AWS Generative AI Professional Practice Exams

AWS Generative AI Professional Exams | Practice Questions

10 Question Exam AWS Generative AI Professional 10-question practice exam badge
10 Question Exam AWS Generative AI Professional 10-question practice exam badge
15 Question Exam AWS Generative AI Professional 15-question practice exam badge
15 Question Exam AWS Generative AI Professional 15-question practice exam badge
25 Question Exam AWS Generative AI Professional 25-question practice exam badge
25 Question Exam AWS Generative AI Professional 25-question practice exam badge
25 Question Exam AWS Generative AI Professional 25-question practice exam badge
25 Question Exam AWS Generative AI Professional 25-question practice exam badge

AWS Generative AI Developer – Professional Exam Facts

  • Exam code: AIP-C01
  • 75 total questions: 65 scored and 10 unscored
  • Question types are multiple choice and multiple response
  • Scaled score from 100–1,000 with a passing score of 750
  • Target candidate: 2+ years building production applications and 1 year implementing GenAI solutions
  • Compensatory scoring model with no penalty for guessing

AIP-C01 Content Domains & Weighting

  • Domain 1: Foundation Model Integration, Data Management, and Compliance – 31%
  • Domain 2: Implementation and Integration – 26%
  • Domain 3: AI Safety, Security, and Governance – 20%
  • Domain 4: Operational Efficiency and Optimization for GenAI Applications – 12%
  • Domain 5: Testing, Validation, and Troubleshooting – 11%

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AWS Certified Generative AI Developer – Professional Exam Topics

Exam Basics

  • Exam code: AIP-C01
  • Format: 75 total questions, including 65 scored and 10 unscored questions
  • Question types: multiple choice and multiple response
  • Multiple-response questions require all correct responses for credit
  • Scoring: scaled from 100–1,000 with a passing score of 750
  • Scoring model: compensatory across the entire exam
  • Unanswered questions are incorrect, but there is no penalty for guessing
  • The exam validates production implementation of generative AI solutions on AWS

Target Candidate Experience

  • At least 2 years building production-grade applications on AWS or with open-source technologies
  • General AI/ML or data engineering experience
  • At least 1 year of hands-on experience implementing generative AI solutions
  • Experience with AWS compute, storage, databases, and networking
  • Understanding of AWS security, identity, deployment, infrastructure as code, monitoring, and cost optimization
  • Comfort with APIs, serverless services, containers, CI/CD, observability, and enterprise integrations

Domain 1: Foundation Model Integration, Data Management, and Compliance (31%)

  • Analyze business requirements and design GenAI architectures that fit technical, operational, and compliance constraints
  • Create proofs of concept with Amazon Bedrock and apply the AWS Well-Architected Generative AI Lens
  • Select foundation models by capability, limitations, benchmark results, cost, latency, and use-case fit
  • Design dynamic model selection, model fallback, graceful degradation, and Cross-Region Inference
  • Manage customized and fine-tuned models with SageMaker AI, Model Registry, versioning, rollback, and retirement processes
  • Validate and process text, image, audio, and tabular data for foundation model consumption
  • Use AWS Glue Data Quality, SageMaker Data Wrangler, SageMaker Processing, Amazon Transcribe, and Amazon Comprehend
  • Design vector stores with Bedrock Knowledge Bases, OpenSearch Service, Aurora PostgreSQL with pgvector, DynamoDB, RDS, and S3
  • Apply metadata, indexing, sharding, synchronization, refresh, and data-quality strategies
  • Select chunking, embedding, semantic search, keyword search, hybrid search, and reranking approaches
  • Use Amazon Titan embeddings and Bedrock reranker models
  • Implement query expansion, decomposition, transformation, function calling, and Model Context Protocol retrieval clients
  • Create governed prompts with Bedrock Prompt Management, Prompt Flows, reusable templates, approval workflows, and versioning
  • Test prompts for quality, edge cases, regression, structured outputs, context handling, and consistency

Domain 2: Implementation and Integration (26%)

  • Build agentic AI systems with memory, state, tools, stopping conditions, timeouts, and resource boundaries
  • Use Strands Agents, AWS Agent Squad, Model Context Protocol, Step Functions, Lambda, and IAM
  • Implement single-agent, multi-agent, ReAct, human-in-the-loop, and tool-calling patterns
  • Deploy models through Amazon Bedrock on-demand inference, provisioned throughput, SageMaker AI endpoints, and containers
  • Account for GPU, memory, model loading, token capacity, latency, throughput, and model size
  • Apply model cascading and use smaller specialized models for routine tasks
  • Integrate GenAI with enterprise applications through API Gateway, Lambda, EventBridge, SQS, Step Functions, and webhooks
  • Apply federation, role-based access, least privilege, hybrid integration, Outposts, and Wavelength
  • Build CI/CD pipelines with CodePipeline and CodeBuild, including tests, security scans, quality gates, and rollback
  • Implement synchronous, asynchronous, streaming, WebSocket, and server-sent event model APIs
  • Handle retries, exponential backoff, throttling, quotas, rate limits, fallbacks, and dynamic model routing
  • Build application interfaces with Amplify, OpenAPI, Bedrock Data Automation, Prompt Flows, and Amazon Q Developer

Domain 3: AI Safety, Security, and Governance (20%)

  • Filter unsafe or policy-violating inputs and outputs with Amazon Bedrock Guardrails
  • Build moderation workflows with Lambda, Step Functions, Amazon Comprehend, and custom classifiers
  • Reduce hallucinations through grounding, Bedrock Knowledge Bases, fact verification, confidence scoring, and structured outputs
  • Detect prompt injection, jailbreaks, adversarial inputs, unsafe tool calls, and model misuse
  • Implement defense in depth through preprocessing, model controls, post-processing, and API filtering
  • Protect deployments with VPC endpoints, IAM, least privilege, Lake Formation, KMS, and secure network boundaries
  • Detect and protect PII with Amazon Comprehend, Amazon Macie, masking, anonymization, redaction, and retention controls
  • Track data lineage, source attribution, metadata, prompts, outputs, and model decisions
  • Use AWS Glue Data Catalog, CloudTrail, CloudWatch Logs, model cards, and audit evidence
  • Monitor for misuse, drift, bias drift, policy violations, unsafe output, and compliance failures
  • Apply fairness evaluation, transparency, confidence, explanations, source attribution, and Bedrock agent tracing
  • Use LLM-as-a-judge, policy-based guardrails, model limitations, and automated compliance checks

Domain 4: Operational Efficiency and Optimization (12%)

  • Estimate, track, and reduce input and output token usage
  • Use context pruning, prompt compression, response limits, and conversation-history optimization
  • Apply model cascading, tiered model selection, batching, concurrency, and capacity planning
  • Optimize Bedrock provisioned throughput and resource utilization
  • Use semantic caching, prompt caching, result fingerprinting, deterministic hashing, and edge caching
  • Reduce latency through streaming, parallel requests, precomputation, and latency-optimized models
  • Optimize RAG indexes, queries, retrieval paths, and vector database performance
  • Tune temperature, top-k, top-p, batch inference, and concurrent model invocation
  • Monitor tokens, latency, throughput, cost, hallucination rate, drift, retrieval quality, and tool calls
  • Use Bedrock Model Invocation Logs, CloudWatch, X-Ray, Logs Insights, Synthetics, and Managed Grafana

Domain 5: Testing, Validation, and Troubleshooting (11%)

  • Evaluate relevance, factual accuracy, consistency, fluency, safety, usefulness, and business outcomes
  • Use Bedrock Model Evaluations, A/B tests, canary tests, multi-model comparisons, and LLM-as-a-judge
  • Collect human feedback, ratings, annotations, corrections, and user-experience signals
  • Build continuous evaluation, regression tests, automated quality gates, and deployment validation
  • Measure RAG relevance, context matching, retrieval latency, agent task completion, and tool effectiveness
  • Diagnose context-window overflow, truncation, missing context, and incomplete responses
  • Troubleshoot Bedrock API requests, credentials, permissions, quotas, parameters, and response handling
  • Correct prompt, chunking, embedding, vectorization, retrieval, drift, preprocessing, and output-schema issues
  • Use CloudWatch Logs, Logs Insights, X-Ray, synthetic workflows, output diffing, and golden datasets

Outside the Primary Exam Scope

  • Developing or training foundation models from scratch
  • Advanced machine learning research and algorithm development
  • Broad data engineering that is unrelated to GenAI application integration
  • Feature engineering for traditional machine learning workloads
  • Services and technologies that do not support the tested GenAI developer role

What the Exam Emphasizes

  • Production GenAI application design and implementation
  • Foundation model integration rather than model training
  • RAG, embeddings, vector stores, chunking, retrieval, and reranking
  • Prompt engineering, prompt governance, agents, tools, and MCP
  • Enterprise APIs, event-driven integration, serverless, containers, and CI/CD
  • Safety, privacy, security, governance, responsible AI, and auditability
  • Cost, token, latency, throughput, quality, and operational tradeoffs
  • Evaluation, observability, troubleshooting, and continuous improvement

Core Generative AI and Machine Learning Services

  • Amazon Bedrock, Bedrock AgentCore, Bedrock Knowledge Bases, Prompt Management, and Prompt Flows
  • Amazon SageMaker AI, Clarify, Data Wrangler, Ground Truth, JumpStart, Model Monitor, Model Registry, Neo, Processing, and Unified Studio
  • Amazon Titan models and embeddings
  • Amazon Comprehend, Kendra, Lex, Rekognition, Textract, and Transcribe
  • Amazon Q Business, Q Business Apps, Q Developer, and Amazon Quick
  • Amazon Augmented AI for human review workflows

Application, Data, and Integration Services

  • API Gateway, AppSync, Lambda, App Runner, EC2, Outposts, and Wavelength
  • Amazon ECS, EKS, ECR, and AWS Fargate
  • Amazon EventBridge, SNS, SQS, Step Functions, AppFlow, and AppConfig
  • Amazon OpenSearch Service, Aurora, RDS, DynamoDB, DynamoDB Streams, DocumentDB, Neptune, and ElastiCache
  • Amazon S3, EBS, EFS, Intelligent-Tiering, Lifecycle policies, and Cross-Region Replication
  • AWS Glue, Athena, EMR, Kinesis, MSK, and QuickSight

Security, Developer, and Operations Services

  • IAM, IAM Access Analyzer, IAM Identity Center, Cognito, KMS, Encryption SDK, Secrets Manager, Macie, and WAF
  • Amazon VPC, PrivateLink, CloudFront, Global Accelerator, Route 53, and Elastic Load Balancing
  • CloudTrail, CloudWatch, CloudWatch Logs, CloudWatch Synthetics, Managed Grafana, and X-Ray
  • AWS CDK, CloudFormation, CLI, CodeArtifact, CodeBuild, CodeDeploy, CodePipeline, Kiro, and AWS SDKs
  • AWS Auto Scaling, Systems Manager, Service Catalog, Well-Architected Tool, Cost Explorer, and Cost Anomaly Detection
  • AWS DataSync and Transfer Family for data movement and integration

How to Prepare for AIP-C01

  1. Build RAG solutions with Bedrock Knowledge Bases, embeddings, vector stores, metadata filters, chunking, hybrid search, and reranking
  2. Compare foundation models by quality, context window, latency, cost, modality, limitations, and business fit
  3. Practice Prompt Management, Prompt Flows, Guardrails, prompt versioning, regression testing, and structured outputs
  4. Build agents with tools, memory, state, MCP, Step Functions, Lambda, IAM restrictions, and human review
  5. Implement Bedrock APIs with streaming, asynchronous processing, retries, throttling, routing, and graceful degradation
  6. Practice enterprise integration with API Gateway, EventBridge, SQS, Step Functions, CodePipeline, and CodeBuild
  7. Apply prompt-injection defenses, content moderation, PII detection, VPC endpoints, KMS, least privilege, and output filtering
  8. Review model cards, lineage, audit logs, fairness, transparency, source attribution, responsible AI, and continuous governance
  9. Optimize tokens, model selection, caching, provisioned throughput, latency, concurrency, RAG performance, and parameter settings
  10. Evaluate and troubleshoot with Bedrock Model Evaluations, LLM-as-a-judge, human feedback, golden datasets, CloudWatch, X-Ray, and retrieval diagnostics

Top-Level AIP-C01 Exam Objectives

  • Foundation Model Integration, Data Management, and Compliance
  • Implementation and Integration
  • AI Safety, Security, and Governance
  • Operational Efficiency and Optimization for GenAI Applications
  • Testing, Validation, and Troubleshooting