sentiment analysis, spam detection, and document categorization.

Sequence to Sequence – seq2seq

  • is a supervised learning algorithm where the input is a sequence of tokens (for example, text, audio), and the output generated is another sequence of tokens.
  • key uses cases are machine translation (input a sentence from one language and predict what that sentence would be in another language), text summarization (input a longer string of words and predict a shorter string of words that is a summary), speech-to-text (audio clips converted into output sentences in tokens)

Forecasting

DeepAR

  • is a supervised learning algorithm for forecasting scalar (one-dimensional) time series using recurrent neural networks (RNN).
  • use the trained model to generate forecasts for new time series that are similar to the ones it has been trained on.
  • supports learning complex patterns from multiple related time series simultaneously.

Clustering

K-means algorithm

  • is an unsupervised learning algorithm for clustering
  • attempts to find discrete groupings within data, where members of a group are as similar as possible to one another and as different as possible from members of other groups

Topic Modelling

Latent Dirichlet Allocation (LDA)

  • is an unsupervised learning algorithm that attempts to describe a set of observations as a mixture of distinct categories.
  • used to discover a user-specified number of topics shared by documents within a text corpus.

Neural Topic Model (NTM)

  • is an unsupervised learning algorithm that is used to organize a corpus of documents into topics that contain word groupings based on their statistical distribution
  • Topic modeling can be used to classify or summarize documents based on the topics detected or to retrieve information or recommend content based on topic similarities.

Feature Reduction

Object2Vec

  • is a general-purpose neural embedding algorithm that is highly customizable
  • can learn low-dimensional dense embeddings of high-dimensional objects.
  • useful for duplicate detection, finding similar items, and relationship prediction.

Principal Component Analysis – PCA

  • is an unsupervised ML algorithm that attempts to reduce the dimensionality (number of features) within a dataset while still retaining as much information as possible.
  • projects data points onto the first few principal components (eigenvectors of the data’s covariance matrix).

Anomaly Detection

Random Cut Forest (RCF)

  • is an unsupervised algorithm for detecting anomalous data points within a data set.
  • detects data points that diverge from otherwise well-structured or patterned data.

IP Insights

  • is an unsupervised learning algorithm that learns the usage patterns for IPv4 addresses.
  • designed to capture associations between IPv4 addresses and various entities, such as user IDs or account numbers
  • useful for detecting suspicious login attempts from anomalous IP addresses.

Computer Vision – CV

Image Classification – MXNet

  • a supervised learning algorithm that supports multi-label classification
  • takes an image as input and outputs one or more labels
  • uses a convolutional neural network (ResNet) that can be trained from scratch or trained using transfer learning when a large number of training images are not available.
  • recommended input format is Apache MXNet RecordIO. Also supports raw images in .jpg or .png format.

Image Classification – TensorFlow

  • is a supervised learning algorithm that supports transfer learning with many pretrained models from the TensorFlow Hub.
  • uses deep learning networks such as MobileNet, ResNet, Inception, and EfficientNet for image classification.
  • takes an image as input and outputs probability for each of the class labels.
  • supports fine-tuning pretrained models for specific image classification tasks.

Object Detection – MXNet

  • detects and classifies objects in images using a single deep neural network.
  • is a supervised learning algorithm that takes images as input and identifies all instances of objects within the image scene.

Object Detection – TensorFlow

  • is a supervised learning algorithm that supports transfer learning with many pretrained models from the TensorFlow Model Garden.
  • takes an image as input and predicts bounding boxes and object labels.
  • uses deep learning networks such as MobileNet, ResNet, Inception, and EfficientNet for object detection.

Semantic Segmentation

  • provides a fine-grained, pixel-level approach to developing computer vision applications.
  • tags every pixel in an image with a class label from a predefined set of classes and is critical to an increasing number of CV applications, such as self-driving vehicles, medical imaging diagnostics, and robot sensing.
  • also provides information about the shapes of the objects contained in the image. The segmentation output is represented as a grayscale image, called a segmentation mask.

SageMaker JumpStart – Pre-trained Models

  • SageMaker JumpStart provides pre-trained foundation models, pre-built solution templates, and example notebooks for popular ML problem types.
  • Foundation models include large language models (LLMs) such as LLaMA, Falcon, BLOOM, FLAN-T5, Mistral, and GPT-J for generative AI tasks.
  • Supports 15+ problem types including:
    • Text Generation, Text Summarization, Question Answering
    • Text Embedding, Named Entity Recognition
    • Image Classification, Object Detection, Instance Segmentation
    • Tabular Classification, Tabular Regression
    • Machine Translation, Sentence Pair Classification
  • Models can be fine-tuned on custom datasets and deployed directly from SageMaker Studio.

SageMaker Autopilot (AutoML)

  • SageMaker Autopilot automatically explores different solutions to find the best model for your data.
  • Analyzes data, selects algorithms, preprocesses data, trains models, and performs hyperparameter optimization.
  • Supports classification, regression, and time-series forecasting problem types.
  • Available as a no-code/low-code option through SageMaker Canvas for business analysts.

AWS Certification Exam Practice Questions

  • Questions are collected from Internet and the answers are marked as per my knowledge and understanding (which might differ with yours).
  • AWS services are updated everyday and both the answers and questions might be outdated soon, so research accordingly.
  • AWS exam questions are not updated to keep up the pace with AWS updates, so even if the underlying feature has changed the question might not be updated
  • Open to further feedback, discussion and correction.
  1. An Analytics team is leading an organization and wants to use anomaly detection to identify potential risks. What Amazon SageMaker AI machine learning algorithms are best suited for identifying anomalies?
    1. Semantic segmentation
    2. K-nearest neighbors
    3. Latent Dirichlet Allocation (LDA)
    4. Random Cut Forest (RCF)
  2. A ML specialist team works for a marketing consulting firm wants to
    apply different marketing strategies per segment of their customer base. Online retailer purchase history from the last 5 years is available, it has been decided to segment the customers based on their purchase history. Which type of machine learning algorithm would give you segmentation based on purchase history in the most expeditious manner?

    1. K-Nearest Neighbors (KNN)
    2. K-Means
    3. Semantic Segmentation
    4. Neural Topic Model (NTM)
  3. A ML specialist team is looking to improve the quality of searches for their library of documents that are uploaded in PDF, Rich Text Format, or ASCII text. It is looking to use machine learning to automate the identification of key topics for each of the documents. What machine learning resources are best suited for this problem? (Select TWO)
    1. BlazingText algorithm
    2. Latent Dirichlet Allocation (LDA) algorithm
    3. Topic Finder (TF) algorithm
    4. Neural Topic Model (NTM) algorithm
  4. A manufacturing company has a large set of labeled historical sales data. The company would like to predict how many units of a particular part should be produced each quarter. Which machine learning approach should be used to solve this problem?
    1. BlazingText algorithm
    2. Random Cut Forest (RCF)
    3. Principal component analysis (PCA)
    4. Linear regression
  5. An agency collects census information with responses for approximately 500 questions from each citizen. Which algorithm would help reduce the number of features?
    1. Factorization machines (FM) algorithm
    2. Latent Dirichlet Allocation (LDA) algorithm
    3. Principal component analysis (PCA) algorithm
    4. Random Cut Forest (RCF) algorithm
  6. A store wants to understand some characteristics of visitors to the store. The store has security video recordings from the past several years. The store wants to group visitors by hair style and hair color. Which solution will meet these requirements with the LEAST amount of effort?
    1. Object detection algorithm
    2. Latent Dirichlet Allocation (LDA) algorithm
    3. Random Cut Forest (RCF) algorithm
    4. Semantic segmentation algorithm
  7. A data scientist needs to build a model that can automatically classify product reviews as positive or negative. The dataset contains millions of labeled reviews. Which SageMaker AI built-in algorithm is MOST suitable for this text classification task with transfer learning?
    1. Sequence-to-Sequence (seq2seq)
    2. BlazingText in Word2Vec mode
    3. Text Classification – TensorFlow
    4. Neural Topic Model (NTM)
  8. A company wants to predict customer churn using a tabular dataset with both numerical and categorical features. The team wants an AutoML approach that automatically ensembles multiple models. Which SageMaker AI built-in algorithm should they use?
    1. XGBoost
    2. Linear Learner
    3. AutoGluon-Tabular
    4. Factorization Machines
  9. A team needs to detect objects in images and draw bounding boxes around them. They want to leverage pretrained models and use transfer learning. Which SageMaker AI algorithm should they choose?
    1. Image Classification – MXNet
    2. Semantic Segmentation
    3. Image Classification – TensorFlow
    4. Object Detection – TensorFlow
  10. A company has tabular data with many categorical features and wants a gradient-boosted trees algorithm that handles categorical features natively without manual encoding. Which algorithm is BEST suited?
    1. XGBoost
    2. LightGBM
    3. CatBoost
    4. Linear Learner

References

AWS AI & Machine Learning Services Cheat Sheet

AWS Machine Learning Services
~ Last updated on : ~ jayendrapatil ~ 1 Comment

AWS Machine Learning Services

AWS Machine Learning Services

Amazon Bedrock

  • is a fully managed service providing access to high-performing foundation models (FMs) from leading AI companies (GA September 2023).
  • offers foundation models from AI21 Labs, Amazon (Nova), Anthropic, Cohere, Meta, Mistral AI, OpenAI, and Stability AI through a unified API.
  • enables building and scaling generative AI applications without managing infrastructure.
  • supports model customization including fine-tuning and reinforcement fine-tuning (RFT) with your own data while maintaining data privacy and security.
  • provides serverless experience with pay-per-use pricing.
  • includes capabilities for text generation, chat, image generation, video generation, and embeddings.
  • supports Retrieval Augmented Generation (RAG) with Knowledge Bases and the new Managed Knowledge Base (2026) that abstracts storage, retrieval, embeddings, and re-ranking into a single managed primitive.
  • provides Bedrock Agents for multi-step task automation.
  • includes Amazon Bedrock Guardrails for configurable safety controls including content filtering, topic classification, sensitive information protection, and hallucination detection across both text and images with up to 88% harmful content blocking accuracy.
  • supports OpenAI-compatible API endpoints (2026) including Responses API and Chat Completions API for simplified migration and integration.
  • ensures data is not used to train base models and remains within your AWS environment.
  • includes Amazon Bedrock AgentCore (2026) — a platform to build, connect, deploy, and optimize AI agents with managed harness, observability, guardrails integration, and continuous optimization capabilities.

Amazon Nova Foundation Models

  • is Amazon’s family of proprietary foundation models available exclusively through Amazon Bedrock (launched December 2024 at re:Invent).
  • includes Amazon Nova Micro — a text-only model optimized for speed and lowest cost, ideal for summarization, translation, and classification (128K context).
  • includes Amazon Nova Lite — a low-cost multimodal model processing text, images, and video for tasks like document analysis and visual Q&A.
  • includes Amazon Nova Pro — a balanced multimodal model offering strong accuracy, speed, and cost for a wide range of tasks.
  • includes Amazon Nova Premier — the most capable model for complex reasoning, agentic workflows, and model distillation.
  • includes Amazon Nova Canvas — an image generation model.
  • includes Amazon Nova Reel — a video generation model.
  • includes Amazon Nova Sonic — a speech-to-speech model.
  • Amazon Nova 2 models (Nova 2 Lite and Nova 2 Pro) announced in December 2025 with improved capabilities.
  • all Nova models are among the fastest and most cost-effective in their respective intelligence classes, optimized for RAG and agentic applications.

Amazon Q Developer (formerly CodeWhisperer) → Transitioning to Kiro

  • is a generative AI-powered coding assistant for software developers (rebranded from CodeWhisperer in April 2024).
  • provides real-time code suggestions, completions, and generation based on comments and existing code.
  • supports multiple programming languages including Python, Java, JavaScript, TypeScript, C#, Go, Rust, PHP, Ruby, Kotlin, C, C++, Shell, SQL, and more.
  • integrates with popular IDEs including VS Code, IntelliJ IDEA, PyCharm, WebStorm, and AWS Cloud9.
  • performs security scanning to identify and suggest fixes for vulnerabilities.
  • provides code explanations and documentation generation.
  • assists with debugging, upgrading applications, and troubleshooting.
  • tracks open-source code references and license information.
  • offers free tier for individual developers and paid tier for professional use.
⚠️ Transition Notice (May 2026): Amazon Q Developer IDE plugins and paid subscriptions will reach end-of-support on April 30, 2027. New signups blocked as of May 15, 2026. The successor is Kiro — AWS’s next-generation agentic development environment (IDE and CLI) built on Code OSS and powered by Amazon Bedrock. Kiro includes agentic coding, inline chat, terminal integration, and MCP support. Users have a 12-month transition window.

Amazon Quick (formerly Amazon Q Business)

  • is a generative AI-powered assistant for enterprise use, rebranded from Amazon Q Business to Amazon Quick in April 2026.
  • is described as “the next evolution of Amazon Q Business” — an AI assistant for work that connects to apps, learns workflows, and takes action.
  • answers questions, provides summaries, generates content, and completes tasks based on enterprise data.
  • connects to 40+ enterprise data sources including S3, SharePoint, Salesforce, ServiceNow, Jira, and more.
  • respects existing access controls and permissions from connected data sources.
  • provides conversational interface for employees to access company information.
  • available as a desktop app (Windows and Mac) with Microsoft 365 extensions (Outlook, Word, Teams).
  • offers Free and Plus pricing plans.
  • supports autonomous agents for handling recurring tasks continuously.
  • supports Amazon Q Apps for creating AI-powered applications from conversations.
  • ensures enterprise data privacy and security with data isolation.

Amazon SageMaker AI (formerly Amazon SageMaker)

  • Naming Update (December 2024): On December 3, 2024, Amazon SageMaker was renamed to Amazon SageMaker AI. The “SageMaker” brand now refers to the next-generation unified platform for data, analytics, and AI.
  • Build, train, and deploy machine learning models at scale.
  • fully-managed service that enables data scientists and developers to quickly and easily build, train & deploy machine learning models.
  • enables developers and scientists to build machine learning models for use in intelligent, predictive apps.
  • is designed for high availability with no maintenance windows or scheduled downtimes.
  • allows users to select the number and type of instance used for the hosted notebook, training & model hosting.
  • can be deployed as endpoint interfaces and batch.
  • supports Canary deployment using ProductionVariant and deploying multiple variants of a model to the same SageMaker HTTPS endpoint.
  • supports Jupyter notebooks.
  • Users can persist their notebook files on the attached ML storage volume.
  • Users can modify the notebook instance and select a larger profile through the SageMaker console, after saving their files and data on the attached ML storage volume.
  • includes built-in algorithms for linear regression, logistic regression, k-means clustering, principal component analysis, factorization machines, neural topic modeling, latent dirichlet allocation, gradient boosted trees, seq2seq, time series forecasting, word2vec & image classification
  • algorithms work best when using the optimized protobuf recordIO format for the training data, which allows Pipe mode that streams data directly from S3 and helps faster start times and reduce space requirements
  • provides built-in algorithms, pre-built container images, or extend a pre-built container image and even build your custom container image.
  • supports users custom training algorithms provided through a Docker image adhering to the documented specification.
  • also provides optimized MXNet, Tensorflow, Chainer & PyTorch containers
  • ensures that ML model artifacts and other system artifacts are encrypted in transit and at rest.
  • requests to the API and console are made over a secure (SSL) connection.
  • stores code in ML storage volumes, secured by security groups and optionally encrypted at rest.
  • SageMaker Neo is a capability that enables machine learning models to train once and run anywhere in the cloud and at the edge.

Amazon SageMaker Unified Studio

  • is a unified web-based development environment announced at re:Invent 2024 and GA in March 2025.
  • is part of the next generation of Amazon SageMaker — the center for all data, analytics, and AI.
  • breaks down silos in data and tools, giving data engineers, data scientists, data analysts, and ML developers a single development experience.
  • brings together functionality from Amazon EMR, AWS Glue, Amazon Redshift, Amazon Bedrock, and SageMaker AI Studio.
  • enables discovering data and AI assets from across the organization, then collaborating in projects to securely build and share analytics and AI artifacts.
  • includes SageMaker Lakehouse — unifies data across data lakes, data warehouses, operational databases, and enterprise applications with Apache Iceberg compatibility.
  • includes SageMaker Data and AI Governance for integrated access controls and data governance.
  • offers choice of IDEs including JupyterLab, Code Editor (based on VS Code OSS), and RStudio.
  • Note: The previous “SageMaker Studio” experience was renamed to “SageMaker Studio Classic” (November 2023) and is now part of SageMaker AI.

Amazon SageMaker Canvas

  • is a no-code machine learning service for business analysts (launched November 2021).
  • enables building accurate ML models without writing code or requiring ML expertise.
  • provides visual, point-and-click interface for data preparation and model building.
  • supports tabular, image, and text data for predictions.
  • connects to 50+ data sources including S3, Redshift, Snowflake, and SaaS applications.
  • offers ready-to-use ML models and custom model building capabilities.
  • includes generative AI capabilities (October 2023) for text generation, summarization, and content creation.
  • provides automated feature engineering, algorithm selection, and hyperparameter tuning.
  • enables one-click model deployment and batch predictions.
  • supports collaboration between business analysts and data scientists.
  • is the recommended migration path for Amazon Forecast customers for time-series forecasting.

Amazon SageMaker Clarify

  • provides bias detection, model explainability, and foundation model evaluation capabilities.
  • detects pre-training bias (Class Imbalance, DPL, KL Divergence) and post-training bias (Disparate Impact, Demographic Parity Difference).
  • provides SHAP-based feature importance for individual predictions and partial dependence plots.
  • evaluates foundation models for accuracy, robustness, toxicity, and stereotyping.
  • integrates with Model Monitor for continuous bias drift detection in production.
  • identifies biases in training data and ML models across different groups (age, gender, income, etc.).
  • detects potential bias during data preparation, after model training, and in deployed models.
  • generates detailed reports quantifying different types of possible bias.
  • provides feature importance graphs to explain model predictions.
  • integrates with SageMaker Data Wrangler for bias detection during data preparation.
  • supports continuous monitoring of deployed models for bias drift.
  • helps meet regulatory requirements and ethical AI standards.
  • produces reports for internal presentations and compliance documentation.

Amazon SageMaker HyperPod

  • is purpose-built infrastructure for distributed training at scale (GA November 2023).
  • reduces time to train foundation models by up to 40% with optimized infrastructure.
  • supports GPU-based and AWS Trainium-based instances for cost-effective training.
  • provides automated cluster health monitoring and node replacement.
  • enables training for weeks or months with automated resiliency.
  • automatically saves checkpoints and resumes training from last checkpoint on failure.
  • efficiently distributes models and data across thousands of compute resources.
  • includes preconfigured distributed training libraries for popular frameworks.
  • provides recipes for accelerating foundation model training and fine-tuning.
  • offers flexible training plans to meet timelines and budgets.

Amazon Textract

  • Textract provides OCR and helps add document text detection and analysis to the applications.
  • includes simple, easy-to-use API operations that can analyze image files and PDF files.
  • extracts text, handwriting, tables, and forms from scanned documents.
  • supports Queries for extracting specific information from documents using natural language questions.
  • provides Lending API for automated mortgage document processing.

Amazon Comprehend

  • Comprehend is a managed natural language processing (NLP) service to find insights and relationships in text.
  • identifies the language of the text; extracts key phrases, places, people, brands, or events; understands how positive or negative the text is; analyzes text using tokenization and parts of speech; and automatically organizes a collection of text files by topic.
  • can analyze a collection of documents and other text files (such as social media posts) and automatically organize them by relevant terms or topics.
  • supports custom entity recognition and custom classification for domain-specific NLP.
  • provides Comprehend Medical for extracting medical information such as conditions, medications, dosages, and their relationships.
⚠️ Note (April 2026): Amazon Comprehend topic modeling, event detection, and prompt safety classification features are no longer available to new customers as of April 30, 2026. Existing customers can continue to use these features.

Amazon Lex

  • is a service for building conversational interfaces using voice and text.
  • provides the advanced deep learning functionalities of automatic speech recognition (ASR) for converting speech to text, and natural language understanding (NLU) to recognize the intent of the text, to enable building applications with highly engaging user experiences and lifelike conversational interactions.
  • common use cases of Lex include: Application/Transactional bot, Informational bot, Enterprise Productivity bot, and Device Control bot.
  • leverages Lambda for Intent fulfillment, Cognito for user authentication & Polly for text-to-speech.
  • scales to customers’ needs and does not impose bandwidth constraints.
  • is a completely managed service so users don’t have to manage the scaling of resources or maintenance of code.
  • uses deep learning to improve over time.
  • supports Generative AI features powered by Amazon Bedrock LLMs including:
    • AMAZON.QnAIntent — handles FAQ-style questions using knowledge bases without configuring individual intents.
    • Assisted NLU (2025) — uses LLMs to improve intent classification and slot resolution accuracy while staying within configured intents.
    • Descriptive Bot Builder — generates bot configurations from natural language descriptions.

Amazon Polly

  • text into speech
  • uses advanced deep-learning technologies to synthesize speech that sounds like a human voice.
  • provides dozens of lifelike voices across 60+ languages.
  • supports multiple voice engines:
    • Standard — concatenative synthesis voices.
    • Neural — higher-quality neural TTS voices.
    • Long-Form — optimized for long content like articles and books.
    • Generative (2024-2025) — the most natural-sounding voices using generative AI, with new voices continually added.
  • supports Lexicons to customize pronunciation of specific words & phrases.
  • supports Speech Synthesis Markup Language (SSML) tags like prosody so users can adjust the speech rate, pitch, pauses, or volume.
  • supports bidirectional streaming API for real-time applications.

Amazon Rekognition

  • analyzes image and video
  • identify objects, people, text, scenes, and activities in images and videos, as well as detect any inappropriate content.
  • provides highly accurate facial analysis and facial search capabilities that can be used to detect, analyze, and compare faces for a wide variety of user verification, people counting, and public safety use cases.
  • helps identify potentially unsafe or inappropriate content across both image and video assets and provides detailed labels that help accurately control what you want to allow based on your needs.
  • provides Rekognition Custom Labels (launched December 2019) – an AutoML feature to build custom ML models for detecting specific objects and scenes unique to business needs.
  • Custom Labels requires as few as 10 sample images per label to train custom models.
  • Custom Labels automatically selects optimal ML algorithms and trains models without requiring ML expertise.
  • enables identifying business-specific items like machine parts, product defects, or brand logos.

Amazon Forecast

⚠️ SERVICE CLOSED TO NEW CUSTOMERS (July 29, 2024)
Amazon Forecast is no longer available to new customers. Existing customers can continue using the service. Migration: Use Amazon SageMaker Canvas for time-series forecasting with a no-code interface.
  • Amazon Forecast is a fully managed time-series forecasting service that uses statistical and machine learning algorithms to deliver highly accurate time-series forecasts and is built for business metrics analysis.
  • automatically tracks the accuracy of the model over time as new data is imported.
  • provides six built-in algorithms which include ARIMA, Prophet, NPTS, ETS, CNN-QR, and DeepAR+.
  • integrates with AutoML to choose the optimal model for the datasets.

Amazon SageMaker Ground Truth

  • helps build highly accurate training datasets for machine learning quickly.
  • offers easy access to labelers through Amazon Mechanical Turk and provides them with built-in workflows and interfaces for common labeling tasks.
  • allows using your own labelers or use vendors recommended by Amazon through AWS Marketplace.
  • helps lower labeling costs by up to 70% using automatic labeling, which works by training Ground Truth from data labeled by humans so that the service learns to label data independently.
  • provides annotation consolidation to help improve the accuracy of the data object’s labels.

Amazon Translate

  • provides natural and fluent language translation
  • a neural machine translation service that delivers fast, high-quality, and affordable language translation.
  • Neural machine translation is a form of language translation automation that uses deep learning models to deliver more accurate and natural-sounding translation than traditional statistical and rule-based translation algorithms.
  • allows content localization – such as websites and applications – for international users, and to easily translate large volumes of text efficiently.

Amazon Transcribe

  • provides speech-to-text capability
  • uses a deep learning process called automatic speech recognition (ASR) to convert speech to text quickly and accurately.
  • can be used to transcribe customer service calls, automate closed captioning and subtitling, and generate metadata for media assets to create a fully searchable archive.
  • adds punctuation and formatting so that the output closely matches the quality of manual transcription at a fraction of the time and expense.
  • process audio in batch or near real-time.
  • supports automatic language identification.
  • supports custom vocabulary to generate more accurate transcriptions for domain-specific words and phrases like product names, technical terminology, or names of individuals.
  • supports specifying a list of words to remove from transcripts.
  • provides Transcribe Call Analytics (launched August 2021) for extracting insights from customer conversations.
  • Call Analytics generates turn-by-turn transcripts with speaker identification and sentiment analysis.
  • supports real-time Call Analytics (November 2022) for live conversation insights and agent assistance.
  • provides Transcribe Medical for healthcare and medical transcription with HIPAA eligibility.

Amazon Kendra

  • is an intelligent search service that uses NLP and advanced ML algorithms to return specific answers to search questions from your data.
  • uses its semantic and contextual understanding capabilities to decide whether a document is relevant to a search query.
  • returns specific answers to questions, giving users an experience that’s close to interacting with a human expert.
  • provides a unified search experience by connecting multiple data repositories to an index and ingesting and crawling documents.
  • can use the document metadata to create a feature-rich and customized search experience for the users, helping them efficiently find the right answers to their queries.
  • can be used as a retriever for Amazon Quick (formerly Amazon Q Business) to power enterprise search with generative AI.

Augmented AI (Amazon A2I)

  • Augmented AI (Amazon A2I) is an ML service that makes it easy to build the workflows required for human review.
  • brings human review to all developers, removing the undifferentiated heavy lifting associated with building human review systems or managing large numbers of human reviewers, whether it runs on AWS or not.
  • integrates with Amazon Textract for document processing and Amazon Rekognition for content moderation.
  • supports private review teams, Amazon Mechanical Turk, and AWS Marketplace vendors.

Amazon Personalize

  • Personalize is a fully managed machine learning service that uses data to generate item recommendations.
  • can also generate user segments based on the users’ affinity for certain items or item metadata.
  • generates recommendations primarily based on item interaction data that comes from the users interacting with items in the catalog.
  • includes API operations for real-time personalization, and batch operations for bulk recommendations and user segments.

Amazon Panorama

⚠️ SERVICE END OF SUPPORT — May 31, 2026
AWS will end support for AWS Panorama on May 31, 2026. After this date, you will no longer be able to access the AWS Panorama console or resources, and Panorama devices will become non-functional. Consider migrating to Amazon SageMaker AI with edge deployment or third-party edge CV solutions.
  • brings computer vision to the on-premises camera network.
  • AWS Panorama Appliance or another compatible device can be installed in the data center and registered with AWS Panorama to deploy computer vision applications from the cloud.
  • AWS Panorama Appliance
    • is a compact edge appliance that uses a powerful system-on-module (SOM) that is optimized for ML workloads.
    • can run multiple computer vision models against multiple video streams in parallel and output the results in real-time.
    • is designed for use in commercial and industrial settings and is rated for dust and liquid protection.
  • works with the existing real-time streaming protocol (RTSP) network cameras.

Amazon Fraud Detector

  • Fraud Detector is a fully managed service to identify potentially fraudulent online activities such as online payment fraud and fake account creation.
  • takes care of all the heavy lifting such as data validation and enrichment, feature engineering, algorithm selection, hyperparameter tuning, and model deployment.

AWS IoT Greengrass ML Inference

  • IoT Greengrass helps perform machine learning inference locally on devices, using models that are created, trained, and optimized in the cloud.
  • provides flexibility to use machine learning models trained in SageMaker or to bring your pre-trained model stored in S3.
  • helps get inference results with very low latency to ensure the IoT applications can respond quickly to local events.

Amazon Elastic Inference

⚠️ SERVICE DEPRECATED (April 2023)
Amazon Elastic Inference is no longer available to new customers. Alternatives: Use AWS Inferentia instances (Inf1/Inf2) for better price-performance on inference workloads, or use SageMaker AI real-time inference endpoints with appropriate instance types.
  • helped attach low-cost GPU-powered acceleration to EC2 and SageMaker instances or ECS tasks to reduce the cost of running deep learning inference by up to 75%.
  • supported TensorFlow, Apache MXNet, and ONNX models.

AWS Certification Exam Practice Questions

  • Questions are collected from Internet and the answers are marked as per my knowledge and understanding (which might differ with yours).
  • AWS services are updated everyday and both the answers and questions might be outdated soon, so research accordingly.
  • AWS exam questions are not updated to keep up the pace with AWS updates, so even if the underlying feature has changed the question might not be updated
  • Open to further feedback, discussion and correction.
  1. A company has built a deep learning model and now wants to deploy it using the SageMaker Hosting Services. For inference, they want a cost-effective option that guarantees low latency but still comes at a fraction of the cost of using a GPU instance for your endpoint. As a machine learning Specialist, what feature should be used?
    1. Inference Pipeline
    2. Elastic Inference [Note: Elastic Inference is deprecated. Current recommendation is AWS Inferentia (Inf2) instances for cost-effective inference.]
    3. SageMaker Ground Truth
    4. SageMaker Neo
  2. A machine learning specialist works for an online retail company that sells health products. The company allows users to enter reviews of the products they buy from the website. The company wants to make sure the reviews do not contain any offensive or unsafe content, such as obscenities or threatening language. Which Amazon SageMaker algorithm or service will allow scanning user’s review text in the simplest way?
    1. BlazingText
    2. Transcribe
    3. Semantic Segmentation
    4. Comprehend
  3. A company develops a tool whose coverage includes blogs, news sites, forums, videos, reviews, images, and social networks such as Twitter and Facebook. Users can search data by using Text and Image Search, and use charting, categorization, sentiment analysis, and other features to provide further information and analysis. They want to provide Image and text analysis capabilities to the applications which include identifying objects, people, text, scenes, and activities, and also provide highly accurate facial analysis and facial recognition. What service can provide this capability?
    1. Amazon Comprehend
    2. Amazon Rekognition
    3. Amazon Polly
    4. Amazon SageMaker
  4. A company wants to build generative AI applications using foundation models without managing infrastructure. Which service should they use?
    1. Amazon SageMaker
    2. Amazon Comprehend
    3. Amazon Bedrock
    4. Amazon Lex
  5. A development team wants an AI assistant that provides real-time code suggestions and security scanning in their IDE. Which service should they use?
    1. Amazon CodeGuru
    2. Amazon Q Developer (transitioning to Kiro)
    3. AWS Cloud9
    4. Amazon SageMaker
  6. A business analyst with no ML experience wants to build accurate ML models using a visual interface. Which service should they use?
    1. Amazon SageMaker Studio
    2. Amazon SageMaker Canvas
    3. Amazon Forecast
    4. Amazon Personalize
  7. A company needs to detect bias in their ML models and explain predictions for regulatory compliance. Which service should they use?
    1. Amazon SageMaker Ground Truth
    2. Amazon Inspector
    3. Amazon SageMaker Clarify
    4. AWS Audit Manager
  8. A company wants to train large foundation models for weeks with automated resiliency and checkpoint management. Which service should they use?
    1. Amazon SageMaker Training Jobs
    2. Amazon SageMaker HyperPod
    3. AWS Batch
    4. Amazon EC2 with GPU instances
  9. A contact center wants real-time insights from customer calls including sentiment analysis and agent assistance. Which service should they use?
    1. Amazon Transcribe
    2. Amazon Transcribe Call Analytics
    3. Amazon Comprehend
    4. Amazon Connect
  10. A company wants to build custom image recognition models to identify specific machine parts with minimal training data. Which service should they use?
    1. Amazon Rekognition (standard)
    2. Amazon Rekognition Custom Labels
    3. Amazon SageMaker
    4. Amazon Textract
  11. A company wants to deploy AI agents that can perform multi-step workflows, access enterprise tools, and maintain state across conversations in production. Which service should they use?
    1. Amazon Lex
    2. Amazon SageMaker AI
    3. Amazon Bedrock AgentCore
    4. AWS Step Functions
  12. A company needs Amazon’s own foundation models that offer industry-leading price-performance for text, image, and video generation tasks. Which model family should they use?
    1. Amazon Titan
    2. Amazon Nova
    3. Amazon Comprehend
    4. Amazon SageMaker JumpStart
  13. An enterprise wants a unified platform for data engineering, analytics, ML development, and generative AI that breaks down tool silos. Which service should they use?
    1. Amazon SageMaker AI
    2. Amazon EMR
    3. Amazon SageMaker Unified Studio
    4. AWS Glue
  14. A company wants to implement safety guardrails for their generative AI application to filter harmful content, block prompt injections, and protect sensitive information. Which service should they use?
    1. AWS WAF
    2. Amazon Macie
    3. Amazon Bedrock Guardrails
    4. AWS Shield

References