AIP-C01 Practice Questions

A.  

Deploy an AWS Lambda function that is exposed by a private Amazon API Gateway REST API to a VPC in eu-north-1. Create a VPC endpoint for the selected FM in eu-central-1 and eu-west-3. Extend existing SCPs to allow employees to use the FM. Integrate the REST API with the business application.

B.  

Deploy the FM on Amazon EC2 instances in eu-north-1. Deploy a private Amazon API Gateway REST API in front of the EC2 instances. Configure an Amazon Bedrock VPC endpoint. Integrate the REST API with the business application.

C.  

Configure the FM to use cross-Region inference through a Europe-scoped endpoint. Configure an Amazon Bedrock VPC endpoint. Extend existing SCPs to allow employees to use the FM through inference profiles in Europe-based Regions where the FM is available. Use an inference profile to integrate Amazon Bedrock with the business application.

D.  

Deploy the FM in Amazon SageMaker in eu-north-1. Configure a SageMaker VPC endpoint. Extend existing SCPs to allow employees to use the SageMaker endpoint. Integrate the FM in SageMaker with the business application.

A.  

Launch an Amazon MemoryDB cluster and configure the index by using the Flat algorithm. Configure a horizontal scaling policy based on performance metrics.

B.  

Launch an Amazon MemoryDB cluster and configure the index by using the Hierarchical Navigable Small World (HNSW) algorithm. Configure a vertical scaling policy based on performance metrics.

C.  

Launch an Amazon Aurora PostgreSQL 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.

D.  

Launch an Amazon DocumentDB cluster that has an IVFFlat index and a high probe value. Configure connections to the cluster as a replica set. Distribute reads to replica instances.

A.  

Import the datasets to an Amazon S3 bucket. Provide appropriate IAM permissions and cross-origin resource sharing (CORS) permissions to give the evaluation jobs access to the datasets.

B.  

Import the datasets to an Amazon S3 bucket. Provide appropriate IAM permissions and a VPC endpoint configuration to give the evaluation jobs access to the datasets.

C.  

Configure an AWS Lambda function to create model evaluation jobs on a schedule in the Amazon Bedrock console. Provide the URI of the S3 bucket that contains the datasets as an input. Configure the evaluation jobs to measure the real world knowledge (RWK) score for text generation and BERTScore for summarization. Configure a second Lambda function to check the status of the jobs and publish custom logs to Amazon CloudWatch. Create a custom A

D.  

Use Amazon SageMaker Clarify on a schedule to create model evaluation jobs. Use open source frameworks to create and run standardized evaluations. Publish results to Amazon CloudWatch namespaces. Use an AWS Lambda function to check the status of the jobs and publish custom logs to Amazon CloudWatch. Create a custom Amazon CloudWatch Logs Insights dashboard.

E.  

Run an Amazon SageMaker AI notebook job on a schedule by using the fmvelos or ragas framework to run evaluations that use the datasets in the S3 bucket. Write Python code in the notebook that makes direct InvokeModel API calls to the FMs and processes their responses for evaluation. Publish job status and results to Amazon CloudWatch Logs to measure the real world knowledge (RWK) score for text generation and toxicity for summarization as m

A.  

Use Amazon Macie to detect and redact PII in user inputs and in the model responses. Apply prompt engineering techniques to force the model to avoid investment advice topics. Use AWS CloudTrail to capture conversation logs.

B.  

Use an AWS Lambda function and Amazon Comprehend to detect and redact PII. Use Amazon Comprehend topic modeling to prevent the AI assistant from discussing investment advice topics. Set up custom metrics in Amazon CloudWatch to capture customer conversations.

C.  

Configure Amazon Bedrock guardrails to apply a sensitive information policy to detect and filter PII. Set up a topic policy to ensure that the AI assistant avoids investment advice topics. Use the Converse API to log model invocations. Enable delivery and image logging to Amazon S3.

D.  

Use regex controls to match patterns for PII. Apply prompt engineering techniques to avoid returning PII or investment advice topics to customers. Enable model invocation logging, delivery logging, and image logging to Amazon S3.

A.  

Increase grounding within Amazon Bedrock Guardrails. Enable automated reasoning checks. Set up provisioned throughput.

B.  

Use prompt engineering to restrict model responses to relevant products. Use streaming inference to reduce perceived latency.

C.  

Create an Amazon Bedrock Knowledge Bases and implement Retrieval Augmented Generation (RAG). Set the PerformanceConfigLatency parameter to optimized.

D.  

Store product catalog data in Amazon OpenSearch Service. Validate model recommendations against the catalog. Use Amazon DynamoDB for response caching.

A.  

Enable the Enhanced Monitoring tool. Use an Amazon CloudWatch alarm to filter traffic to the platform. Use Amazon Comprehend PII detection to pre-process the data. Create a CloudWatch alarm to monitor for Amazon Comprehend PII detection events. Create an AWS Step Functions workflow that includes an Amazon Rekognition image moderation step.

B.  

Use an Amazon API Gateway HTTP API with request validation templates to screen content before storing the uploaded content in Amazon S3. Use Amazon SageMaker AI to build custom content moderation models that process content before sending the processed content to Amazon Bedrock.

C.  

Create an Amazon Cognito user pool that uses pre-authentication AWS Lambda functions to run content moderation checks. Use Amazon Textract to filter text content and Amazon Rekognition to filter image content before allowing users to upload content to the platform.

D.  

Create an AWS Step Functions workflow that uses built-in Amazon Bedrock guardrails to filter content. Use Amazon Comprehend PII detection to pre-process the content. Use Amazon Rekognition image moderation.

A.  

Deploy an AWS Lambda function that uses environment variables to store routing rules and Amazon Bedrock FM IDs. Use the Lambda console to update the environment variables when business requirements change. Configure an Amazon API Gateway REST API to read request parameters to make routing decisions.

B.  

Deploy Amazon API Gateway REST API request transformation templates to implement routing logic based on request attributes. Store Amazon Bedrock FM endpoints as REST API stage variables. Update the variables when the system switches between models.

C.  

Configure an AWS Lambda function to fetch routing configuration from the AWS AppConfig Agent for each user request. Run business logic in the Lambda function to select the appropriate FM for each request. Expose the FM through a single Amazon API Gateway REST API endpoint.

D.  

Use AWS Lambda authorizers for an Amazon API Gateway REST API to evaluate routing rules that are stored in AWS AppConfig. Return authorization contexts based on business logic. Route requests to model-specific Lambda functions for each Amazon Bedrock FM.

A.  

Configure fixed-size chunking at 4,000 tokens for each chunk with 20% overlap. Use application-level logic to link multiple chunks sequentially until the FM ' s maximum context window of 200,000 tokens is reached before making inference calls.

B.  

Use hierarchical chunking with parent chunks of 8,000 tokens and child chunks of 2,000 tokens. Use Amazon Bedrock Knowledge Bases built-in retrieval to automatically select relevant parent chunks based on query context. Configure overlap tokens to maintain semantic continuity.

C.  

Use semantic chunking with a breakpoint percentile threshold of 95% and a buffer size of 3 sentences. Use the RetrieveAndGenerate API to dynamically select the most relevant chunks based on embedding similarity scores.

D.  

Create a pre-processing AWS Lambda function that analyzes document token count by using the FM ' s tokenizer. Configure the Lambda function to split documents into equal segments that fit within 80% of the context window. Configure the Lambda function to process each segment independently before aggregating the results.

A.  

Configure Amazon CloudWatch Container Insights for the application infrastructure. Set up CloudWatch alarms for latency thresholds. Add custom metrics for token counts by using the CloudWatch embedded metric format. Create CloudWatch dashboards to visualize the data.

B.  

Implement AWS X-Ray to trace requests through the application components. Enable CloudWatch Logs Insights for error pattern detection. Set up AWS CloudTrail to monitor all API calls to Amazon Bedrock. Create custom dashboards in Amazon QuickSight.

C.  

Enable Amazon CloudWatch Application Insights for the application resources. Create custom metrics for recommendation quality, token usage, and response latency by using the CloudWatch embedded metric format with dimensions for request types and user segments. Configure CloudWatch anomaly detection on the model metrics. Establish log pattern analysis by using CloudWatch Logs Insights.

D.  

Use Amazon OpenSearch Service with the Observability plugin. Ingest model metrics and logs by using Amazon Kinesis. Create custom Piped Processing Language (PPL) queries to analyze model behavior patterns. Establish operational dashboards to visualize anomalies in real time.

A.  

Use AWS Amplify AI Kit to implement streaming responses from the GraphQL API and to optimize client-side rendering.

B.  

Increase the timeout value of the Lambda resolver. Implement retry logic with exponential backoff.

C.  

Update the application to send an API request to an Amazon SQS queue. Update the AWS AppSync resolver to poll and process the queue.

D.  

Change the RetrieveAndGenerate API to the InvokeModelWithResponseStream API. Update the application to use an Amazon API Gateway WebSocket API to support the streaming response.