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| Section | Weight | Objectives |
|---|---|---|
| AI Safety, Security, and Governance | 20% | - Guardrails and responsible AI implementation - Security controls for GenAI applications |
| Operational Efficiency and Optimization for GenAI Applications | 12% | - Performance optimization - Cost optimization and token efficiency |
| Testing, Validation, and Troubleshooting | 11% | - Model evaluation and testing - Troubleshooting GenAI systems - Monitoring and observability |
| Foundation Model Integration, Data Management, and Compliance | 31% | - Compliance and responsible AI practices - Foundation model integration and usage - Data management and RAG architectures |
| Implementation and Integration | 26% | - Embedding FMs into applications and workflows - Prompt engineering and prompt management - Agent-based AI systems |
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NEW QUESTION # 107
A large ecommerce company has deployed a foundation model (FM) to generate product descriptions. The company ' s engineering team monitors technical metrics such as token usage, latency, and error rates by using Amazon CloudWatch. The company ' s marketing team tracks business metrics such as conversion rates and revenue impact in its own systems. The company needs a unified observability solution that correlates technical performance with business outcomes. The solution must provide automatic alerts to stakeholders when operational metrics indicate degradation. The solution must provide comprehensive visibility across both technical and business metrics. Which solution will meet these requirements?
Answer: C
NEW QUESTION # 108
A global financial services company hosts a fraud-alert system that uses an Amazon Bedrock foundation model (FM) to generate explanations for suspicious transactions. The company processes regulated financial data across three geographic areas. The system must maintain consistent responsiveness globally, support multi-Region failover, and provide full observability for audit and compliance teams.
Load testing shows that the FM's total inference time cannot be reduced. The company cannot increase its inference costs, change the FM, modify token counts, or provision additional compute capacity. Users report that the UI performs slowly because it waits for the complete model response before it shows any content.
The company must improve perceived responsiveness during peak periods, when the system can receive
10,000-15,000 concurrent requests. The solution must maintain multi-Region resiliency and full monitoring visibility.
Which solution will meet these requirements?
Answer: C
Explanation:
Option A is correct because the problem is not total model runtime; the problem is perceived UI responsiveness. AWS documentation states that the Amazon Bedrock InvokeModelWithResponseStream API returns the model response as a stream. AWS also explains that streaming allows an application to access responses in chunks without waiting for the entire result. This lets the frontend display generated content as tokens or chunks arrive, improving the user experience even when the total inference duration remains unchanged.
This option also preserves the stated constraints. It does not require changing the foundation model, increasing token counts, provisioning extra compute, or reducing the model's actual inference time. Instead, it changes how the response is delivered to the user. This is exactly the right pattern when users perceive slowness because the interface waits for the full completion before rendering anything. AWS examples for Bedrock streaming show applications processing response streams in real time, which supports interactive applications during high-concurrency periods.
The monitoring portion of option A is also aligned with AWS guidance. Amazon Bedrock runtime publishes CloudWatch metrics for invocation volume, latency, token consumption, error rates, and model invocation logging delivery. CloudWatch GenAI observability can track invocation count, token usage, and errors using built-in views. This supports audit and compliance visibility across Regions.
Option B adds caching of partially processed fraud explanations, which can be risky for regulated transaction- specific explanations and does not solve the UI issue for unique requests. Option C changes inputs by condensing them, which violates the constraint not to modify token counts or inputs. Option D focuses on Lambda concurrency and log retention, which does not address waiting for the full model response.
Therefore, response streaming with CloudWatch and tracing is the correct solution.
NEW QUESTION # 109
Example Corp provides a personalized video generation service that millions of enterprise customers use.
Customers generate marketing videos by submitting prompts to the company's proprietary generative AI (GenAI) model. To improve output relevance and personalization, Example Corp wants to enhance the prompts by using customer-specific context such as product preferences, customer attributes, and business history.
The customers have strict data governance requirements. The customers must retain full ownership and control over their own data. The customers do not require real-time access. However, semantic accuracy must be high and retrieval latency must remain low to support customer experience use cases.
Example Corp wants to minimize architectural complexity in its integration pattern. Example Corp does not want to deploy and manage services in each customer's environment unless necessary.
Which solution will meet these requirements?
Answer: D
Explanation:
Option A is the correct solution because Amazon Q Business is explicitly designed to provide secure, governed access to enterprise data while preserving customer ownership and control. Each customer maintains their own Amazon Q Business index, which ensures that data never leaves the customer's control boundary unless explicitly shared through approved access mechanisms.
By designating Example Corp as a data accessor, customers can allow controlled, auditable access to their indexed content through secure APIs. This model satisfies strict data governance requirements, including data ownership, access transparency, and revocation capability. Customers do not need to expose raw data or deploy infrastructure in Example Corp's environment.
Amazon Q Business provides high semantic accuracy through managed indexing, ranking, and retrieval optimizations. Because real-time access is not required, this approach avoids the complexity and latency challenges of live federated retrieval while still delivering fast query performance suitable for customer experience use cases.
Option B introduces unnecessary operational complexity by requiring real-time MCP servers per customer.
Option C requires customers to manage Amazon Bedrock knowledge bases and enable cross-account access, which increases integration complexity and governance risk. Option D requires shared Amazon Kendra indexes across accounts, which complicates access control and data ownership boundaries.
Therefore, Option A provides the cleanest, lowest-overhead architecture that meets data governance, accuracy, performance, and scalability requirements while minimizing operational burden for both Example Corp and its customers.
NEW QUESTION # 110
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: C,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 # 111
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: A,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 # 112
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