What is the Number of Questions Asked in Amazon AWS AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 Exam?

The number of questions on the Amazon AWS AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 exam is defined by Amazon AWS in the official exam guide. The Study4Pass practice test for this exam contains 117 practice questions with answers and explanations.

What is the Competency Level required for Amazon AWS AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 Exam?

The Amazon AWS AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 exam is an advanced level exam. It is designed for candidates who have the knowledge and practical understanding expected at the professional level of the Amazon AWS certification program.

What is the Question Format of Amazon AWS AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 Exam?

The Amazon AWS AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 exam typically includes multiple choice and multiple response questions based on the topics in the official syllabus.

What is the Recommended Experience for Amazon AWS AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 Exam?

For the Amazon AWS AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 exam, at least 2 years of hands-on experience in the field is recommended. Candidates should also review the official syllabus and practice with sample questions before scheduling the exam.

What is the Expected Retirement Date of Amazon AWS AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 Exam?

No retirement date for the Amazon AWS AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 exam has been announced by Amazon AWS; any retirement would be published on the official Amazon AWS certification pages.

What is the Difficulty Level of Amazon AWS AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 Exam?

The difficulty level of the Amazon AWS AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 exam is considered to be moderate to difficult. It is recommended to have a good understanding of the topics covered in the official syllabus.

What are the Sample Questions of Amazon AWS AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 Exam?

You can try sample questions of the Amazon AWS AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 exam for free on Study4Pass. The free practice test on this page includes sample questions with answers and explanations from the full set of 117 practice questions.

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Q: What is the Duration of Amazon AWS AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 Exam?

The duration of the Amazon AWS AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 exam is defined by Amazon AWS in the official exam guide for the AWS Certified Generative AI Developer - Professional AIP-C01 certification. Candidates should confirm the current duration on the official exam page before scheduling.

Q: What is the Passing Score for Amazon AWS AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 Exam?

The passing score for the Amazon AWS AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 exam is set by Amazon AWS and published in the official exam guide for the AWS Certified Generative AI Developer - Professional AIP-C01 certification.

Q: How Can You Take Amazon AWS AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 Exam?

You can register for the Amazon AWS AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 exam through the Amazon AWS certification website and take it at an authorized testing center or, where Amazon AWS offers it, as an online proctored exam.

Q: What is the Cost of Amazon AWS AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 Exam?

The cost of the Amazon AWS AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 exam is set by Amazon AWS and can vary by region. The current exam fee is published on the official Amazon AWS certification page for the AWS Certified Generative AI Developer - Professional AIP-C01 certification.

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Showing 1–10 of 10 questions

Question 1 (Topic 1)

A retail company has a generative AI (GenAI) product recommendation application that uses Amazon Bedrock. The application suggests products to customers based on browsing history and demographics. The company needs to implement fairness evaluation across multiple demographic groups to detect and measure bias in recommendations between two prompt approaches. The company wants to collect and monitor fairness metrics in real time. The company must receive an alert if the fairness metrics show a discrepancy of more than 15% between demographic groups. The company must receive weekly reports that compare the performance of the two prompt approaches. Which solution will meet these requirements with the LEAST custom development effort?

Select an option, then click Submit answer.

○ Configure an Amazon CloudWatch dashboard to display default metrics from Amazon Bedrock API calls. Create custom metrics based on model outputs. Set up Amazon EventBridge rules to invoke AWS lambda functions that perform post-processing analysis on model responses and publish custom fairness metrics.
○ Create the two prompt variants in Amazon Bedrock Prompt Management. Use Amazon Bedrock Flows to deploy the prompt variants with defined traffic allocation. Configure Amazon Bedrock guardrails that have content filters to monitor demographic fairness. Set up Amazon CloudWatch alarms on the GuardrailContentSource dimension that use InvocationsIntervened metrics to detect recommendation discrepancy threshold violations.
○ Set up Amazon SageMaker Clarify to analyze model outputs. Publish fairness metrics to Amazon CloudWatch. Create CloudWatch composite alarms that combine SageMaker Clarify bias metrics with Amazon Bedrock latency metrics to provide a comprehensive fairness evaluation dashboard.
○ Create an Amazon Bedrock model evaluation job to compare fairness between the two prompt variants. Enable model invocation logging in Amazon CloudWatch. Set up CloudWatch alarms for InvocationsIntervened metrics with a dimension for each demographic group.

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Reference / correct answer:

Set up Amazon SageMaker Clarify to analyze model outputs. Publish fairness metrics to Amazon CloudWatch. Create CloudWatch composite alarms that combine SageMaker Clarify bias metrics with Amazon Bedrock latency metrics to provide a comprehensive fairness evaluation dashboard.

Most accepted answer: C. Set up Amazon SageMaker Clarify to analyze model outputs. Publish fairness metrics to Amazon CloudWatch. Create CloudWatch composite alarms that combine SageMaker Clarify bias metrics with Amazon Bedrock latency metrics to provide a comprehensive fairness evaluation dashboard.

Community votes: A=3, C=11, D=3

Selected Answer: C A - Too Much Custom Development B - Guardrails Are Not Fairness Evaluation Tools D - Model Evaluation Jobs Are Not Continuous Monitoring https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-measure-data-bias.html upvoted 8 times

Selected Answer: A C seems like a reasonable choice, however it is not an "least custom development" path upvoted 1 times

Selected Answer: D D wins because it uses Bedrock's native evaluation jobs (prompt comparison), native invocation logging (real-time monitoring), and native CloudWatch alarms (threshold alerting) — all configured features, not built ones. upvoted 1 times

Selected Answer: C Okay gave my AWS Certified Generative AI Developer - Professional AIP-C01 and obtained the certification. Thank you for the assistance. upvoted 2 times

Question 2 (Topic 1)

A company has deployed an AI assistant as a React application that uses AWS Amplify, an AWS AppSync GraphQL API, and Amazon Bedrock Knowledge Bases. The application uses the GraphQL API to call the Amazon Bedrock RetrieveAndGenerate API for knowledge base interactions. The company configures an AWS Lambda resolver to use the RequestResponse invocation type. Application users report frequent timeouts and slow response times. Users report these problems more frequently for complex questions that require longer processing. The company needs a solution to fix these performance issues and enhance the user experience. Which solution will meet these requirements?

Select an option, then click Submit answer.

○ Use AWS Amplify AI Kit to implement streaming responses from the GraphQL API and to optimize client-side rendering.
○ Increase the timeout value of the Lambda resolver. Implement retry logic with exponential backoff.
○ Update the application to send an API request to an Amazon SQS queue. Update the AWS AppSync resolver to poll and process the queue.
○ Change the RetrieveAndGenerate API to the InvokeModelWithResponseStream API. Update the application to use an Amazon API Gateway WebSocket API to support the streaming response.

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Reference / correct answer:

Use AWS Amplify AI Kit to implement streaming responses from the GraphQL API and to optimize client-side rendering.

Most accepted answer: A. Use AWS Amplify AI Kit to implement streaming responses from the GraphQL API and to optimize client-side rendering.

Community votes: A=5, D=4

Selected Answer: D It did not mention least development effort or use existing. The solution is streaming response which is D. A solved FE integration but backend solve Bedrock streaming. upvoted 1 times

Selected Answer: A D is incorrect. You can replace RetrieveAndGenerate with Retrieve + InvokeModelWithResponseStream , not InvokeModelWithResponseStream alone. upvoted 1 times

Selected Answer: A The answer is A because AWS Amplify AI Kit is specifically designed to add streaming response capability to the exact technology stack the company already uses — React, Amplify, AppSync GraphQL, and Amazon Bedrock — resolving both timeout and slow response issues by delivering progressive token streaming to the client without replacing any existing architectural components, while optimizing client-side rendering for the best possible user experience with the least implementation effort. upvoted 1 times

Selected Answer: A AWS Amplify AI Kit is designed for this exact pattern: a React app using AppSync and Bedrock, where long-running model responses hurt user experience. Its streaming model sends incremental updates to the browser over an AWS AppSync WebSocket subscription instead of making the frontend wait for one large synchronous response. AWS documents that Amplify AI Kit streaming works by having Lambda call Bedrock with a streaming API request, then forward chunks through AppSync to the client. That directly addresses the “timeouts and slow response times,” especially for longer answers, by improving time-to-first-response and perceived responsiveness. upvoted 2 times

Selected Answer: D It’s D upvoted 1 times

Question 3 (Topic 1)

A medical company is building a generative AI (GenAI) application that uses 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?

Select an option, then click Submit answer.

○ Configure hybrid search by combining vector similarity with keyword matching to improve semantic understanding and exact term and acronym matching.
○ 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.
○ Replace OpenSearch Service with Amazon Kendra. Use query expansion to handle medical acronyms and terminology variants during pre-processing.
○ Implement a two-stage retrieval architecture in which initial vector search results are re-ranked by an ML model that is hosted on Amazon SageMaker AI.

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Question 4 (Topic 1)

A company has a recommendation system. The system's applications run on Amazon EC2 instances. The applications make API calls to Amazon Bedrock foundation models (FMs) to analyze customer behavior and generate personalized product recommendations. The system is experiencing intermittent issues. Some recommendations do not match customer preferences. The company needs an observability solution to monitor operational metrics and detect patterns of operational performance degradation compared to established baselines. The solution must also generate alerts with correlation data within 10 minutes when FM behavior deviates from expected patterns. Which solution will meet these requirements?

Select an option, then click Submit answer.

○ Configure Amazon CloudWatch Container Insights for the application infrastructure. Set up CloudWatch alarms for latency thresholds. Add custom metrics for token counts by using the CloudWatch embedded metric format. Create CloudWatch dashboards to visualize the data.
○ Implement AWS X-Ray to trace requests through the application components. Enable CloudWatch Logs Insights for error pattern detection. Set up AWS CloudTrail to monitor all API calls to Amazon Bedrock. Create custom dashboards in Amazon QuickSight.
○ Enable Amazon CloudWatch Application Insights for the application resources. Create custom metrics for recommendation quality, token usage, and response latency by using the CloudWatch embedded metric format with dimensions for request types and user segments. Configure CloudWatch anomaly detection on the model metrics. Establish log pattern analysis by using CloudWatch Logs Insights.
○ Use Amazon OpenSearch Service with the Observability plugin. Ingest model metrics and logs by using Amazon Kinesis. Create custom Piped Processing Language (PPL) queries to analyze model behavior patterns. Establish operational dashboards to visualize anomalies in real time.

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Reference / correct answer:

Enable Amazon CloudWatch Application Insights for the application resources. Create custom metrics for recommendation quality, token usage, and response latency by using the CloudWatch embedded metric format with dimensions for request types and user segments. Configure CloudWatch anomaly detection on the model metrics. Establish log pattern analysis by using CloudWatch Logs Insights.

Most accepted answer: C. Enable Amazon CloudWatch Application Insights for the application resources. Create custom metrics for recommendation quality, token usage, and response latency by using the CloudWatch embedded metric format with dimensions for request types and user segments. Configure CloudWatch anomaly detection on the model metrics. Establish log pattern analysis by using CloudWatch Logs Insights.

Community votes: B=1, C=2

Selected Answer: C CloudWatch anomaly detection uses machine learning to detect performance deviations https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/CloudWatch_Anomaly_Detection.html upvoted 3 times

Selected Answer: C A - container insights is wrong for EC2 B - xray is for different purpose D - opensearch is the most obvious wrong answer upvoted 4 times

Selected Answer: B Could be wrong, but I’m backing B. It just clicks. upvoted 1 times

Question 5 (Topic 1)

A company is developing a generative AI (GenAI) application that analyzes customer service calls in real-time and generates suggested responses for human customer service agents. The application must process 500,000 concurrent calls during peak hours with less than 200 ms end-to-end latency for each suggestion. The company uses existing architecture to transcribe customer call audio streams. The application must not exceed a pre-defined monthly compute budget and must maintain auto scaling capabilities. Which solution will meet these requirements?

Select an option, then click Submit answer.

○ Deploy a large, complex reasoning model on Amazon Bedrock. Purchase provisioned throughput and optimize for batch processing.
○ Deploy a low-latency, real-time optimized model on Amazon Bedrock. Purchase provisioned throughput and set up automatic scaling policies.
○ Deploy a large language model (LLM) on an Amazon SageMaker AI real-time endpoint that uses dedicated GPU instances.
○ Deploy a mid-sized language model on an Amazon SageMaker AI serverless endpoint that is optimized for batch processing.

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Question 6 (Topic 1)

A healthcare company is using Amazon Bedrock to build a system to help practitioners make clinical decisions. The system must provide treatment recommendations to physicians based only on approved medical documentation and must cite specific sources. The system must not hallucinate or produce factually incorrect information. Which solution will meet these requirements with the LEAST operational overhead?

Select an option, then click Submit answer.

○ Integrate Amazon Bedrock with Amazon Kendra to retrieve approved documents. Implement custom post-processing to compare generated responses against source documents and to include citations.
○ Deploy an Amazon Bedrock knowledge base and connect it to approved clinical source documents. Use the Amazon Bedrock RetrieveAndGenerate API to return citations from the knowledge base.
○ Use Amazon Bedrock and Amazon Comprehend Medical to extract medical entities. Implement verification logic against a medical terminology database.
○ Use an Amazon Bedrock knowledge base with Retrieve API calls and InvokeModel API calls to retrieve approved clinical source documents. Implement verification logic to compare against retrieved sources and to cite sources.

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Question 7 (Topic 1)

Example Corp provides a personalized video generation service that millions of enterprise customers use. Customers generate marketing videos by submitting prompts to the company's proprietary generative AI (GenAI) model. To improve output relevance and personalization, Example Corp wants to enhance the prompts by using customer-specific context such as product preferences, customer attributes, and business history. The customers have strict data governance requirements. The customers must retain full ownership and control over their own data. The customers do not require real-time access. However, semantic accuracy must be high and retrieval latency must remain low to support customer experience use cases. Example Corp wants to minimize architectural complexity in its integration pattern. Example Corp does not want to deploy and manage services in each customer's environment unless necessary. Which solution will meet these requirements?

Select an option, then click Submit answer.

○ Ensure that each customer sets up an Amazon Q Business index that includes the customer's internal data. Ensure that each customer designates Example Corp as a data accessor to allow Example Corp to retrieve relevant content by using a secure API to enrich prompts at runtime.
○ Use federated search with Model Context Protocol (MCP) by deploying real-time MCP servers for each customer. Retrieve data in real time during prompt generation.
○ Ensure that each customer configures an Amazon Bedrock knowledge base. Allow cross-account querying so Example Corp can retrieve structured data for prompt augmentation.
○ Configure Amazon Kendra to crawl customer data sources. Share the resulting indexes across accounts so Example Corp can query each customer's Amazon Kendra index to retrieve augmentation data.

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Reference / correct answer:

Ensure that each customer configures an Amazon Bedrock knowledge base. Allow cross-account querying so Example Corp can retrieve structured data for prompt augmentation.

Most accepted answer: C. Ensure that each customer configures an Amazon Bedrock knowledge base. Allow cross-account querying so Example Corp can retrieve structured data for prompt augmentation.

Community votes: A=2, B=1, C=6

Selected Answer: A https://aws.amazon.com/blogs/machine-learning/enhance-enterprise-productivity-for-your-llm-solution-by-becoming-an-amazon-q-business-data-accessor/ upvoted 7 times

Selected Answer: C Lowest Architectural Complexity: Amazon Bedrock Knowledge Bases provide a fully managed RAG (Retrieval-Augmented Generation) workflow. Example Corp doesn't need to manage vector databases or complex indexing logic. upvoted 1 times

Selected Answer: C Amazon Bedrock Knowledge Bases are the purpose-built managed service for RAG with high semantic accuracy, low retrieval latency, and minimal operational overhead. The cross-account querying model keeps data ownership with customers while giving Example Corp a simple, uniform integration pattern across its customer base — no per-customer infrastructure to deploy or manage. upvoted 1 times

Selected Answer: C Customer data ownership preserved (data stays in their account) Low latency + high accuracy (managed vector retrieval) No real-time ingestion requirement (pre-indexed KB) Minimal complexity for Example Corp (no per-customer infra to manage) Native Bedrock integration → simpler GenAI pipeline upvoted 1 times

Selected Answer: C A - Amazon Q Business is an internal enterprise assistant, not designed for cross-organization data retrieval by external companies. - B - Deploying MCP servers per customer = maximum complexity and contradicts "don't deploy services in each customer's environment." - D - Kendra requires crawling raw data sources (more invasive), is heavier/costlier, and is being superseded by Bedrock Knowledge Bases. upvoted 1 times

Question 8 (Topic 1)

A company is developing a new AI-powered application that needs to integrate with various specialized tools. These tools currently run as Model Context Protocol (MCP) servers on the local machines of developers and do not maintain states between invocations. The company plans to deploy each MCP server as an AWS Lambda function to support the company's production application. The solution must be accessible to both internal applications and authorized third-party partners. The solution must use strict authentication and authorization controls. Which additional steps will meet these requirements with the LEAST operational overhead?

Select an option, then click Submit answer.

○ Create a custom Lambda invocation transport by using the Lambda Invoke API. Implement IAM authentication and grant InvokeFunction permissions to authorized users and roles.
○ Expose the Lambda functions through Amazon API Gateway REST API endpoints. Implement API keys for authentication. Configure the applications that need to access the MCP servers to use standard HTTP requests instead of the MCP protocol.
○ Create Lambda function URLs and enable a custom Streamable HTTP transport and SigV4. Implement AWS IAM authentication. Grant InvokeFunctionUrl permissions to authorized users and roles.
○ Expose the Lambda function through Amazon API Gateway HTTP API endpoints with the Streamable HTTP transport. Use Amazon Cognito to implement OAuth authentication. Configure API Gateway to validate OAuth tokens.

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Reference / correct answer:

Create Lambda function URLs and enable a custom Streamable HTTP transport and SigV4. Implement AWS IAM authentication. Grant InvokeFunctionUrl permissions to authorized users and roles.

Most accepted answer: C. Create Lambda function URLs and enable a custom Streamable HTTP transport and SigV4. Implement AWS IAM authentication. Grant InvokeFunctionUrl permissions to authorized users and roles.

Community votes: A=1, C=5, D=1

Selected Answer: C MCP servers as Lambda functions (stateless) MCP's modern Streamable HTTP transport is designed exactly for stateless server deployments — it maps cleanly to Lambda's request/response model. Options A and B don't use MCP-native transport, which means rearchitecting how clients communicate. upvoted 1 times

Selected Answer: C Lowest Operational Overhead: Lambda Function URLs are a simpler way to provide dedicated HTTP(S) endpoints compared to managing Amazon API Gateway.MCP Compatibility: The Streamable HTTP transport is a core part of the Model Context Protocol. It allows for long-running connections or chunked data transfer, which is necessary for the protocol's communication patterns.Strong Security: Using AWS IAM (SigV4) authentication is the most robust way to secure the endpoint. It ensures that only specifically authorized AWS users, roles, or third-party partners (via cross-account roles) can invoke the tools. upvoted 1 times

Selected Answer: A it uses the native Lambda Invoke API to access the stateless MCP servers directly, while IAM authentication and InvokeFunction permissions provide strict access control for both internal applications and authorized third-party partners with the least operational overhead. upvoted 2 times

Selected Answer: D I think D is the answer since the question states to support third-party partners, Cognito is good for these types of situations, C for example is good if it is AWS to AWS only in which the question doesn't explicitly say upvoted 4 times

Selected Answer: C To fulfill the requirements with the least operational overhead while providing strict security for your Model Context Protocol (MCP) servers, the most efficient solution is to leverage Lambda Function URLs. upvoted 2 times

Question 9 (Topic 1)

A company is using Amazon Bedrock to develop a customer support AI assistant. The AI assistant must respond to customer questions about their accounts. The AI assistant must not expose personal information in responses. The company must comply with data residency policies by ensuring that all processing occurs within the same AWS Region where each customer is located. The company wants to evaluate how effective the AI assistant is at preventing the exposure of personal information before the company makes the AI assistant available to customers. Which solution will meet these requirements?

Select an option, then click Submit answer.

○ Configure a cross-Region Amazon Bedrock guardrail to apply sensitive information filters. Set the guardrail to detect mode during development and testing. Switch to block mode for production deployment.
○ Configure an Amazon Bedrock guardrail to apply sensitive information filters. Set the guardrail to mask mode during development and testing. Switch to block mode for production deployment. Deploy a copy of the guardrail to each Region where the company operates.
○ Configure an Amazon Bedrock guardrail to apply content and topic filters. Set the guardrail to detect mode during development, testing, and production. Disable invocation logging for the Amazon Bedrock model.
○ Configure a cross-Region Amazon Bedrock guardrail to apply a set of content and word filters. Set the guardrail to detect mode during development and testing. Switch to mask mode for production deployment.

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Question 10 (Topic 1)

A university recently digitized a collection of archival documents, academic journals, and manuscripts. The university stores the digital files in an AWS Lake Formation data lake. The university hires a GenAI developer to build a solution to allow users to search the digital files by using text queries. The solution must return journal abstracts that are semantically similar to a user's query. Users must be able to search the digitized collection based on text and metadata that is associated with the journal abstracts. The metadata of the digitized files does not contain keywords. The solution must match similar abstracts to one another based on the similarity of their text. The data lake contains fewer than 1 million files. Which solution will meet these requirements with the LEAST operational overhead?

Select an option, then click Submit answer.

○ Use Amazon Titan Embeddings in Amazon Bedrock to create vector representations of the digitized files. Store embeddings in the OpenSearch Neural Plugin for Amazon OpenSearch Service.
○ Use Amazon Comprehend to extract topics from the digitized files. Store the topics and file metadata in an Amazon Aurora PostgreSQL database. Query the abstract metadata against the data in the Aurora database.
○ Use Amazon SageMaker AI to deploy a sentence-transformer model. Use the model to create vector representations of the digitized files. Store embeddings in an Amazon Aurora PostgreSQL database that has the pgvector extension.
○ Use Amazon Titan Embeddings in Amazon Bedrock to create vector representations of the digitized files. Store embeddings in an Amazon Aurora PostgreSQL Serverless database that has the pgvector extension.

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About the AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 Free Practice Test

Thinking of taking the AWS Certified Generative AI Developer - Professional AIP-C01 (AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01) exam? This free Amazon AWS practice test is the place to start. The 10 questions come free from the full 117-question Study4Pass practice set — a quick, honest check of where you stand. The free sample is made up of 10 single-answer questions, each with a full explanation. The Amazon AWS AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 exam is part of the AWS Certified Generative AI Developer - Professional AIP-C01 certification.

The target audience of the Amazon AWS AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 exam is professionals who want to validate their skills in AWS Certified Generative AI Developer - Professional AIP-C01 and demonstrate their competency… The prerequisites of the Amazon AWS AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 exam, if any, are defined by Amazon AWS in the official exam guide.

Certification: AWS Certified Generative AI Developer - Professional AIP-C01 (AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01)
Vendor: Amazon AWS
Free on this page: 10 practice questions with answers and explanations
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Work through the questions one at a time and read every explanation, even for the ones you get right — the reasoning behind an answer is what the real exam tests. If one area keeps costing you marks, you have found exactly what to revise before exam day.

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What to study for each AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 domain

The AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 practice set is built to mirror the full AWS Certified Generative AI Developer - Professional AIP-C01 syllabus, so every major area of the real exam is represented in the questions. The Amazon AWS AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 exam is part of the AWS Certified Generative AI Developer - Professional AIP-C01 certification. The most reliable study method with a set like this is simple: practise a section, read the explanations, then go back to the official Amazon AWS documentation for any topic you missed, and repeat until the explanations feel obvious.

AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 practice test — quick questions

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Is this the real AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 exam?

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