GenAI Professional AIP-C01 Practice Questions & Answers • ExamPrepare

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Question 1

A media company must use Amazon Bedrock to implement a robust governance process for AI-generated content. The company needs to manage hundreds of prompt templates. Multiple teams use the templates across multiple AWS Regions to generate content. The solution must provide version control with approval workflows that include notifications for pending reviews. The solution must also provide detailed audit trails that document prompt activities and consistent prompt parameterization to enforce quality standards.

Which solution will meet these requirements?

Configure Amazon Bedrock Studio prompt templates. Use Amazon CloudWatch to create dashboards that display prompt usage metrics. Store the approval status of content in Amazon DynamoDB. Use AWS Lambda functions to enforce approvals.
Use Amazon Bedrock Prompt Management to implement version control. Configure AWS CloudTrail for audit logging. Use IAM policies to control approval permissions. Create parameterized prompt templates by specifying variables.
Use AWS Step Functions to create an approval workflow. Store prompts as documents in Amazon S3. Use tags to implement version control. Use Amazon EventBridge to send notifications.
Deploy Amazon SageMaker Canvas with prompt templates that are stored in Amazon S3. Use AWS CloudFormation to implement version control. Use AWS Config to enforce approval policies.

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Question 2

An AI company develops an application that allows students to summarize PDF files. The company exposes the application through Amazon API Gateway. The application uses an AWS Lambda function to extract and transform user requests and to query an Amazon OpenSearch Service index to find relevant chunks. The company hosts the model that the application uses on an Amazon SageMaker AI endpoint. The application experiences heavy usage during each day. At night, the application has lower traffic and receives a burst workload.

The company observes that the application has high latency during peak usage hours. The company needs a solution to reduce the application latency under peak load.

Which solution will meet these requirements with the LEAST operational overhead?

Replace the SageMaker AI endpoint with a Lambda function that has higher memory and timeout settings. Deploy a smaller model to the function. Use Amazon SQS to queue excessive user requests.
Replace the SageMaker AI endpoint with a call to an Amazon Bedrock model. Implement automatic scaling for Amazon OpenSearch Service. Use Amazon CloudWatch to monitor latency.
Migrate the SageMaker AI endpoint to a large Amazon EC2 dedicated instance that uses an optimized GPU configuration.
Implement a caching system at the API Gateway level to handle user requests.

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Question 3

A company is using Amazon Bedrock to build a customer-facing AI assistant to handle sensitive customer inquiries. The company must use defense-in-depth safety controls to block sophisticated prompt injection attacks. The company must keep audit logs of all safety interventions. The AI assistant must have cross-Region failover capabilities.

Which solution will meet these requirements?

Configure Amazon Bedrock guardrails to use content filters to protect against prompt injection attacks. Set the content filters to high. Use a guardrail profile to implement cross-Region guardrail inference. Use Amazon CloudWatch Logs with custom metrics to capture detailed guardrail intervention events.
Configure Amazon Bedrock guardrails to use content filters to protect against prompt injection attacks. Set the content filters to high. Use AWS WAF to block suspicious inputs. Use AWS CloudTrail to log API calls for audits.
Deploy Amazon Comprehend custom classification to detect prompt injection attacks. Use Amazon API Gateway to validate requests. Use Amazon CloudWatch Logs with custom metrics to capture detailed intervention events.
Configure Amazon Bedrock guardrails to use custom content filters to protect against harmful content. Set the content filters to high. Use word filters to protect against known attack patterns. Configure cross-Region guardrail replication to provide failover capabilities. Store logs in AWS CloudTrail for compliance auditing.

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Question 4

A healthcare company is developing an Amazon Bedrock based AI clinical assistant. The company needs a solution to track model inputs and outputs so the company can perform offline quality evaluation and regression analysis. The solution must provide centralized, immutable audit records of all model inference and decision events. The company needs a solution that requires minimal runtime overhead so interactive latency remains stable during evaluation and auditing.

Which solution will meet these requirements?

Use Amazon SageMaker Model Registry to track model versions. Use Amazon Macie to classify artifacts. Write inference outputs to Amazon CloudWatch Logs. Manually perform monthly quality reviews.
Use AWS CloudTrail to record API calls. Store logs of API calls in an Amazon S3 bucket that has versioning enabled. Use AWS AppConfig feature flags to perform simple online splits without offline evaluation.
Send logs for Amazon Bedrock model invocations to Amazon CloudWatch Logs and Amazon S3. Enable AWS CloudTrail for Amazon Bedrock and Amazon S3 data events. Store logs in an S3 bucket that has Object Lock enabled. Run Amazon Bedrock model evaluations offline.
Capture dataset lineage in AWS Glue Data Catalog. Store inference payloads in standard Amazon S3 buckets. Use a judge large language model (LLM) to score responses in production for each request.

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Question 5

A financial services company is creating a Retrieval Augmented Generation (RAG) application that uses Amazon Bedrock to generate summaries of market activities. The application relies on a vector database that stores a small proprietary dataset that has a low index count. The application must perform similarity searches. The Amazon Bedrock model's responses must maximize accuracy and maintain high performance.

The company needs to configure the vector database and integrate it with the application.

Which solution will meet these requirements?

Launch an Amazon MemoryDB cluster and configure the index by using the Flat algorithm. Configure a horizontal scaling policy based on performance metrics.
Launch an Amazon MemoryDB cluster and configure the index by using the Hierarchical Navigable Small World (HNSW) algorithm. Configure a vertical policy based on performance metrics.
Launch an Amazon Aurora PostgresSQL cluster and configure the index by using the Inverted File with Flat Compression (IVFFlat) algorithm. Configure the instance class to scale to a larger size when the load increases.
Launch an Amazon DocumentDB cluster that has an Inverted File with Flat Compression (IVFFlat) index and a high probe value. Configure connections to the cluster as a replica set. Distribute reads to replica instances.

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Question 6

A global research company is building a RAG-enabled AI system that uses Amazon Bedrock Knowledge Bases. The company stores documents in Amazon S3 and indexes the documents into an Amazon OpenSearch Serverless vector collection.

When the company evaluates the system, the company identifies three issues. Queries return outdated documents when users request only recent research. Medical research queries return both medical research documents and engineering domain documents. Users can retrieve documents that were authored by researchers who the users should not have access to based on company policies.

The company wants to improve the system so that retrieval becomes more precise and contextually appropriate.

Which solution will meet these requirements?

Add custom metadata fields to the documents in Amazon S3 to record timestamp, authorship, and research domain. Index the document embeddings and the custom metadata fields into the OpenSearch Serverless vector collection. At query time, use the knowledge base to run vector similarity search and return the stored metadata with the results to help the model interpret document relevance.
Use S3 object metadata to store each document’s timestamp. Use a custom metadata field to record authorship. Use S3 object tags to record the research domain. Propagate the metadata fields into the OpenSearch Serverless vector collection as filterable attributes. Use Knowledge Bases to apply timestamp, author, and domain filters before running vector similarity search.
Store documents in Amazon S3. Extract timestamp, author metadata, and research‑domain tags, and store the data in an Amazon DynamoDB table. During retrieval, apply author, domain, and timestamp filters in DynamoDB to identify candidate document IDs. Use the filtered document IDs to narrow the vector similarity search in the OpenSearch Serverless collection.
Store documents in Amazon S3 with custom metadata to record authorship. Use Amazon Comprehend to classify each document into a research domain. Store the classification results in Amazon Aurora. Query Aurora during retrieval to identify relevant domains before performing vector similarity search in the OpenSearch Serverless vector collection.

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