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Amazon AWS Certified Generative AI Developer - Professional Sample Questions (Q40-Q45):

NEW QUESTION # 40
A book publishing company wants to build a book recommendation system that uses an AI assistant. The AI assistant will use ML to generate a list of recommended books from the company's book catalog. The system must suggest books based on conversations with customers.
The company stores the text of the books, customers' and editors' reviews of the books, and extracted book metadata in Amazon S3. The system must support low-latency responses and scale efficiently to handle more than 10,000 concurrent users.
Which solution will meet these requirements?

A. Use Amazon SageMaker AI to deploy a pre-trained model to build a personalized recommendation engine for books. Deploy the model as a SageMaker AI endpoint. Invoke the model endpoint by using Amazon API Gateway.
B. Use Amazon Bedrock Knowledge Bases to generate embeddings. Store the embeddings as a vector store in Amazon OpenSearch Service. Create an AWS Lambda function that queries the knowledge base. Configure Amazon API Gateway to invoke the Lambda function when handling user requests.
C. Create an Amazon Kendra GenAI Enterprise Edition index that uses the S3 connector to index the book catalog data stored in Amazon S3. Configure built-in FAQ in the Kendra index. Develop an AWS Lambda function that queries the Kendra index based on user conversations. Deploy Amazon API Gateway to expose this functionality and invoke the Lambda function.
D. Use Amazon Bedrock Knowledge Bases to generate embeddings. Store the embeddings as a vector store in Amazon DynamoDB. Create an AWS Lambda function that queries the knowledge base.
Configure Amazon API Gateway to invoke the Lambda function when handling user requests.

Answer: B

Explanation:
Option A best meets the requirements because it directly implements a Retrieval Augmented Generation pattern for conversational recommendations using managed Amazon Bedrock capabilities and a scalable vector store. The company's source data already resides in Amazon S3, which aligns naturally with Amazon Bedrock Knowledge Bases ingestion workflows. A knowledge base can ingest book text, reviews, and metadata, generate embeddings using a supported embedding model, and persist those vectors in a purpose- built vector backend such as Amazon OpenSearch Service. This enables semantic retrieval that is well suited to conversation-driven intent, where user prompts are often descriptive and do not map cleanly to keyword filters.
The requirement to suggest books based on conversations implies the system must interpret natural language context and retrieve relevant passages, reviews, and metadata to ground the recommendation. Knowledge Bases provide managed orchestration for embedding creation and retrieval, which reduces development effort compared to building custom embedding pipelines. OpenSearch Service provides scalable vector search and k- nearest neighbors style similarity retrieval, which supports low-latency responses when properly indexed and sized.
For scaling to more than 10,000 concurrent users, the API layer design in option A is a common AWS pattern: Amazon API Gateway provides a managed front door with throttling and request handling, while AWS Lambda scales horizontally with demand and can invoke the knowledge base retrieval operations. This separates compute scaling from the vector store scaling and helps keep latency predictable under load.
Option B is not the best choice because DynamoDB is not the standard native vector store target for Amazon Bedrock Knowledge Bases in this context and would introduce additional implementation complexity around vector indexing and similarity search behavior. Option C requires substantial ML lifecycle work, model hosting, tuning, and continuous iteration to achieve quality recommendations at scale. Option D provides strong enterprise search, but it focuses on retrieval and FAQs rather than a managed RAG recommendation workflow grounded in embeddings and conversational context for generative responses.

NEW QUESTION # 41
A medical device company wants to feed reports of medical procedures that used the company's devices into an AI assistant. To protect patient privacy, the AI assistant must expose patient personally identifiable information (PII) only to surgeons. The AI assistant must redact PII for engineers. The AI assistant must reference only medical reports that are less than 3 years old.
The company stores reports in an Amazon S3 bucket as soon as each report is published. The company has already set up an Amazon Bedrock Knowledge Bases. The AI assistant uses Amazon Cognito to authenticate users.
Which solution will meet these requirements?

A. Create a second knowledge base. Use Lambda and Amazon Comprehend to redact PII before syncing to the second knowledge base. Route users to the appropriate knowledge base based on Cognito group membership.
B. Enable Amazon Macie PII detection on the S3 bucket. Use an S3 trigger to invoke an AWS Lambda function that redacts PII from the reports. Configure the Lambda function to delete outdated documents and invoke knowledge base syncing.
C. Invoke an AWS Lambda function to sync the S3 bucket and the knowledge base when a new report is uploaded. Use a second Lambda function with Amazon Comprehend to redact PII for engineers. Use S3 Lifecycle rules to remove reports older than 3 years.
D. Set up an S3 Lifecycle configuration to remove reports that are older than 3 years. Schedule an AWS Lambda function to run daily syncs between the bucket and the knowledge base. When users interact with the AI assistant, apply a guardrail configuration selected based on the user's Cognito user group to redact PII from responses when required.

Answer: D

Explanation:
Option C is the correct solution because it enforces privacy controls at inference time, not at ingestion time, which is required when different user roles require different visibility into the same underlying data.
Using an S3 Lifecycle configuration ensures that documents older than 3 years are automatically removed, guaranteeing that the knowledge base references only compliant, recent medical reports. Scheduling Lambda- based syncs keeps the knowledge base aligned with the bucket contents without introducing complex per- upload orchestration.
The most important requirement is role-based PII exposure. Amazon Bedrock guardrails support dynamic application at inference time, allowing the system to select a guardrail configuration based on the authenticated user's Amazon Cognito group. Surgeons can receive full responses, while engineers receive responses with PII masked-without duplicating data or maintaining multiple knowledge bases.
This approach preserves a single source of truth for medical reports while enforcing privacy through response- level controls. It also maintains full auditability of access and redaction behavior.
Option A permanently removes PII and violates surgeon access requirements. Option B redacts data inconsistently and couples privacy logic to ingestion. Option D doubles storage, increases cost, and introduces data drift risk.
Therefore, Option C best meets privacy, compliance, scalability, and operational efficiency requirements.

NEW QUESTION # 42
A financial services company uses an AI application to process financial documents by using Amazon Bedrock. During business hours, the application handles approximately 10,000 requests each hour, which requires consistent throughput.
The company uses the CreateProvisionedModelThroughput API to purchase provisioned throughput. Amazon CloudWatch metrics show that the provisioned capacity is unused while on-demand requests are being throttled. The company finds the following code in the application:
response = bedrock_runtime.invoke_model(
modelId="anthropic.claude-v2",
body=json.dumps(payload)
)
The company needs the application to use the provisioned throughput and to resolve the throttling issues.
Which solution will meet these requirements?

A. Increase the number of model units (MUs) in the provisioned throughput configuration.
B. Replace the model ID parameter with the ARN of the provisioned model that the CreateProvisionedModelThroughput API returns.
C. Add exponential backoff retry logic to handle throttling exceptions during peak hours.
D. Modify the application to use the invokeModelWithResponseStream API instead of the invokeModel API.

Answer: B

Explanation:
Option B is the correct solution because Amazon Bedrock provisioned throughput is only used when the application explicitly invokes the provisioned model ARN, not the base foundation model ID. In the provided code, the application is calling the standard model identifier (anthropic.claude-v2), which routes requests to on-demand capacity instead of the purchased provisioned throughput.
When the CreateProvisionedModelThroughput API is used, Amazon Bedrock returns a provisioned model ARN that represents the reserved capacity. Applications must reference this ARN in the modelId parameter when invoking the model. If the base model ID is used instead, Bedrock treats the request as on-demand traffic, which explains why CloudWatch metrics show unused provisioned capacity alongside throttled on- demand requests.
Option A would increase capacity but would not fix the root cause because the application is not using the provisioned resource at all. Option C adds resiliency but does not ensure usage of provisioned throughput and would still incur throttling. Option D changes the response delivery mechanism but does not affect capacity routing.
Therefore, Option B directly resolves the throttling issue by correctly routing traffic to the reserved capacity and ensures that the company benefits from the provisioned throughput it has purchased.

NEW QUESTION # 43
A company is building a generative AI (GenAI) application that processes financial reports and provides summaries for analysts. The application must run two compute environments. In one environment, AWS Lambda functions must use the Python SDK to analyze reports on demand. In the second environment, Amazon EKS containers must use the JavaScript SDK to batch process multiple reports on a schedule. The application must maintain conversational context throughout multi-turn interactions, use the same foundation model (FM) across environments, and ensure consistent authentication.
Which solution will meet these requirements?

A. Use the Amazon Bedrock Converse API directly in both environments with a common authentication mechanism that uses IAM roles. Store conversation states in Amazon ElastiCache. Create programming language-specific wrappers for model parameters.
B. Create a centralized Amazon API Gateway REST API endpoint that handles all model interactions by using the InvokeModel API. Store interaction history in application process memory in each Lambda function or EKS container. Use environment variables to configure model parameters.
C. Use the Amazon Bedrock Converse API and IAM roles for authentication. Pass previous messages in the request messages array to maintain conversational context. Use programming language-specific SDKs to establish consistent API interfaces.
D. Use the Amazon Bedrock InvokeModel API with a separate authentication method for each environment. Store conversation states in Amazon DynamoDB. Use custom I/O formatting logic for each programming language.

Answer: C

Explanation:
Option D is the correct solution because the Amazon Bedrock Converse API is purpose-built for multi-turn conversational interactions and is designed to work consistently across SDKs and compute environments. The Converse API standardizes how messages, roles, and context are represented, which ensures consistent behavior whether the application is running in AWS Lambda with Python or in Amazon EKS with JavaScript.
By passing previous messages in the messages array, the application explicitly maintains conversational context across turns without relying on external state stores. This approach is recommended by AWS for conversational GenAI workflows because it avoids state synchronization complexity and ensures deterministic model behavior across environments.
Using IAM roles for authentication provides a single, consistent security model for both Lambda and EKS.
IAM roles integrate natively with AWS SDKs, eliminating the need for custom authentication logic or environment-specific credentials. This aligns with AWS best practices for least privilege and simplifies governance.
Option A introduces inconsistent authentication and custom formatting logic, increasing complexity. Option B unnecessarily introduces ElastiCache for state management, which is not required when using the Converse API correctly. Option C stores state in process memory, which is unsafe and unreliable for serverless and containerized workloads.
Therefore, Option D best satisfies the requirements for conversational consistency, multi-environment support, shared model usage, and consistent authentication with minimal operational overhead.

NEW QUESTION # 44
A company has a recommendation system running on Amazon EC2 instances. The applications make API calls to Amazon Bedrock foundation models (FMs) to analyze customer behavior and generate personalized product recommendations.
The system experiences intermittent issues where some recommendations do not match customer preferences.
The company needs an observability solution to monitor operational metrics and detect patterns of performance degradation compared to established baselines. The solution must generate alerts with correlation data within 10 minutes when FM behavior deviates from expected patterns.
Which solution will meet these requirements?

A. Implement AWS X-Ray. Enable CloudWatch Logs Insights. Set up AWS CloudTrail and create dashboards in Amazon QuickSight.
B. Configure Amazon CloudWatch Container Insights. Set up alarms for latency thresholds. Add custom token metrics using the CloudWatch embedded metric format.
C. Use Amazon OpenSearch Service with the Observability plugin. Ingest metrics and logs through Amazon Kinesis and analyze behavior with custom queries.
D. Enable Amazon CloudWatch Application Insights. Create custom metrics for recommendation quality, token usage, and response latency using the CloudWatch embedded metric format with dimensions for request types and user segments. Configure CloudWatch anomaly detection on model metrics. Use CloudWatch Logs Insights for pattern analysis.

Answer: D

Explanation:
Option C best satisfies the requirement for rapid, correlated detection of model-related performance degradation. Amazon CloudWatch Application Insights provides automated observability across application components running on Amazon EC2, identifying abnormal behavior patterns without requiring extensive manual configuration.
Using custom metrics for recommendation quality, token usage, and response latency allows the company to directly monitor FM behavior, not just infrastructure health. Applying dimensions such as request type and user segment enables fine-grained correlation between performance issues and specific customer interactions or workloads.
CloudWatch anomaly detection is critical because it establishes dynamic baselines from historical data and detects deviations automatically. This enables alerts to be generated within minutes when FM behavior changes unexpectedly, satisfying the 10-minute alerting requirement without static thresholds that can miss subtle degradations.
CloudWatch Logs Insights complements metrics by enabling rapid analysis of log patterns, error messages, or unusual request flows associated with degraded recommendations. Because all data remains within CloudWatch, correlation between metrics, logs, and alerts is straightforward and operationally efficient.
Option A focuses on infrastructure metrics and lacks behavioral baselining. Option B provides tracing but not automated anomaly detection. Option D adds significant operational overhead and ingestion complexity for a use case already well supported by CloudWatch-native features.
Therefore, Option C delivers the most effective, scalable, and low-overhead observability solution for detecting FM-related performance deviations.

NEW QUESTION # 45
......

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