Free AWS Certified Generative AI Developer - Professional Practice Questions

A.  

Use Amazon Bedrock Prompt Management to specify the required inputs as variables. Select an Amazon Nova Pro FM. Specify the output format for the response. Add the prompt to the prompts node of the flow.

B.  

Create an Amazon Bedrock guardrail that applies the hate content filter. Set the filter response to block. Add the guardrail to the prompts node of the flow.

C.  

Create an Amazon Bedrock prompt router. Specify an Amazon Nova Pro FM. Add the required inputs as variables to the input node of the flow. Add the prompt router to the prompts node. Add the output format to the output node.

D.  

Create an Amazon Bedrock guardrail that applies the insults content filter. Set the filter response to detect. Add the guardrail to the prompts node of the flow.

E.  

Create an Amazon Bedrock application inference profile that specifies an Amazon Nova Pro FM. Specify the output format for the response in the description. Include a tag for each of the input variables. Add the profile to the prompts node of the flow.

A.  

Use Amazon SageMaker Data Wrangler to create a data flow. Configure Amazon CloudWatch metrics and alarms to monitor data quality. Use a custom AWS Lambda function to pre-process the data. Load processed data into Amazon Bedrock.

B.  

Set up an AWS Glue crawler to catalog data sources. Create AWS Glue ETL jobs to run custom transformation scripts. Use AWS Glue Data Quality to validate and monitor data quality. Load processed data into Amazon Bedrock.

C.  

Use Amazon Comprehend to extract entities. Create an AWS Lambda function to chunk text. Run Amazon Athena to query and validate data quality. Load processed data into Amazon Bedrock.

D.  

Create an AWS Step Functions workflow to orchestrate data pre-processing tasks. Run custom code on Amazon EC2 instances. Use Amazon SageMaker Model Monitor to monitor data quality. Load processed data into Amazon Bedrock.

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.  

Use AgentCore with AWS Step Functions to define static workflows that call APIs. Configure Amazon CloudWatch Logs to collect logs for compliance reviews.

B.  

Use AgentCore to integrate reasoning modules with dynamic tool invocation APIs. Configure AgentCore agents to orchestrate enterprise APIs based on semantic context and to log reasoning traces for compliance reviews.

C.  

Use Amazon Bedrock Knowledge Bases to implement semantic search instead of enterprise APIs. Integrate AWS Lambda functions with an AgentCore agent to call APIs to orchestrate runtimes.

D.  

Use Amazon Bedrock Guardrails with an external LLM chain to manage semantic context and to collect logs for compliance reviews. Invoke APIs directly from the application.

A.  

Create a distributed traffic simulation framework that sends translation-heavy workloads to the assistant in multiple languages simultaneously. Use Amazon CloudWatch metrics to monitor latency, concurrency, and throughput. Run simulations before production releases to identify infrastructure bottlenecks.

B.  

Deploy the assistant in multiple AWS Regions with Amazon Route 53 latency-based routing and AWS Global Accelerator to improve global performance. Store multilingual conversation logs in Amazon S3. Perform weekly post-deployment audits to review consistency.

C.  

Create a pre-processing pipeline that normalizes all incoming messages into a consistent format before sending the messages to the assistant. Apply rule-based checks to flag potential hallucinations in the outputs. Focus evaluation on normalized text to simplify testing across languages.

D.  

Set up standardized multilingual test conversations with identical meaning. Run the test conversations in parallel by using Amazon Bedrock model evaluation jobs. Apply similarity and hallucination thresholds. Integrate the process into the CI/CD pipeline to block releases that fail.

A.  

In the application account, create interface VPC endpoints for Amazon Bedrock runtimes. Run Lambda functions in private subnets. Use IAM conditions on inference and data-plane policies to allow calls only to approved endpoints and roles. In the data storage account, use AWS Lake Formation LF-tag-based access control to create table-level and column-level cross-account grants.

B.  

Run Lambda functions in private subnets. Configure a NAT gateway to provide access to Amazon Bedrock and the data lake. Use S3 bucket policies and ACLs to manage permissions. Export AWS CloudTrail logs to Amazon S3 to perform weekly reviews.

C.  

Create a gateway endpoint only for Amazon S3 in the application account. Invoke Amazon Bedrock through public endpoints. Use database-level grants in AWS Lake Formation to manage data access. Stream AWS CloudTrail logs to Amazon CloudWatch Logs. Do not set up metric filters or alarms.

D.  

Use VPC endpoints to provide access to Amazon Bedrock and Amazon S3 in the application account. Use only IAM path-based policies to manage data lake access. Send AWS CloudTrail logs to Amazon CloudWatch Logs. Periodically create dashboards and allow public fallback for cross-Region reads to reduce setup time.

A.  

Implement a chunking strategy that divides the customer data into smaller segments. Configure the model to process each segment separately. Invoke the model a final time to synthesize the individual responses into comprehensive recommendations.

B.  

Modify the prompt structure to place the most critical information at the beginning and end of the context window. Implement token-counting logic to truncate less important data when the interaction approaches the model’s maximum context length.

C.  

Replace Claude Sonnet with a model that has a larger context-window capacity. Increase the Lambda function timeout to accommodate longer processing times for larger inputs.

D.  

Configure the recommendation system to use the Converse API. Modify the additionalModelRequestFields parameter to increase the maximum token limit beyond the model’s default context-window size.

E.  

Implement RAG by using a knowledge base to index the customer data with vector embeddings. Retrieve only the most semantically relevant information for each recommendation request based on the current customer context.

A.  

Configure Amazon Bedrock guardrails with content filters set to high to protect against prompt injection attacks. Use a guardrail profile to implement cross-Region guardrail inference. Use Amazon CloudWatch Logs with custom metrics to capture detailed guardrail intervention events.

B.  

Configure Amazon Bedrock guardrails with content filters set to high. Use AWS WAF to block suspicious inputs. Use AWS CloudTrail to log API calls.

C.  

Deploy Amazon Comprehend custom classifiers to detect prompt injection attacks. Use Amazon API Gateway request validation. Use CloudWatch Logs to capture intervention events.

D.  

Configure Amazon Bedrock guardrails with custom content filters and word filters set to high. Configure cross-Region guardrail replication for failover. Store logs in AWS CloudTrail for compliance auditing.