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AWS Certified Generative AI Developer - Professional (AIP-C01)

Prepare for this AWS Professional exam. Try an exam-style question first, then review the exam domains and practice-test format.

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Practice Questions 1

A GenAI developer is building a Retrieval Augmented Generation (RAG)-based customer support application that uses Amazon Bedrock foundation models (FMs). The application needs to process 50 GB of historical customer conversations that are stored in an Amazon S3 bucket as JSON files. The application must use the processed data as its retrieval corpus. The application's data processing workflow must extract relevant data from customer support documents, remove customer personally identifiable information (PII), and generate embeddings for vector storage. The processing workflow must be cost-effective and must finish within 4 hours. Which solution will meet these requirements with the LEAST operational overhead?

Lambda parallel execution can process the data, but the team takes on concurrency tuning, memory sizing, and error handling that Step Functions plus the rest of the managed pipeline avoid. Trap: It looks plausible because it mentions Amazon Comprehend and AWS Lambda. Lambda parallel execution can process the data, but the team takes on concurrency tuning, memory sizing, and error handling that Step Functions plus the rest of the managed pipeline avoid.
Glue ETL plus SageMaker Processing with HuggingFaceProcessor adds two heavier components — Glue jobs to operate and SageMaker Processing jobs to manage — when Comprehend + Bedrock already provide the same primitives natively. Trap: It looks plausible because it mentions Amazon SageMaker Processing and Amazon OpenSearch Service. Glue ETL plus SageMaker Processing with HuggingFaceProcessor adds two heavier components — Glue jobs to operate and SageMaker Processing jobs to manage — when Comprehend + Bedrock already provide the same primitives natively.
An EMR Spark cluster with UDFs is the highest-overhead option: cluster sizing, scaling, and maintenance are all on the team. Aurora PostgreSQL with pgvector also adds an always-on database to operate. Trap: It looks plausible because it mentions Amazon Comprehend and Amazon EMR. An EMR Spark cluster with UDFs is the highest-overhead option: cluster sizing, scaling, and maintenance are all on the team.
AWS Step Functions orchestrates the four-stage pipeline (extract, redact PII with Comprehend, embed with Bedrock, store in OpenSearch Serverless) end to end without standing up clusters or handling concurrency manually. Every component is fully managed: Comprehend handles PII detection at scale, Bedrock generates embeddings, and OpenSearch Serverless stores vectors and serves similarity search. Within the 4-hour budget for 50 GB, the whole flow runs through managed services, which is the LEAST operational overhead path.

Exam Domains

Use the exam weights to decide which domains to study first.

Foundation Model Integration, Data Management, and ComplianceWeight 31%
Implementation and IntegrationWeight 26%
AI Safety, Security, and GovernanceWeight 20%
Operational Efficiency and Optimization for GenAI ApplicationsWeight 12%
Testing, Validation, and TroubleshootingWeight 11%

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