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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Testing, Validation, and Troubleshooting | 11% | - Troubleshoot and resolve issues
|
| Topic 2: Operational Efficiency and Optimization for GenAI Applications | 12% | - Optimize performance and cost
|
| Topic 3: AI Safety, Security, and Governance | 20% | - Secure generative AI systems
|
| Topic 4: Implementation and Integration | 26% | - Implement generative AI features and capabilities
|
| Topic 5: Foundation Model Integration, Data Management, and Compliance | 31% | - Ensure compliance and responsible AI practices
|
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質問 # 134
A company is designing an API for a generative AI (GenAI) application that uses a foundation model (FM) that is hosted on a managed model service. The API must stream responses to reduce latency, enforce token limits to manage compute resource usage, and implement retry logic to handle model timeouts and partial responses.
Which solution will meet these requirements with the LEAST operational overhead?
正解：A
解説：
Option A is the best solution because it satisfies streaming, token control, and retry requirements while keeping operational overhead low by using fully managed, serverless AWS services. Amazon API Gateway HTTP APIs provide a lightweight, cost-effective front door for APIs and integrate cleanly with AWS Lambda for request processing and security controls.
AWS Lambda response streaming allows the API to begin returning content to the client as soon as partial model output is available, reducing perceived latency and improving user experience for long responses.
Using Lambda as the integration layer also provides a centralized place to enforce token-aware request handling, such as rejecting oversized requests, truncating optional context, or applying consistent limits across users and tenants to manage compute usage.
Retry logic is best handled in the client or integration layer for transient failures such as timeouts and throttling. Lambda can implement controlled retries with exponential backoff and jitter, while API Gateway timeouts help bound request lifetimes and prevent hung connections from consuming resources indefinitely.
Because the model service is managed, the company avoids infrastructure management and focuses only on request shaping, safety, and resiliency behavior.
Option B is not suitable because client-side polling is not true streaming, front-end token enforcement is insecure and inconsistent, and API Gateway does not provide model-aware retry behavior on its own. Option C introduces container hosting and scaling complexity, which increases operational overhead compared to serverless. Option D can work, but REST APIs are generally heavier than HTTP APIs for this pattern and do not reduce overhead compared to Option A.
Therefore, Option A provides the required streaming and resiliency capabilities with the least infrastructure management effort.
質問 # 135
An ecommerce company operates a global product recommendation system that needs to switch between multiple foundation models (FMs) in Amazon Bedrock based on regulations, cost optimization, and performance requirements. The company must apply custom controls based on proprietary business logic, including dynamic cost thresholds, AWS Region-specific compliance rules, and real-time A/B testing across multiple FMs.
The system must be able to switch between FMs without deploying new code. The system must route user requests based on complex rules including user tier, transaction value, regulatory zone, and real-time cost metrics that change hourly and require immediate propagation across thousands of concurrent requests.
Which solution will meet these requirements?
正解：B
解説：
Option C cleanly separates rapidly changing routing configuration from application code. AWS AppConfig Agent is the AWS-recommended mechanism for retrieving configuration data and maintains a local cache while asynchronously polling AppConfig for updates. In Lambda environments, the extension periodically refreshes configuration in the background and lets functions retrieve cached values locally with very low latency. The Lambda function can therefore evaluate proprietary rules involving user tier, transaction value, Region, compliance constraints, and current model costs without redeployment. Model identifiers and routing thresholds can change independently through AppConfig while API Gateway exposes one stable application endpoint. Option A requires environment-variable modifications and Lambda configuration updates. Option B makes API Gateway transformation templates responsible for complex business decisions. Option D incorrectly couples model-routing decisions with authorization logic and requires separate model-specific Lambda functions, reducing flexibility. AWS Documentation
質問 # 136
A healthcare company uses Amazon Bedrock to deploy an application that generates summaries of clinical documents. The application experiences inconsistent response quality with occasional factual hallucinations.
Monthly costs exceed the company's projections by 40%. A GenAI developer must implement a near real- time monitoring solution to detect hallucinations, identify abnormal token consumption, and provide early warnings of cost anomalies. The solution must require minimal custom development work and maintenance overhead.
Which solution will meet these requirements?
正解：A
解説：
Option C is the correct solution because it provides near real-time monitoring, hallucination detection, and cost anomaly awareness using built-in Amazon Bedrock and Amazon CloudWatch capabilities, with minimal custom development.
By configuring Amazon Bedrock invocation logging with text output logging, the company captures detailed prompt and response data for auditing and analysis without building custom logging pipelines. This data is stored in Amazon S3, providing durable storage for compliance and retrospective investigation.
Using Amazon Bedrock guardrails with contextual grounding checks allows the application to automatically detect hallucinations by verifying whether generated summaries are grounded in the provided clinical documents. This is the AWS-recommended approach for hallucination detection in RAG and summarization workloads and avoids the need to maintain custom evaluation models or pipelines.
Creating Amazon CloudWatch anomaly detection alarms for InputTokenCount and OutputTokenCount metrics enables automatic detection of abnormal token usage patterns that often correlate with runaway prompts, inefficient summarization, or prompt injection attempts. Anomaly detection adapts dynamically to usage trends, making it more effective than static thresholds for early cost warnings.
Option A introduces batch analytics with Glue and Athena, which is not near real time and increases operational overhead. Option B requires managing evaluation jobs and Lambda-based notification logic.
Option D focuses on infrastructure-level monitoring and offline dashboards rather than near real-time GenAI quality and cost signals.
Therefore, Option C best meets the requirements with the least operational effort and maintenance overhead.
質問 # 137
A financial services company is developing a customer service AI assistant by using Amazon Bedrock. The AI assistant must not discuss investment advice with users. The AI assistant must block harmful content, mask personally identifiable information (PII), and maintain audit trails for compliance reporting. The AI assistant must apply content filtering to both user inputs and model responses based on content sensitivity.
The company requires an Amazon Bedrock guardrail configuration that will effectively enforce policies with minimal false positives. The solution must provide multiple handling strategies for multiple types of sensitive content.
Which solution will meet these requirements?
正解：A
解説：
Option C is the correct solution because it uses a single, well-tuned Amazon Bedrock guardrail that applies different actions to different content types, which is the recommended approach for minimizing false positives while enforcing strong policy controls.
Setting content filters to medium rather than high reduces overblocking of benign customer conversations while still preventing harmful content. Amazon Bedrock guardrails are designed to balance precision and recall, and medium sensitivity is commonly recommended for customer-facing financial services use cases.
Denied topics explicitly prevent the assistant from discussing investment advice, which is a regulatory requirement. Including definitions and sample phrases improves detection accuracy and reduces ambiguity.
Sensitive information filters support different actions per context. Masking PII in responses preserves conversational usefulness for legitimate customer support while preventing exposure of sensitive data.
Blocking sensitive financial information in inputs prevents downstream processing of disallowed content before it reaches the foundation model.
Critically, enabling both input and output evaluation ensures that guardrails are applied consistently at every stage of interaction. Custom blocked messages and audit logging provide clear compliance evidence for regulators and internal audits.
Option A causes excessive false positives by blocking all PII outright. Option B introduces unnecessary complexity and is not how Bedrock guardrails are intended to be applied. Option D uses orchestration logic that Bedrock guardrails already handle natively.
Therefore, Option C best satisfies enforcement, flexibility, auditability, and accuracy requirements.
質問 # 138
A company provides a service that helps users from around the world discover new restaurants. The service has 50 million monthly active users. The company wants to implement a semantic search solution across a database that contains 20 million restaurants and 200 million reviews. The company currently stores the data in PostgreSQL.
The solution must support complex natural language queries and return results for at least 95% of queries within 500 ms. The solution must maintain data freshness for restaurant details that update hourly. The solution must also scale cost-effectively during peak usage periods.
Which solution will meet these requirements with the LEAST development effort?
正解：C
解説：
Option B best satisfies the requirements while minimizing development effort by combining managed semantic search capabilities with fully managed foundation models. AWS Generative AI guidance describes semantic search as a vector-based retrieval pattern where both documents and user queries are embedded into a shared vector space. Similarity search (such as k-nearest neighbors) then retrieves results based on meaning rather than exact keywords.
Amazon OpenSearch Service natively supports vector indexing and k-NN search at scale. This makes it well suited for large datasets such as 20 million restaurants and 200 million reviews while still achieving sub- second latency for the majority of queries. Because OpenSearch is a distributed, managed service, it automatically scales during peak traffic periods and provides cost-effective performance compared with building and tuning custom vector search pipelines on relational databases.
Using Amazon Bedrock to generate embeddings significantly reduces development complexity. AWS manages the foundation models, eliminates the need for custom model hosting, and ensures consistency by using the same FM for both document embeddings and query embeddings. This aligns directly with AWS- recommended semantic search architectures and removes the need for model lifecycle management.
Hourly updates to restaurant data can be handled efficiently through incremental re-indexing in OpenSearch without disrupting query performance. This approach cleanly separates transactional data storage from search workloads, which is a best practice in AWS architectures.
Option A does not meet the semantic search requirement because keyword-based search cannot reliably interpret complex natural language intent. Option C introduces scalability and performance risks by running large-scale vector similarity searches inside PostgreSQL, which increases operational complexity. Option D adds unnecessary ingestion and abstraction layers intended for retrieval-augmented generation, not high- throughput semantic search.
Therefore, Option B provides the optimal balance of performance, scalability, data freshness, and minimal development effort using AWS Generative AI services.
質問 # 139
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