AIP-C01 Free Practice Test

A medical device company wants to feed reports of medical procedures that used the company??s devices into an AI assistant. To protect patient privacy, the AI assistant must expose patient personally identifiable information (PII) only to surgeons. The AI assistant must redact PII for engineers. The AI assistant must reference only medical reports that are less than 3 years old.
The company stores reports in an Amazon S3 bucket as soon as each report is published. The company has already set up an Amazon Bedrock Knowledge Bases. The AI assistant uses Amazon Cognito to authenticate users.
Which solution will meet these requirements?

1. A. Enable Amazon Macie PII detection on the S3 bucke
2. B. Use an S3 trigger to invoke an AWS Lambda function that redacts PII from the report
3. C. Configure the Lambda function to delete outdated documents and invoke knowledge base syncing.
4. D. Invoke an AWS Lambda function to sync the S3 bucket and the knowledge base when a new report is uploade
5. E. Use a second Lambda function with Amazon Comprehend to redact PII for engineer
6. F. Use S3 Lifecycle rules to remove reports older than 3 years.
7. G. Set up an S3 Lifecycle configuration to remove reports that are older than 3 year
8. H. Schedule an AWS Lambda function to run daily syncs between the bucket and the knowledge bas
9. I. 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.
10. J. Create a second knowledge bas
11. K. Use Lambda and Amazon Comprehend to redact PII before syncing to the second knowledge bas
12. L. Route users to the appropriate knowledge base based on Cognito group membership.

Correct Answer: C

A company provides a service that helps users from around the world discover new restaurants. The service has 50 million monthly active users. The company wants to implement a semantic search solution across a database that contains 20 million restaurants and 200 million reviews. The company currently stores the data in a PostgreSQL database.
The solution must support complex natural language queries and return results for at least 95% of queries within 500 ms. The solution must maintain data freshness for restaurant details that update hourly. The solution must also scale cost-effectively during peak usage periods.
Which solution will meet these requirements with the LEAST development effort?

1. A. Migrate the restaurant data to Amazon OpenSearch Servic
2. B. Implement keyword-based search rules that use custom analyzers and relevance tuning to find restaurants based on attributes such as cuisine type, feature, and locatio
3. C. Create Amazon API Gateway HTTP API endpoints to transform user queries into structured search parameters.
4. D. Migrate the restaurant data to Amazon OpenSearch Servic
5. E. Use a foundation model (FM) in Amazon Bedrock to generate vector embeddings from restaurant descriptions, reviews, and menu item
6. F. When users submit natural language queries, convert the queries to embeddings by using the same F
7. G. Perform k-nearest neighbors (k-NN) searches to find semantically similar results.
8. H. Keep the restaurant data in PostgreSQL and implement a pgvector extensio
9. I. Use a foundation model (FM) in Amazon Bedrock to generate vector embeddings from restaurant dat
10. J. Store the vector embeddings directly in PostgreSQ
11. K. Create an AWS Lambda function to convert natural language queries to vector representations by using the same F
12. L. Configure the Lambda function to perform similarity searches within the database.
13. M. Migrate the restaurant data to an Amazon Bedrock knowledge base by using a custom ingestion pipelin
14. N. Configure the knowledge base to automatically generate embeddings from restaurant informatio
15. O. Use the Amazon Bedrock Retrieve API with built-in vector search capabilities to query the knowledge base directly by using natural language input.

Correct Answer: D

A specialty coffee company has a mobile app that generates personalized coffee roast profiles by using Amazon Bedrock with a three-stage prompt chain. The prompt chain converts user inputs into structured metadata, retrieves relevant logs for coffee roasts, and generates a personalized roast recommendation for each customer.
Users in multiple AWS Regions report inconsistent roast recommendations for identical inputs, slow inference during the retrieval step, and unsafe recommendations such as brewing at excessively high temperatures. The company must improve the stability of outputs for repeated inputs. The company must also improve app performance and the safety of the app??s outputs. The updated solution must ensure 99.5% output consistency for identical inputs and achieve inference latency of less than 1 second. The solution must also block unsafe or hallucinated recommendations by using validated safety controls.
Which solution will meet these requirements?

1. A. Deploy Amazon Bedrock with provisioned throughput to stabilize inference latenc
2. B. Apply Amazon Bedrock guardrails with semantic denial rules to block unsafe output
3. C. Use Amazon Bedrock Prompt Management to manage prompts by using approval workflows.
4. D. Use Amazon Bedrock Agents to manage chainin
5. E. Log model inputs and outputs to Amazon CloudWatch Log
6. F. Use logs from CloudWatch to perform A/B testing for prompt versions.
7. G. Cache prompt results in Amazon ElastiCach
8. H. Use AWS Lambda functions to pre- process metadata and to trace end-to-end latenc
9. I. Use AWS X-Ray to identify and remediate performance bottlenecks.
10. J. Use Amazon Kendra to improve roast log retrieval accurac
11. K. Store normalized prompt metadata within Amazon DynamoD
12. L. Use AWS Step Functions to orchestrate multi-step prompts.

Correct Answer: A

A medical company is building a generative AI (GenAI) application that uses Retrieval Augmented Generation (RAG) to provide evidence-based medical information. The application uses Amazon OpenSearch Service to retrieve vector embeddings. Users report that searches frequently miss results that contain exact medical terms and acronyms and return too many semantically similar but irrelevant documents. The company needs to improve retrieval quality and maintain low end-user latency, even as the document collection grows to millions of documents.
Which solution will meet these requirements with the LEAST operational overhead?

1. A. Configure hybrid search by combining vector similarity with keyword matching to improve semantic understanding and exact term and acronym matching.
2. B. Increase the dimensions of the vector embeddings from 384 to 1536. Use a post- processing AWS Lambda function to filter out irrelevant results after retrieval.
3. C. Replace OpenSearch Service with Amazon Kendr
4. D. Use query expansion to handle medical acronyms and terminology variants during pre-processing.
5. E. Implement a two-stage retrieval architecture in which initial vector search results are re- ranked by an ML model hosted on Amazon SageMaker.

Correct Answer: A

A company is using Amazon Bedrock to develop an AI-powered application that uses a foundation model (FM) that supports cross-Region inference and provisioned throughput. The application must serve users in Europe and North America with consistently low latency. The application must comply with data residency regulations that require European user data to remain within Europe-based AWS Regions.
During testing, the application experiences service degradation when Regional traffic spikes reach service quotas. The company needs a solution that maintains application resilience and minimizes operational complexity.
Which solution will meet these requirements?

1. A. Deploy separate Amazon Bedrock instances in North American and European Region
2. B. Use a custom routing layer that directs traffic based on user locatio
3. C. Configure Amazon CloudWatch alarms to monitor Regional service usag
4. D. Use Amazon SNS to send email alerts when usage approaches thresholds.
5. E. Use Amazon Bedrock cross-Region inference profiles by specifying geographical codes in profile IDs when calling the InvokeModel AP
6. F. Configure separate Amazon API Gateway HTTP APIs to direct European and North American users to the appropriate Regional endpoints.
7. G. Deploy a multi-Region Amazon API Gateway HTTP API and AWS Lambda functions that implement retry logic to handle throttlin
8. H. Configure the Lambda functions to call the FM in the nearest secondary Region when quotas are reached.
9. I. Configure provisioned throughput for Amazon Bedrock in multiple Region
10. J. Implement failover logic in application code to switch Regions when throttling occur
11. K. Use AWS Global Accelerator to route traffic based on user location.

Correct Answer: B