AWS Certified Generative AI Developer (Professional (AIP-C01)) — exam guide & mock tests | explainx.ai

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AWS Certified Generative AI Developer (Professional (AIP-C01)) — exam guide & mock tests | explainx.ai | explainx.ai

Who this certification is for

AWS positions the Generative AI Developer – Professional (AIP-C01) credential for developers who integrate foundation models into production applications and business workflows on AWS. The exam tests practical judgment about Bedrock, RAG, agents, security, and cost — not model training research.

→Integrating foundation models into applications with Amazon Bedrock, SageMaker AI, and AWS compute patterns
→Designing RAG architectures — vector stores, Knowledge Bases, chunking, hybrid search, reranking
→Building agentic AI with Strands Agents, Step Functions, MCP tool servers, and Bedrock Agents
→Implementing prompt engineering, Prompt Management, Prompt Flows, and guardrails for production FM behavior
→Applying security, governance, and Responsible AI — PII handling, IAM, VPC isolation, audit trails
→Optimizing GenAI for cost and latency — token efficiency, caching, provisioned throughput, model routing
→Monitoring and troubleshooting GenAI apps — CloudWatch, Model Invocation Logs, evaluation frameworks
→Typically 2+ years building production apps on AWS and 1 year hands-on implementing GenAI solutions

Official exam format

Summarized from AWS's public AIP-C01 Exam Guide — confirm on AWS Certification before you schedule.

Level: Professional — production GenAI integration on AWS, not model training
Format: 65 scored questions (75 total including 10 unscored) · multiple choice and multiple response · no penalty for guessing
Duration: 180 minutes · Pearson VUE or online proctored
Pass score: 750 / 1000 scaled (compensatory scoring — pass overall, not per domain)
Official price: $300 per attempt
Questions: 65 scored + 10 unscored

Professional tierMCQ + multiple responseNo penalty for guessing

Task statements by domain

The official guide lists weighted domains and granular task statements. explainx practice questions target these competencies — use this map to plan study time and pick focused mock tests.

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

1.1 Analyze requirements and design GenAI solutions
- · Architectural designs aligned to business needs and technical constraints
- · POC implementations with Bedrock to validate feasibility before full deployment
- · Standardized components via Well-Architected Framework and Generative AI Lens
1.2 Select and configure foundation models
- · FM selection by capability benchmarks, limitations, and use-case fit
- · Dynamic model selection with Lambda, API Gateway, AppConfig — provider switching without code changes
- · Resilience: Step Functions circuit breakers, Cross-Region Inference, graceful degradation
- · Customization lifecycle: SageMaker fine-tuning, LoRA/adapters, Model Registry, rollback pipelines
1.3 Data validation and processing pipelines

Six production scenarios

Practice questions are scenario-framed around realistic AWS GenAI workloads — RAG, agents, compliance, multimodal pipelines, cost optimization, and enterprise integration.

Enterprise Knowledge Assistant (RAG)

Build an internal Q&A system over PDFs, wikis, and SharePoint exports. Use Bedrock Knowledge Bases with OpenSearch, implement hybrid search with reranking, and keep embeddings fresh with incremental sync pipelines.

Domains: FM Integration & Data · Operational Efficiency

Bedrock Agent & Tool Orchestration

Automate multi-step research and action workflows with Strands Agents, Step Functions ReAct loops, and Lambda MCP tool servers. Enforce IAM boundaries and circuit breakers for production reliability.

Domains: Implementation & Integration · Testing & Validation

Regulated Data & Responsible AI

Deploy a GenAI assistant over financial or healthcare documents. Apply Guardrails, Comprehend/Macie PII redaction, VPC-only Bedrock access, and comprehensive CloudTrail audit trails for regulatory readiness.

Domains: Safety & Governance · FM Integration & Data

Multimodal Document Processing

Ingest scanned forms, audio recordings, and tabular exports into a unified FM pipeline. Use Transcribe, SageMaker Processing, Glue Data Quality, and Bedrock multimodal models with validated JSON outputs.

Domains: FM Integration & Data · Implementation & Integration

Cost-Optimized GenAI at Scale

A high-traffic chatbot must balance response quality with inference cost. Implement semantic caching, model routing by query complexity, provisioned throughput planning, and CloudWatch cost anomaly alerts.

In scope on the exam

✓ Foundation model selection, customization deployment, and lifecycle management
✓ RAG architecture — vector stores, chunking, embeddings, hybrid search, reranking
✓ Bedrock Knowledge Bases and managed retrieval augmentation
✓ Prompt engineering, Prompt Management, Prompt Flows, and governance
✓ Agentic AI — Strands Agents, Step Functions ReAct, MCP integrations
✓ Enterprise integration — API Gateway, EventBridge, CI/CD for GenAI components
✓ Guardrails, PII handling, IAM/VPC security, compliance and audit logging
✓ Cost optimization — token efficiency, caching, model routing, provisioned throughput
✓ GenAI monitoring, evaluation, and troubleshooting unique to LLM workloads

Explicitly out of scope

✗ Model development, training, and advanced ML techniques
✗ Data engineering and feature engineering (beyond FM consumption prep)
✗ Deep SageMaker training job configuration unrelated to FM deployment

Technologies you should know

Amazon Bedrock — FMs, Knowledge Bases, Guardrails, Prompt Management, Agents, Model Evaluations
Amazon SageMaker AI — fine-tuning, endpoints, Model Registry, Data Wrangler, Processing
Vector search — OpenSearch Service, Aurora pgvector, Bedrock managed vector stores
Orchestration — Step Functions, Lambda, EventBridge, API Gateway
Agents — Strands Agents, Agent Squad, MCP tool servers on Lambda/ECS
Data — Glue Data Quality/Catalog, S3, DynamoDB, Transcribe, Comprehend, Macie
Security — IAM, VPC endpoints, Lake Formation, CloudTrail, CloudWatch
Dev tools — Amazon Q Developer, Amplify, CodePipeline, AppConfig

Practice on explainx — live now

Independent mock exams aligned to the AIP-C01 Exam Guide — not affiliated with AWS's proctored certification. 1,200 multiple-choice questions in the bank, shuffled every attempt, instant explanations after submit.

Recommended study path

Complete AWS Skill Builder Exam Prep Plan for AIP-C01
Build a Bedrock Knowledge Base RAG app with hybrid search and reranking
Deploy a Bedrock Agent or Strands Agent workflow with Lambda tool integrations
Configure Guardrails, PII detection, and VPC-only Bedrock access for a sample app
Implement token tracking and semantic caching with CloudWatch dashboards
Run Bedrock Model Evaluations and set up regression testing for prompt changes
Practice explainx mock tests for scenario-style MCQ practice before the proctored sitting

Official exam page →AWS Skill Builder exam prep →

FAQ

Quick answers for candidates preparing with explainx mock exams (not Anthropic support).

Anthropic's ~301-level proctored certification for solution architects building production apps with Claude Code, the Claude Agent SDK, MCP, and structured output. The official exam has 60 multiple-choice questions in 120 minutes, four scenario frames per sitting, and a scaled pass score of 720/1000.

Five weighted areas: Agentic Architecture & Orchestration (27%), Tool Design & MCP Integration (18%), Claude Code Configuration & Workflows (20%), Prompt Engineering & Structured Output (20%), and Context Management & Reliability (15%).

No. explainx.ai offers independent practice mock tests aligned to the public Foundations Exam Guide domains, task statements, and scenarios. Official registration and proctoring are through Anthropic Academy / Skilljar.

Lifetime access to all Claude Certified Architect mock tests on explainx.ai is $5 USD (one-time). Each attempt draws a shuffled subset from a bank of 1,000+ practice questions with explanations after submit.

You can browse certification and test pages without an account. Starting a timed mock test requires signing in and purchasing lifetime practice access for that program.

Eight mock tests per program including a full exam simulation, plus 1,000+ banked multiple-choice items. Programs include Claude Certified Architect (agentic architecture, MCP, Claude Code) and AWS GenAI Developer Professional (Bedrock, RAG, agents, governance).

Questions mirror the six official production scenarios: customer support agents, Claude Code development, multi-agent research, developer productivity, CI/CD with Claude Code, and structured data extraction.

Use Anthropic's Foundations Exam Guide and Academy courses for the proctored exam. Use explainx mock tests for timed MCQ practice, domain drills, and instant feedback — especially scenario-framed items and tradeoff questions.

A business-level, non-technical Google Cloud certification. The 90-minute exam has 50-60 multiple-choice questions across four domains — gen AI fundamentals, Google Cloud's gen AI offerings, techniques to improve model output, and business strategy — with no prerequisites required.

Microsoft's Associate-level certification for Azure AI engineers building generative AI apps and agents with Microsoft Foundry and Python. The 120-minute exam covers five domains with a minimum passing score of 700/1000.

AWS Certified Generative AI Developer

PRACTICE ACCESS

Mock tests

Foundations Practice 1

Foundations Practice 2

RAG & Vector Store Focus

Agents & Integration Focus

Security & Governance Focus

Cost, Monitoring & Evaluation Focus

Scenario Marathon

Full Exam Simulation

Exam domains

Production scenarios