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

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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: 107 Q&A's Shared By: addison
Question 8

A company needs a system to automatically generate study materials from multiple content sources. The content sources include document files (PDF files, PowerPoint presentations, and Word documents) and multimedia files (recorded videos). The system must process more than 10,000 content sources daily with peak loads of 500 concurrent uploads. The system must also extract key concepts from document files and multimedia files and create contextually accurate summaries. The generated study materials must support real-time collaboration with version control.

Which solution will meet these requirements?

Options:

A.

Use Amazon Bedrock Data Automation (BDA) with AWS Lambda functions to orchestrate document file processing. Use Amazon Bedrock Knowledge Bases to process all multimedia. Store the content in Amazon DocumentDB with replication. Collaborate by using Amazon SNS topic subscriptions. Track changes by using Amazon Bedrock Agents.

B.

Use Amazon Bedrock Data Automation (BDA) with foundation models (FMs) to process document files. Integrate BDA with Amazon Textract for PDF extraction and with Amazon Transcribe for multimedia files. Store the processed content in Amazon S3 with versioning enabled. Store the metadata in Amazon DynamoDB. Collaborate in real time by using AWS AppSync GraphQL subscriptions and DynamoDB.

C.

Use Amazon Bedrock Data Automation (BDA) with Amazon SageMaker AI endpoints to host content extraction and summarization models. Use Amazon Bedrock Guardrails to extract content from all file types. Store document files in Amazon Neptune for time series analysis. Collaborate by using Amazon Bedrock Chat for real-time messaging.

D.

Use Amazon Bedrock Data Automation (BDA) with AWS Lambda functions to process batches of content files. Fine-tune foundation models (FMs) in Amazon Bedrock to classify documents across all content types. Store the processed data in Amazon ElastiCache (Redis OSS) by using Cluster Mode with sharding. Use Prompt management in Amazon Bedrock for version control.

Discussion
Question 9

A company is building a serverless application that uses AWS Lambda functions to help students around the world summarize notes. The application uses Anthropic Claude through Amazon Bedrock. The company observes that most of the traffic occurs during evenings in each time zone. Users report experiencing throttling errors during peak usage times in their time zones.

The company needs to resolve the throttling issues by ensuring continuous operation of the application. The solution must maintain application performance quality and must not require a fixed hourly cost during low traffic periods.

Which solution will meet these requirements?

Options:

A.

Create custom Amazon CloudWatch metrics to monitor model errors. Set provisioned throughput to a value that is safely higher than the peak traffic observed.

B.

Create custom Amazon CloudWatch metrics to monitor model errors. Set up a failover mechanism to redirect invocations to a backup AWS Region when the errors exceed a specified threshold.

C.

Enable invocation logging in Amazon Bedrock. Monitor key metrics such as Invocations, InputTokenCount, OutputTokenCount, and InvocationThrottles. Distribute traffic across cross-Region inference endpoints.

D.

Enable invocation logging in Amazon Bedrock. Monitor InvocationLatency, InvocationClientErrors, and InvocationServerErrors metrics. Distribute traffic across multiple versions of the same model.

Discussion
Question 10

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?

Options:

A.

Configure hybrid search by combining vector similarity with keyword matching to improve semantic understanding and exact term and acronym matching.

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.

C.

Replace OpenSearch Service with Amazon Kendra. Use query expansion to handle medical acronyms and terminology variants during pre-processing.

D.

Implement a two-stage retrieval architecture in which initial vector search results are re-ranked by an ML model hosted on Amazon SageMaker.

Discussion
Marley
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Isla-Rose Feb 13, 2026
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Joey
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Dexter Feb 16, 2026
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Sarah
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Question 11

A company is building a generative AI (GenAI) application that produces content based on a variety of internal and external data sources. The company wants to ensure that the generated output is fully traceable. The application must support data source registration and enable metadata tagging to attribute content to its original source. The application must also maintain audit logs of data access and usage throughout the pipeline.

Which solution will meet these requirements?

Options:

A.

Use AWS Lake Formation to catalog data sources and control access. Apply metadata tags directly in Amazon S3. Use AWS CloudTrail to monitor API activity.

B.

Use AWS Glue Data Catalog to register and tag data sources. Use Amazon CloudWatch Logs to monitor access patterns and application behavior.

C.

Store data in Amazon S3 and use object tagging for attribution. Use AWS Glue Data Catalog to manage schema information. Use AWS CloudTrail to log access to S3 buckets.

D.

Use AWS Glue Data Catalog to register all data sources. Apply metadata tags to attribute data sources. Use AWS CloudTrail to log access and activity across services.

Discussion
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