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Amazon AIP-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • AI Safety, Security, and Governance: This domain addresses input
  • output safety controls, data security and privacy protections, compliance mechanisms, and responsible AI principles including transparency and fairness.
Topic 2
  • Operational Efficiency and Optimization for GenAI Applications: This domain encompasses cost optimization strategies, performance tuning for latency and throughput, and implementing comprehensive monitoring systems for GenAI applications.
Topic 3
  • Testing, Validation, and Troubleshooting: This domain covers evaluating foundation model outputs, implementing quality assurance processes, and troubleshooting GenAI-specific issues including prompts, integrations, and retrieval systems.
Topic 4
  • Foundation Model Integration, Data Management, and Compliance: This domain covers designing GenAI architectures, selecting and configuring foundation models, building data pipelines and vector stores, implementing retrieval mechanisms, and establishing prompt engineering governance.
Topic 5
  • Implementation and Integration: This domain focuses on building agentic AI systems, deploying foundation models, integrating GenAI with enterprise systems, implementing FM APIs, and developing applications using AWS tools.

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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q83-Q88):

NEW QUESTION # 83
A GenAI developer is building a RAG system that uses Amazon Bedrock Knowledge Bases. The system needs to process 50 textbooks that are stored in an Amazon S3 bucket. The textbooks are all an average of
500 pages long. The system needs to generate a knowledge base to answer domain-specific customer questions.
During initial testing on a subset of documents, the GenAI developer notices that query responses contain irrelevant information and sometimes miss critical context from the source materials. The GenAI developer must ensure that the solution provides accurate responses with low latency and no hallucinations.
Which solution will meet these requirements?

Answer: B

Explanation:
Option C is correct because the issue is retrieval quality: responses contain irrelevant information and sometimes miss important surrounding context. Amazon Bedrock Knowledge Bases supports hierarchical chunking with parent and child chunks. In this pattern, smaller child chunks improve precise retrieval, while larger parent chunks provide broader surrounding context when returned for generation. AWS documentation states that hierarchical chunking supports two levels: a parent chunk size and a child chunk size, with configurable overlap. This directly addresses the textbook problem because dense technical materials often need both fine-grained matching and enough context to avoid incomplete or misleading answers.
Hybrid search is also the strongest retrieval option for domain-specific textbooks. AWS documentation for Knowledge Bases explains that HYBRID search uses both vector embeddings and raw text, while SEMANTIC search uses only vector embeddings. For technical content, equations, terms, acronyms, product names, and exact phrases may be missed by pure semantic search. Hybrid search reduces this risk by combining semantic similarity with keyword matching. The Knowledge Base vector search configuration also supports controls such as number of source chunks, metadata filters, search type override, and reranking configuration, all of which help tune retrieval quality.
Adjusting the relevance threshold, or filtering low-confidence retrieval results in the application layer, helps reduce hallucinations because the model should not generate answers from weak or unrelated context. The key GenAI principle is that RAG quality depends on retrieving the right evidence before generation. If irrelevant chunks are passed to the FM, the FM can produce irrelevant or unsupported answers.
Option A uses very small fixed chunks, which can miss context across sections. Option B adds caching and feedback loops but does not fix the underlying retrieval issue. Option D's semantic chunking can help topic boundaries, but it does not explicitly solve exact-term retrieval or low-confidence filtering. Therefore, hierarchical chunking plus hybrid search and relevance filtering is the best answer.


NEW QUESTION # 84
A financial services company is using Amazon Bedrock to deploy a GenAI application across multiple business units. The company must ensure that all prompts that are used with the application ' s FMs follow regulatory compliance standards and maintain consistent formatting.
The company must implement a solution that provides version control for prompt templates, requires approval workflows for new prompts, and maintains detailed audit trails of all prompt usage and modifications.
Which combination of solutions will meet these requirements? (Select TWO.)

Answer: A,D

Explanation:
Options A and C provide the most AWS-native governance approach. Amazon Bedrock Prompt Management supports reusable parameterized prompts and explicit prompt versions, allowing teams to preserve approved configurations and deploy stable versions instead of uncontrolled working drafts. An organizational approval gate can therefore be applied before a new prompt version becomes production-approved. AWS CloudTrail records Amazon Bedrock API activity, including the identity making the request, the operation performed, time, source information, and other event details, providing the required audit trail for Bedrock resource modifications and use. CloudWatch can complement this with operational usage monitoring and reporting.
Option B recreates prompt governance through S3, Lambda, and SNS. Option D uses general-purpose configuration storage rather than Bedrock-native prompt management. Option E adds substantial custom state and filtering logic without providing the same managed prompt-version lifecycle. AWS Documentation


NEW QUESTION # 85
A healthcare company is developing an application to process medical queries. The application must answer complex queries with high accuracy by reducing semantic dilution. The application must refer to domain- specific terminology in medical documents to reduce ambiguity in medical terminology. The application must be able to respond to 1,000 queries each minute with response times less than 2 seconds.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: D


NEW QUESTION # 86
An elevator service company has developed an AI assistant application by using Amazon Bedrock. The application generates elevator maintenance recommendations to support the company's elevator technicians.
The company uses Amazon Kinesis Data Streams to collect the elevator sensor data.
New regulatory rules require that a human technician must review all AI-generated recommendations. The company needs to establish human oversight workflows to review and approve AI recommendations. The company must store all human technician review decisions for audit purposes.
Which solution will meet these requirements?

Answer: B


NEW QUESTION # 87
A financial services company is developing an AI-powered search assistant application to help investment advisors quickly retrieve investment data. The application runs as an AWS Lambda function. The company is using Amazon Bedrock to develop the application by using an Amazon Bedrock knowledge base that uses Amazon OpenSearch Serverless as its data source. The application agent must manage collections at scale by automatically assigning access permissions to collections and indexes that match a specific pattern. The company uses Amazon Bedrock tools to test the knowledge base. The knowledge base sync process finishes successfully. However, the test reveals a 400 Bad Authorization error from the BedrockAgentRuntime API and a 403 Forbidden error when the test attempts to access OpenSearch Serverless. The company must resolve the permissions issues. Which combination of solutions will meet this requirement? (Select TWO.)

Answer: C,E

Explanation:
The errors described indicate missing permissions at both the application orchestration and data access levels.
The 400 Bad Authorization from BedrockAgentRuntime indicates the Lambda execution role lacks the identity permission to invoke the agent; adding bedrock:InvokeAgent and aoss:APIAccessAll (which allows the principal to interact with OpenSearch Serverless APIs) is necessary. The 403 Forbidden error from OpenSearch Serverless specifically relates to data-plane permissions. Unlike traditional OpenSearch, Serverless uses data access policies . To " manage collections at scale " automatically, a policy must be created that uses pattern-based resource rules (e.g., matching a prefix), ensuring that as new collections or indexes are created, the required principals (the Lambda role and the Bedrock service role) are granted the necessary access without manual policy updates for every new resource.


NEW QUESTION # 88
......

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