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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Testing, Validation, and Troubleshooting | 11% | - Troubleshooting GenAI systems - Monitoring and observability - Model evaluation and testing |
| Topic 2: Implementation and Integration | 26% | - Embedding FMs into applications and workflows - Agent-based AI systems - Prompt engineering and prompt management |
| Topic 3: Operational Efficiency and Optimization for GenAI Applications | 12% | - Cost optimization and token efficiency - Performance optimization |
| Topic 4: AI Safety, Security, and Governance | 20% | - Security controls for GenAI applications - Guardrails and responsible AI implementation |
| Topic 5: Foundation Model Integration, Data Management, and Compliance | 31% | - Foundation model integration and usage - Data management and RAG architectures - Compliance and responsible AI practices |
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NEW QUESTION # 88
An ecommerce company is using an Anthropic Claude Sonnet model in Amazon Bedrock to generate product recommendations. An AWS Lambda function retrieves customer purchase data from Amazon DynamoDB, product reviews from Amazon S3, and customer profile information from Amazon RDS. Then the function sends the data directly to the Amazon Bedrock model through API calls. Recently, customers who have extensive purchase histories have begun to receive incomplete recommendations.
Amazon CloudWatch logs for the Lambda function show execution timeouts. CloudWatch logs for Amazon Bedrock API calls show intermittent errors. The company reviews the logs and finds that some requests are failing with context-length-exceeded errors. Other requests finish but appear to ignore portions of the input data.
The company wants the recommendation system to consider all customer data when the system generates recommendations. The company wants to use Amazon Bedrock Knowledge Bases to improve data organization and retrieval.
Which combination of solutions will meet these requirements? (Select TWO.)
Answer: A,E
Explanation:
Options A and E are correct because the failure pattern is caused by exceeding the model's usable context window and by sending too much raw customer data directly to the FM. Amazon Bedrock Knowledge Bases follows the RAG pattern by splitting source content into manageable chunks, converting chunks into embeddings, storing them in a vector index, and using semantic similarity to retrieve relevant content for a user query. This supports option E because the application can index customer purchases, profiles, and review- related information, then retrieve only the most relevant records for the current recommendation request instead of stuffing all raw data into one prompt.
Option A is also correct because chunking and staged synthesis are standard ways to handle inputs that exceed an FM's context capacity. Processing smaller segments avoids context-length-exceeded errors, and a final synthesis call can combine intermediate findings into a complete recommendation. This is especially useful when the company says it wants the system to consider all customer data, not merely truncate older or less important data. Amazon Bedrock Knowledge Bases also lets teams configure how content is chunked for storage and retrieval, which supports a structured approach rather than an oversized one-shot prompt.
Option B is not sufficient because truncating "less important" data conflicts with the requirement to consider all customer data. Option C violates the implied design direction because the company wants to use Knowledge Bases, and simply choosing a larger context model does not solve long-term growth of customer histories. Option D is technically incorrect: model parameters such as max_tokens control output generation limits, not the model's maximum context window, and different Claude models have model-specific maximum values. You cannot use additionalModelRequestFields to bypass the FM's context limit.
NEW QUESTION # 89
A software company is using Amazon Q Business to build an AI assistant that allows employees to access company information and personal information by using natural language prompts. The company stores this information in an Amazon S3 bucket.
Each department in the company has a dedicated prefix in the S3 bucket. Each object name includes the S3 prefix of the department that it belongs to. Each department can belong to only a single group in AWS IAM Identity Center. Each employee belongs to a single department.
The company configures Amazon Q Business to access data stored in an S3 bucket as a data source. The company needs to ensure that the AI assistant respects access controls based on the user's IAM Identity Center group membership.
Which solution will meet this requirement with the LEAST operational overhead?
Answer: A
Explanation:
Option B is the correct solution because Amazon Q Business natively supports access control lists (ACLs) for S3 data sources using a single, centralized JSON file that maps S3 prefixes to IAM Identity Center groups.
This approach directly aligns with the company's data organization model, where each department's data is stored under a distinct S3 prefix and each employee belongs to exactly one department group.
Using a single acl.json file at the bucket root minimizes operational overhead by centralizing access control logic in one location. Administrators can update department mappings without touching individual folders or changing IAM permissions, which simplifies governance and reduces the risk of configuration drift. Amazon Q Business automatically evaluates the user's IAM Identity Center group membership at query time and filters accessible documents accordingly.
Option A increases operational complexity by requiring a separate ACL file in every department folder, which becomes difficult to maintain as departments or prefixes change. Option C attempts to enforce access using IAM permissions sets, but Amazon Q Business access control for S3 data sources is not designed to be managed through IAM condition logic and would significantly increase complexity. Option D introduces a custom metadata structure that is not the supported mechanism for Amazon Q Business access enforcement.
Therefore, Option B provides the cleanest, most scalable, and AWS-recommended solution for enforcing department-based access control with the least operational effort.
NEW QUESTION # 90
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: B,C
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 # 91
A company is using Amazon Bedrock to build an AI assistant to help internal teams analyze unstructured customer feedback data. The company stores the customer feedback in an Amazon S3 bucket. The S3 bucket contains more than 25 TB of historical data from mobile app reviews, chat conversations, and call center transcripts. The company expects the data source to grow by 3 GB every day. The data entries often contain multiple unrelated topics within the same input.
The company needs a solution that reliably delivers accurate answers to questions based on the data source.
The solution must not export any personally identifiable information (PII) to the Amazon Bedrock model during processing or response generation.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: B
Explanation:
Option A is the only listed choice that satisfies the hard requirement that PII must not reach the Bedrock model. AWS explicitly states that Knowledge Base guardrails apply to the user input and generated model response, but not to references retrieved from the Knowledge Base at runtime. Therefore, Options B, C, and D can still place PII-bearing retrieved chunks into model context even though a guardrail is configured. Amazon Comprehend can detect PII, enabling the Lambda preprocessing step in A to remove sensitive data before embeddings are created and before retrieved context is ever sent to the foundation model. Although semantic chunking would normally be preferable for records containing multiple unrelated topics, privacy is a mandatory constraint. Option A has more custom processing, but it is the only option that enforces PII removal upstream of model invocation.
NEW QUESTION # 92
A healthcare company is developing a generative AI (GenAI) application that recommends patient treatment plans to physicians. The company wants to use Amazon Bedrock to build the application.
The application must document model limitations, prevent unauthorized clinical recommendations, and maintain detailed audit trails of all AI-generated outputs. The company must store outputs in compliance with healthcare regulations. The solution must prevent output tampering.
Which solution will meet these requirements?
Answer: C
Explanation:
Option A addresses all three governance layers. Model cards provide documented information about a model' s capabilities, limitations, supported modalities, context constraints, and other characteristics that teams can review before deployment. Amazon Bedrock Guardrails can enforce application-specific safeguards on user inputs and model responses, and IAM policies can require a specific guardrail for inference calls, helping prevent uncontrolled model use. Storing generated outputs in Amazon S3 provides durable audit storage, while S3 Object Lock uses a write-once-read-many model that can prevent retained objects from being overwritten or deleted and is specifically intended to support regulatory retention requirements. Option B lacks formal model-limit documentation and relies on custom postprocessing. Option C does not protect stored audit evidence against tampering. Option D relies primarily on prompts and ordinary database storage rather than enforceable safety and immutable-retention controls. AWS Documentation
NEW QUESTION # 93
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