Free AWS Certified Generative AI Developer - Professional Practice Questions

A.  

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.

B.  

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.

C.  

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.

D.  

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 collection.

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 Step Functions to orchestrate a workflow to process the recordings. Configure steps to invoke Amazon Transcribe to convert audio to text, validate job completion, and to invoke an AWS Lambda function to process the text by using Amazon Bedrock FMs to generate structured analysis output.

B.  

Use AWS Step Functions to orchestrate a workflow to process the recordings. Configure steps to invoke Amazon Transcribe to convert audio to text, validate job completion, and to directly invoke Amazon Bedrock FMs to generate summaries and sentiment analysis in JSON format.

C.  

Use AWS Step Functions to orchestrate a workflow to process the recordings. Configure steps to invoke Amazon Transcribe to convert audio to text, validate job completion, and to invoke an AWS Lambda function to create a prompt to invoke Amazon Bedrock FMs to generate structured analysis output.

D.  

Configure the source S3 bucket to send events to Amazon EventBridge. Create an EventBridge rule to invoke the Step Functions workflow when an object is created in the bucket.

E.  

Configure the source S3 bucket to send notifications to the Step Functions workflow when an object is created in the bucket.

A.  

Create a flow in Amazon Bedrock Flows. Configure S3 action nodes at the beginning and end of the flow to retrieve and store the communications and the model responses. In the middle of the flow, configure an expression to parse each communication. Configure an agent step to send the parsed input to the model for review.

B.  

Create an AWS Step Functions Express workflow state machine. Use an Amazon S3 integration GetObject step to retrieve the original communications. Use an intrinsic function Pass step to parse the communications and to pass the results to an Amazon Bedrock InvokeModel step. Configure an Amazon S3 integration PutObject step to store the model responses back to the S3 bucket.

C.  

Create an Amazon Bedrock agent that has an action group. Configure instructions to define how the agent should parse the communications. Configure the action group to retrieve the communications from the S3 bucket, invoke the Amazon Bedrock model, and store the model responses back to the S3 bucket.

D.  

Create an Amazon Bedrock agent that has a single action group. Configure three AWS Lambda functions in the action group. Configure the functions to retrieve the communications from the S3 bucket, parse the communications and invoke the Amazon Bedrock model, and store the model responses back to the S3 bucket.

A.  

Use Amazon Bedrock Knowledge Bases and managed data connectors to ingest content from the source documents. Enable semantic retrieval to augment the FM.

B.  

Index documents from the source documents into Amazon OpenSearch Service by using keyword mappings. Invoke the FM and manually select search results.

C.  

Store the source documents in Amazon S3. Use AWS Lambda functions to generate embeddings. Implement custom retrieval logic in the GenAI assistant application layer.

D.  

Store the source documents in Amazon DynamoD

B.  

A.  

Use parallel processing with asynchronous API calls. Use toxicity detection for offensive content. Use prompt safety classification for inappropriate advice solicitation. Use personally identifiable information (PII) detection without redaction.

B.  

Use custom classification to build an FM that detects offensive content and inappropriate advice solicitation. Apply personally identifiable information (PII) detection as a secondary filter only when messages pass the custom classifier.

C.  

Deploy a multi-stage process. Configure the process to use prompt safety classification first, then toxicity detection on safe prompts only, and finally personally identifiable information (PII) detection in streaming mode. Route flagged messages through Amazon EventBridge for human review.

D.  

Use toxicity detection with thresholds configured to 0.5 for all categories. Use parallel processing for both prompt safety classification and personally identifiable information (PII) detection with entity redaction. Apply Amazon CloudWatch alarms to filter metrics.

A.  

Create AWS Systems Manager Parameter Store parameters for each configuration. Use Lambda functions to poll for parameter updates. Use Amazon EventBridge events to trigger redeployments when configurations change.

B.  

Store model configurations in Amazon DynamoDB tables. Optimize access patterns to retrieve configurations according to customer tier. Configure Lambda functions to query DynamoDB at the beginning of each request to determine which model to use.

C.  

Use AWS AppConfig to manage model configurations. Use feature flags to perform A/B testing. Define JSON schema validation rules for model parameters. Configure Lambda functions to retrieve configurations by using the AWS AppConfig Agent.

D.  

Create an Amazon ElastiCache (Redis OSS) cluster to store model configurations. Set short TTL values. Run custom validation logic in Lambda functions. Use Amazon CloudWatch metrics to monitor configuration usage.

A.  

Configure a cross-Region Amazon Bedrock guardrail to apply sensitive information filters. Set the guardrail to detect mode during development and testing. Switch to block mode for production deployment.

B.  

Configure an Amazon Bedrock guardrail to apply sensitive information filters. Set the guardrail to mask mode during development and testing. Switch to block mode for production deployment. Deploy a copy of the guardrail to each Region where the company operates.

C.  

Configure an Amazon Bedrock guardrail to apply content and topic filters. Set the guardrail to detect mode during development, testing, and production. Disable invocation logging for the Amazon Bedrock model.

D.  

Configure a cross-Region Amazon Bedrock guardrail to apply a set of content and word filters. Set the guardrail to detect mode during development and testing. Switch to mask mode for production deployment.

A.  

Configure an AWS Step Functions workflow that uses Task states to handle each Amazon Bedrock invocation. Configure a Choice state to route claims based on the claim amount.

B.  

Use Amazon Bedrock AgentCore to implement action groups to handle each step. Use agent reasoning to run conditional logic.

C.  

Use Amazon Bedrock Prompt Flows to implement prompt nodes to handle each step. Use a condition node to route claims based on the claim amount.

D.  

Configure AWS Lambda functions to invoke Amazon Bedrock to perform each step sequentially. Include conditional routing in the function code.

A.  

Create a retrieve-only evaluation job that uses a supported version of Anthropic Claude Sonnet as the evaluator model. Configure metrics for context relevance and context coverage. Define deployment thresholds in a separate CI/CD pipeline.

B.  

Create a retrieve-and-generate evaluation job that uses custom precision-at-k metrics and an LLM-as-a-judge metric with a scale of 1–5. Include each chunking strategy in the evaluation dataset. Use a supported version of Anthropic Claude Sonnet to evaluate responses from both FMs.

C.  

Create a separate evaluation job for each chunking strategy and FM combination. Use Amazon Bedrock built-in metrics for correctness and completeness. Manually review scores before deployment approval.

D.  

Set up a pipeline that uses multiple retrieve-only evaluation jobs to assess retrieval quality. Create separate evaluation jobs for both FMs that use Amazon Nova Pro as the LLM-as-a-judge model. Evaluate based on faithfulness and citation precision metrics.