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AIP-C01 Exam Dumps : AWS Certified Generative AI Developer - Professional

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AWS Certified Generative AI Developer - Professional Questions and Answers

Question 1

A financial services company is developing a real-time generative AI (GenAI) assistant to support human call center agents. The GenAI assistant must transcribe live customer speech, analyze context, and provide incremental suggestions to call center agents while a customer is still speaking. To preserve responsiveness, the GenAI assistant must maintain end-to-end latency under 1 second from speech to initial response display. The architecture must use only managed AWS services and must support bidirectional streaming to ensure that call center agents receive updates in real time.

Which solution will meet these requirements?

Options:

A.

Use Amazon Transcribe streaming to transcribe calls. Pass the text to Amazon Comprehend for sentiment analysis. Feed the results to Anthropic Claude on Amazon Bedrock by using the InvokeModel API. Store results in Amazon DynamoDB. Use a WebSocket API to display the results.

B.

Use Amazon Transcribe streaming with partial results enabled to deliver fragments of transcribed text before customers finish speaking. Forward text fragments to Amazon Bedrock by using the InvokeModelWithResponseStream API. Stream responses to call center agents through an Amazon API Gateway WebSocket API.

C.

Use Amazon Transcribe batch processing to convert calls to text. Pass complete transcripts to Anthropic Claude on Amazon Bedrock by using the ConverseStream API. Return responses through an Amazon Lex chatbot interface.

D.

Use the Amazon Transcribe streaming API with an AWS Lambda function to transcribe each audio segment. Call the Amazon Titan Embeddings model on Amazon Bedrock by using the InvokeModel API. Publish results to Amazon SNS.

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Question 2

A pharmaceutical company is developing a Retrieval Augmented Generation (RAG) application that uses an Amazon Bedrock knowledge base. The knowledge base uses Amazon OpenSearch Service as a data source for more than 25 million scientific papers. Users report that the application produces inconsistent answers that cite irrelevant sections of papers when queries span methodology, results, and discussion sections of the papers.

The company needs to improve the knowledge base to preserve semantic context across related paragraphs on the scale of the entire corpus of data.

Which solution will meet these requirements?

Options:

A.

Configure the knowledge base to use fixed-size chunking. Set a 300-token maximum chunk size and a 10% overlap between chunks. Use an appropriate Amazon Bedrock embedding model.

B.

Configure the knowledge base to use hierarchical chunking. Use parent chunks that contain 1,000 tokens and child chunks that contain 200 tokens. Set a 50-token overlap between chunks.

C.

Configure the knowledge base to use semantic chunking. Use a buffer size of 1 and a breakpoint percentile threshold of 85% to determine chunk boundaries based on content meaning.

D.

Configure the knowledge base not to use chunking. Manually split each document into separate files before ingestion. Apply post-processing reranking during retrieval.

Question 3

A company has a generative AI (GenAI) application that uses Amazon Bedrock to provide real-time responses to customer queries. The company has noticed intermittent failures with API calls to foundation models (FMs) during peak traffic periods.

The company needs a solution to handle transient errors and provide detailed observability into FM performance. The solution must prevent cascading failures during throttling events and provide distributed tracing across service boundaries to identify latency contributors. The solution must also enable correlation of performance issues with specific FM characteristics.

Which solution will meet these requirements?

Options:

A.

Implement a custom retry mechanism with a fixed delay of 1 second between retries. Configure Amazon CloudWatch alarms to monitor the application’s error rates and latency metrics.

B.

Configure the AWS SDK with standard retry mode and exponential backoff with jitter. Use AWS X-Ray tracing with annotations to identify and filter service components.

C.

Implement client-side caching of all FM responses. Add custom logging statements in the application code to record API call durations.

D.

Configure the AWS SDK with adaptive retry mode. Use AWS CloudTrail distributed tracing to monitor throttling events.