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Grokking the AWS Certified Generative AI Developer - Professional

Free AWS Certified Generative AI Developer Exam Practice

Explore real-world practice questions designed to test your knowledge of AWS generative AI applications including model selection, RAG systems, prompt management, security controls, cost optimization, and agentic AI workflows. This lesson helps you prepare effectively for the AWS Certified Generative AI Developer exam by simulating exam-like conditions and focused scenario-based problems.

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Question 1:

A financial services company is building a real-time GenAI application that analyzes short market alerts and generates risk summaries for traders during market hours. The application processes approximately 25,000 alerts per day, with peak bursts of 1,000 requests per minute. Traders require responses within 250 ms p95 latency, and the company must minimize inference cost while ensuring high factual accuracy and availability across Regions. The solution must allow future flexibility to switch models without application code changes.

Which solution best meets these requirements?

A. Implement Amazon Bedrock model invocation behind AWS Lambda with dynamic model selection configured through AWS AppConfig, enable Cross-Region Inference, and use a lower-cost model by default with automatic fallback to a higher-capability model for complex alerts.

B. Deploy a fine-tuned large language model on Amazon SageMaker AI real-time endpoints using GPU instances and provision capacity for peak traffic to ensure consistent latency.

C. Use Amazon Bedrock with a single high-capability model configured with provisioned throughput sized for peak load and store all generated summaries in Amazon DynamoDB for reuse.

D. Use Amazon Bedrock Knowledge Bases backed by Amazon OpenSearch Service to retrieve historical alerts and generate summaries dynamically for every request.

Question 2:

A global consulting firm is building a RAG-powered knowledge assistant for 3,000 consultants. The system must retrieve relevant content from over 12 million documents across policy manuals, client reports, and research papers stored in Amazon S3. The application must support semantic search, metadata filtering by region and document type, and return top-5 results within 500 ms. The firm wants to minimize operational overhead while ensuring retrieval accuracy at scale.

Which design choices best meet these requirements? (Select any three options.)

A. Organize the vector store into multiple indexes segmented by document domain to reduce search scope and improve query performance at scale.

B. Implement Amazon Aurora PostgreSQL with pgvector and store all embeddings in a single table with region and document type columns.

C. Use Amazon Bedrock Knowledge Bases backed by Amazon OpenSearch Serverless with hybrid search enabled.

D. Store embeddings in Amazon DynamoDB and calculate cosine similarity in AWS Lambda for each query.

E. Apply a hierarchical metadata framework using document type, region, and update timestamp as filterable attributes.

F. Use fixed-size chunking only, regardless of document structure, to simplify ingestion.

Question 3

Sequence - Horizontal
A health care company is building a GenAI assistant that answers clinician questions using medical transcripts and reports. Audio recordings are uploaded daily to Amazon S3, transcribed, quality-validated, enriched with medical entities, and indexed for retrieval. The pipeline must ensure that only validated and enriched text is embedded and stored. The company uses Amazon Bedrock, Amazon Transcribe, AWS Glue, and Amazon Comprehend. The developer should implement the following steps:
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Question 4:

A multinational pharmaceutical company is building an enterprise GenAI research assistant to support drug discovery and regulatory analysis. The system must ingest over 8 million documents from internal research reports, clinical trial summaries, and regulatory submissions stored in Amazon S3 across multiple Regions. Scientists query the system to retrieve grounded answers with citations, and responses must not expose patient PII or unapproved research content.

The company uses Amazon Bedrock for generation, Amazon OpenSearch Service for vector search, and AWS Lambda for orchestration. Regulatory requirements mandate full auditability of prompt usage, prompt versioning with approvals, and traceability of data sources used in responses. The system must support frequent, prompt updates from a central AI governance team without disrupting downstream applications.

Which solution best ensures controlled, prompt updates, auditability, and safe reuse across teams?

A. Store prompts in Amazon S3 with versioned objects and use Lambda environment variables to load prompts at runtime.

B. Use Amazon Bedrock Prompt Management with parameterized templates, approval workflows, and CloudTrail logging.

C. Embed prompts directly in application code repositories and manage changes through CI/CD pipelines.

D. Store prompts in Amazon DynamoDB and track changes using DynamoDB ...