AIP-C01
Free AWS Certified GenAI Developer (AIP-C01) Practice Test
12 exam-style questions with full explanations — no sign-up. Score yourself, then close your gaps with the full course.
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A financial services company needs to implement a generative AI solution that processes customer financial documents containing PII and account numbers. The solution must ensure that no customer data is used to train or improve the foundation model, and all data must remain within the company's AWS account. Which combination of services and configurations meets these requirements?
Amazon Bedrock on-demand inference does not use customer prompts or outputs for model training. VPC endpoints ensure data never traverses the public internet and remains within the AWS network. Not configuring model customization prevents any customer data from being incorporated into model weights.
A healthcare company is implementing a RAG-based medical document search system using Amazon Bedrock. The system must retrieve relevant passages from 50,000 medical PDFs and generate responses. The company requires sub-second retrieval latency and wants to minimize operational overhead. Which implementation approach BEST meets these requirements?
Amazon Bedrock Knowledge Bases with OpenSearch Serverless provides fully managed vector storage with sub-second query performance, native integration with Bedrock models, and supports hybrid search (combining semantic vector search with keyword matching) which improves retrieval accuracy for domain-specific terms. This is the recommended AWS-native approach with minimal operational overhead.
A healthcare company is deploying an AI system to assist radiologists with analyzing medical images. The system must maintain detailed records of all AI-generated recommendations for regulatory compliance and enable investigation of any diagnostic errors. Which AWS service combination BEST meets these requirements?
Amazon Bedrock's model invocation logging captures complete request/response pairs including prompts, model outputs, and metadata. Combined with CloudTrail Lake, this provides queryable audit trails specifically designed for AI model interactions, meeting both compliance and investigative requirements for healthcare AI systems.
A company is running a generative AI application on Amazon Bedrock that processes customer support tickets. The application uses Claude 3 Sonnet and costs have increased to $15,000/month due to processing 500,000 tickets with an average of 2,000 input tokens and 500 output tokens per request. The company needs to reduce costs by at least 40% while maintaining response quality. Which solution provides the MOST cost-effective optimization?
Prompt caching in Amazon Bedrock can reduce costs by 90% for cached input tokens and 10% for cache writes. With 2,000 input tokens per request where system prompts and examples might be 1,200 tokens, caching these common patterns across 500,000 requests provides massive savings. Claude pricing: input tokens cost significantly more than cached token reads, making this the highest ROI optimization that maintains quality.
A company is testing a generative AI application that summarizes customer support tickets. During validation, testers notice that 15% of responses contain information not present in the source tickets. The model occasionally references ticket numbers that don't exist and invents customer names. What is the BEST approach to reduce this behavior?
RAG with source attribution directly addresses hallucination by grounding responses in retrieved documents. Post-processing verification catches any remaining fabricated entities. This combines prevention (RAG) with detection (verification).
A healthcare organization is evaluating foundation models for clinical documentation. They need a model that excels at understanding medical terminology, can process 180,000-token context windows for complete patient histories, and supports structured JSON output for integration with their EHR system. Which model best meets these requirements?
Claude 3.5 Sonnet provides a 200,000-token context window (exceeding the 180,000 requirement), has strong performance on medical and technical content, and supports structured JSON output through its native formatting capabilities.
A financial services application uses Amazon Bedrock with Claude to analyze transaction data and generate fraud alerts. The application processes sensitive PII and must ensure that no customer data is used to train foundation models. Compliance requires audit logs showing that data was not retained. What configuration satisfies these requirements?
Amazon Bedrock does not use customer prompts or model outputs to train foundation models by default, but the optOut parameter provides explicit configuration control. AWS CloudTrail with data event logging captures all Bedrock API calls including request parameters (with sensitive data optionally excluded via event selectors), providing the audit trail compliance requires. This is the correct AWS-native approach.
A financial services company uses Amazon Bedrock to generate investment advice summaries. During testing, the model occasionally produces recommendations that conflict with regulatory guidelines. What is the MOST effective approach to prevent harmful outputs before they reach end users?
Amazon Bedrock Guardrails provide native, real-time content filtering at the model inference layer. Custom policies can define denied topics (specific regulatory violations), apply content filters for harmful categories, and block outputs before they're generated. This is purpose-built for AI safety and operates at the correct point in the inference pipeline.
An AI startup is monitoring their generative AI application built on Amazon Bedrock. They notice that 15% of requests are being throttled with ProvisionedThroughputExceededException errors during peak hours (9 AM - 5 PM EST), while off-peak usage is only 30% of provisioned capacity. The application uses Provisioned Throughput with 2 model units of Claude 3 Sonnet. What is the MOST operationally efficient solution to eliminate throttling while optimizing costs?
Hybrid approach (Provisioned + On-Demand) is the most cost-effective for variable workloads. Provisioned Throughput handles base load (85% of requests) at lower per-token cost with guaranteed capacity. On-Demand with exponential backoff handles the 15% burst traffic during peaks, paying slightly more per-token only when needed. This avoids paying for unused provisioned capacity during off-peak hours (70% waste currently) while eliminating throttling.
An AI application using Amazon Bedrock's Claude model fails with a 'ModelTimeoutException' error during load testing. The application processes legal documents averaging 45,000 tokens and generates 8,000-token summaries. Peak load is 50 concurrent requests. Which action will MOST effectively resolve this issue?
Chunking with parallel processing reduces per-request time and avoids model timeout limits (typically 30-120 seconds for large contexts). Step Functions orchestrates parallel execution efficiently. This addresses both timeout and throughput issues.
A company is building a chatbot using Amazon Bedrock and needs to implement data classification and filtering to prevent exposure of sensitive information in model responses. The solution must block SSNs, credit card numbers, and email addresses in real-time before responses reach users. Which approach provides this capability with the least operational overhead?
Bedrock Guardrails provides native, real-time PII detection and redaction integrated directly into the inference pipeline. It supports SSN, credit card, email detection with configurable actions (block or anonymize) without requiring separate services or custom code.
A company is building a multi-agent system where specialized agents handle different tasks: research, analysis, and report generation. The orchestrator agent must dynamically route requests to the appropriate specialist agent based on user intent. Which Amazon Bedrock feature should be used to implement this routing logic?
Amazon Bedrock Flows provides visual workflow orchestration with conditional branching, loops, and parallel processing. The Condition node can evaluate intent classification results and dynamically route to different flow paths (specialist agents). This is the purpose-built solution for multi-agent orchestration with dynamic routing.
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How much does the AIP-C01 exam cost?
The exam fee is approximately $300 and varies by region — confirm current pricing with the certification vendor before you book.
What topics are on the exam?
It covers 5 domains: Foundation Model Integration, Data Management, and Compliance (31%), Implementation and Integration (26%), AI Safety, Security, and Governance (20%), Operational Efficiency and Optimization for GenAI Applications (12%), Testing, Validation, and Troubleshooting (11%). The full course has a dedicated chapter, lab and practice-test coverage for each.
Is this practice test really free?
Yes — all questions on this page are free with explanations and no sign-up. The paid Udemy course adds two full-length timed exams, video lessons and hands-on labs.
How do I get the discount?
Use code FREETEST33 at checkout for $17.99 (list $54.99) through August 22 — the enroll button applies it automatically.
Will this prepare me for the real exam?
The questions mirror the real exam's style and are mapped to the official domains. This is exam-focused preparation — combine the free test with the full course's timed simulations to gauge your readiness.