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Amazon Web Services Updated AIP-C01 Exam Questions and Answers by verity

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Amazon Web Services AIP-C01 Exam Overview :

Exam Name: AWS Certified Generative AI Developer - Professional
Exam Code: AIP-C01 Dumps
Vendor: Amazon Web Services Certification: AWS Certified Professional
Questions: 119 Q&A's Shared By: verity
Question 16

A research company is developing a GenAI system to produce summaries of technical documents. The company must catalog all data sources in a central location. The company needs a solution that can automatically discover and update data sources. The solution must tag each generated summary with citations as metadata that users can query. The solution must retain tamper-evident, immutable audit logs for every model invocation and store I/O records. Which solution will meet these requirements?

Options:

A.

Use Amazon Comprehend to identify data sources in the documents. Store generated summaries in Amazon S3 and enable S3 Object Lock. Use Amazon CloudWatch metrics to generate reports about application throughput. Do not include logs for each invocation.

B.

Use AWS Glue Data Catalog with crawlers to maintain data sources. Store generated summaries in Amazon S3. Write object tags that include a source ID. Store Amazon Bedrock model invocation logs in Amazon S3. Enable S3 Object Lock on the S3 bucket that stores invocation logs. Use AWS CloudTrail log file integrity validation to provide tamper-evident immutability.

C.

Store application outputs in Amazon DynamoDB. Apply item-level tags that include source attribution. Write application events to Amazon CloudWatch Logs. Use IAM roles to provide audit traceability.

D.

Use AWS AppConfig feature flags to implement data versioning. Restrict access to the model by using IAM condition keys. Maintain a versioned mapping file of source-to-output relationships in Amazon S3.

Discussion
Question 17

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 PostgreSQL.

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?

Options:

A.

Migrate the restaurant data to Amazon OpenSearch Service. Implement keyword-based search rules that use custom analyzers and relevance tuning to find restaurants based on attributes such as cuisine type, features, and location. Create Amazon API Gateway HTTP API endpoints to transform user queries into structured search parameters.

B.

Migrate the restaurant data to Amazon OpenSearch Service. Use a foundation model (FM) in Amazon Bedrock to generate vector embeddings from restaurant descriptions, reviews, and menu items. When users submit natural language queries, convert the queries to embeddings by using the same FM. Perform k-nearest neighbors (k-NN) searches to find semantically similar results.

C.

Keep the restaurant data in PostgreSQL and implement a pgvector extension. Use a foundation model (FM) in Amazon Bedrock to generate vector embeddings from restaurant data. Store the vector embeddings directly in PostgreSQL. Create an AWS Lambda function to convert natural language queries to vector representations by using the same FM. Configure the Lambda function to perform similarity searches within the database.

D.

Migrate restaurant data to an Amazon Bedrock knowledge base by using a custom ingestion pipeline. Configure the knowledge base to automatically generate embeddings from restaurant information. Use the Amazon Bedrock Retrieve API with built-in vector search capabilities to query the knowledge base directly by using natural language input.

Discussion
Question 18

A medical company is creating a generative AI (GenAI) system by using Amazon Bedrock. The system processes data from various sources and must maintain end-to-end data lineage. The system must also use real-time personally identifiable information (PII) filtering and audit trails to automatically report compliance.

Which solution will meet these requirements?

Options:

A.

Use AWS Glue Data Catalog to register all data sources and track lineage. Use Amazon Bedrock Guardrails PII filters. Enable AWS CloudTrail logging for all Amazon Bedrock API calls with Amazon S3 integration. Use Amazon Macie to scan stored data for sensitive information and publish findings to Amazon CloudWatch Logs. Create CloudWatch dashboards to visualize the findings and generate automated compliance reports.

B.

Use AWS Config to track data source configurations and changes. Use AWS WAF with custom rules to filter PII at the application layer before Amazon Bedrock processes the data. Configure Amazon EventBridge to capture and route audit events to Amazon S3. Use Amazon Comprehend Medical with scheduled AWS Lambda functions to analyze stored outputs for compliance violations.

C.

Use AWS DataSync to replicate data sources to track lineage. Configure Amazon Macie to scan Amazon Bedrock outputs for sensitive information. Use AWS Systems Manager Session Manager to log user interactions. Deploy Amazon Textract with AWS Step Functions workflows to identify and redact PII from generated reports.

D.

Configure Amazon Athena to query data sources to analyze and report on data lineage. Use Amazon CloudWatch custom metrics to monitor PII exposure in Amazon Bedrock responses and establish AWS X-Ray tracing to generate an audit trail. Use an Amazon Rekognition Custom Labels model to detect sensitive information in the data that Amazon Bedrock processes.

Discussion
Question 19

Company configures a landing zone in AWS Control Tower. The company handles sensitive data that must remain within the European Union. The company must use only the eu-central-1 Region. The company uses Service Control Policies (SCPs) to enforce data residency policies. GenAI developers at the company are assigned IAM roles that have full permissions for Amazon Bedrock.

The company must ensure that GenAI developers can use the Amazon Nova Pro model through Amazon Bedrock only by using cross-Region inference (CRI) and only in eu-central-1. The company enables model access for the GenAI developer IAM roles in Amazon Bedrock. However, when a GenAI developer attempts to invoke the model through the Amazon Bedrock Chat/Text playground, the GenAI developer receives the following error:

User arn:aws:sts:123456789012:assumed-role/AssumedDevRole/DevUserName

Action: bedrock:InvokeModelWithResponseStream

On resource(s): arn:aws:bedrock:eu-west-3::foundation-model/amazon.nova-pro-v1:0

Context: a service control policy explicitly denies the action

The company needs a solution to resolve the error. The solution must retain the company ' s existing governance controls and must provide precise access control. The solution must comply with the company ' s existing data residency policies.

Which combination of solutions will meet these requirements? (Select TWO.)

Options:

A.

Add an AdministratorAccess policy to the GenAI developer IAM role

B.

Extend the existing SCPs to enable CRI for the eu.amazon.nova-pro-v1:0 inference profile

C.

Enable Amazon Bedrock model access for Amazon Nova Pro in the eu-west-3 Region

D.

Validate that the GenAI developer IAM roles have permissions to invoke Amazon Nova Pro through the eu.amazon.nova-pro-v1:0 inference profile on all European Union AWS Regions that can serve the model

E.

Extend the existing SCP to enable CRI for the eu-* inference profile

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