AIP-C01 Practice Questions

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.  

Store the active model provider in AWS Systems Manager Parameter Store. Configure a Lambda function to read the parameter at runtime to determine which model to invoke.

B.  

Store the active model provider in AWS AppConfig. Configure a Lambda function to read the configuration at runtime to determine which model to invoke.

C.  

Configure an Amazon API Gateway REST API to route requests to separate Lambda functions. Hardcode each Lambda function to a specific model provider. Switch the integration target manually.

D.  

Store the active model provider in a JSON file hosted on Amazon S3. Use AWS AppConfig to reference the S3 file as a hosted configuration source. Configure a Lambda function to read the file through AppConfig at runtime to determine which model to invoke.

A.  

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 Configure the Lambda function to delete outdated documents and invoke knowledge base syncing.

B.  

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.

C.  

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.

D.  

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.

A.  

Use Amazon Comprehend to analyze and classify RAG responses and to extract medical entities and relationships. Use AWS Step Functions to orchestrate automated evaluations. Configure Amazon CloudWatch metrics to track entity recognition confidence scores. Configure CloudWatch to send an alert when accuracy falls below specified thresholds.

B.  

Implement automated large language model (LLM)-based evaluations that use a specialized model that is fine-tuned for medical content to assess all responses. Deploy AWS Lambda functions to parallelize evaluations. Publish results to Amazon CloudWatch metrics that track relevance and factual accuracy.

C.  

Configure Amazon CloudWatch Synthetics to generate test queries that have known answers on a regular schedule, and track model success rates. Set up dashboards that compare synthetic test results against expected outcomes.

D.  

Deploy a hybrid evaluation system that uses an automated LLM-as-a-judge evaluation to initially screen responses and targeted human reviews for edge cases. Use a built-in Amazon Bedrock evaluation to track retrieval precision and hallucination rates.

A.  

Create an IAM permissions boundary for each employee's IAM role. Configure the permissions boundary to require an approved Amazon Bedrock guardrail identifier to invoke Amazon Bedrock models. Create an SCP that allows employees to use only approved models.

B.  

Create an SCP that allows employees to use only approved models. Configure the SCP to require employees to specify a guardrail identifier in calls to invoke an approved model.

C.  

Create an SCP that prevents an employee from invoking a model if a centrally deployed guardrail identifier is not specified in a call to the model. Create a permissions boundary on each employee's IAM role that allows each employee to invoke only approved models.

D.  

Use AWS CloudFormation to create a custom Amazon Bedrock guardrail that has a block filtering policy. Use stack sets to deploy the guardrail to each account in the organization.

E.  

Use AWS CloudFormation to create a custom Amazon Bedrock guardrail that has a mask filtering policy. Use stack sets to deploy the guardrail to each account in the organization.

A.  

Configure VPC endpoints for Amazon Bedrock model API calls. Implement Amazon Bedrock guardrails to filter harmful or unauthorized content in prompts and responses. Use Amazon Bedrock trace events to track all agent and model invocations for auditing purposes. Export the traces to Amazon CloudWatch Logs as an audit record of model usage. Store all prompts and outputs in Amazon S3 with server-side encryption with AWS KMS keys (SSE-KMS).

B.  

Define access control by using IAM with attribute-based access control (ABAC) to map departments to specific permissions. Configure VPC endpoints for Amazon Bedrock model API calls. Use IAM condition keys to enforce specific GuardrailIdentifier and ModelId values. Configure AWS CloudTrail to capture management and data events for S3 objects and KMS key usage activities. Enable S3 server access logging to record detailed file-level interacti

C.  

Restrict access to services by using VPC endpoint policies. Use AWS Config to track resource changes and compliance with security rules. Use server-side encryption with AWS KMS keys (SSE-KMS) to encrypt data at rest. Store the model’s I/O in separate Amazon S3 buckets. Enable S3 server access logging to track file-level interactions.

D.  

Configure AWS CloudTrail Insights to analyze API call patterns across accounts and detect anomalous activity in Amazon Bedrock, Amazon Rekognition, Amazon S3, and AWS KMS. Deploy Amazon Macie to scan and classify the video archive. Use server-side encryption with AWS KMS keys (SSE-KMS) to encrypt all stored data. Configure CloudTrail to capture KMS API usage events for audit purposes. Configure Amazon EventBridge rules to process CloudTrail

A.  

Implement zero-shot chain-of-thought (CoT) instructions that require step-by-step reasoning with explicit fact verification before the model generates each summary.

B.  

Use Retrieval Augmented Generation (RAG) with an Amazon Bedrock knowledge base. Apply semantic chunking and tuned embeddings to ground summaries in source content.

C.  

Configure Amazon Bedrock guardrails to block any generated output that matches patterns that are associated with hallucinated content.

D.  

Increase the temperature parameter in Amazon Bedrock.

E.  

Prompt the Amazon Bedrock model to summarize each full document in one pass.

A.  

Combine the sample prompts into a single JSON document. Create an Amazon Bedrock knowledge base with the document. Write a prompt that asks the FM to generate a response to each sample prompt. Use the RetrieveAndGenerate API to generate a report for each model.

B.  

Combine the sample prompts into a single JSONL document. Store the document in an Amazon S3 bucket. Create an Amazon Bedrock evaluation job that uses a judge model. Specify the S3 location as input and a different S3 location as output. Run an evaluation job for each FM and select the FM as the generator.

C.  

Combine the sample prompts into a single JSONL document. Store the document in an Amazon S3 bucket. Create an Amazon Bedrock evaluation job that uses a judge model. Specify the S3 location as input and Amazon QuickSight as output. Run an evaluation job for each FM and select the FM as the evaluator.

D.  

Combine the sample prompts into a single JSON document. Create an Amazon Bedrock knowledge base from the document. Create an Amazon Bedrock evaluation job that uses the retrieval and response generation evaluation type. Specify an Amazon S3 bucket as the output. Run an evaluation job for each FM.

A.  

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.

B.  

Use Amazon Bedrock Knowledge Bases to generate embeddings. Store the embeddings as a vector store in Amazon DynamoD

B.  

Create an AWS Lambda function that queries the knowledge base. Configure Amazon API Gateway to invoke the Lambda function when handling user requests.

C.  

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.

D.  

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.

A.  

Deploy separate Amazon Bedrock instances in North American and European Regions. Use a custom routing layer that directs traffic based on user location. Configure Amazon CloudWatch alarms to monitor Regional service usage. Use Amazon SNS to send email alerts to the company when usage approaches specified thresholds.

B.  

Use Amazon Bedrock cross-Region inference profiles by specifying geographical codes in profile IDs when the application calls the InvokeModel API. Configure separate Amazon API Gateway HTTP APIs to direct European and North American users to the appropriate Regional endpoints.

C.  

Deploy a multi-Region Amazon API Gateway HTTP API and AWS Lambda functions that implement retry logic to handle throttling. Configure the Lambda functions to call the foundation model in the nearest secondary Region when the application reaches service quotas in the primary Region. Use intelligent routing to ensure compliance with data residency requirements.

D.  

Configure provisioned throughput for Amazon Bedrock in multiple Regions. Implement failover logic in the application code to switch between Regions when throttling occurs. Use AWS Global Accelerator to route traffic to the appropriate endpoints based on user location.