Weekend Sale Limited Time 70% Discount Offer - Ends in 0d 00h 00m 00s - Coupon code: merry71

Free AWS Certified Generative AI Developer - Professional Practice Questions

Exams4sure Dumps

Exam style questions across every AIP-C01 domain

Last Update 2 days ago
Total Questions : 138

Start with our free AIP-C01 practice questions, carefully crafted to mirror the domains, phrasing, and difficulty of the real AWS Certified Professional exam. Each AIP-C01 exam question comes with a detailed rationale that explains not just which answer is correct but why the others fall short. That's how concepts stick. Use the free set to benchmark yourself: identify your Amazon Web Services weak domains, see where you're losing marks, and build a focused study plan in minutes.

AIP-C01 PDF

AIP-C01 PDF (Printable)
$46.5
$154.99

AIP-C01 Testing Engine

AIP-C01 PDF (Printable)
$51
$169.99

AIP-C01 PDF + Testing Engine

AIP-C01 PDF (Printable)
$63.9
$212.99
Question # 31

A company is designing a canary deployment strategy for a payment processing API. The system must support automated gradual traffic shifting between multiple Amazon Bedrock models based on real-time inference metrics, historical traffic patterns, and service health. The solution must be able to gradually increase traffic to new model versions. The system must increase traffic if metrics remain healthy and decrease traffic if the performance degrades below acceptable thresholds.

The company needs to comprehensively monitor inference latency and error rates during the deployment phase. The company must also be able to halt deployments and revert to a previous model version without any manual intervention.

Which solution will meet these requirements?

Options:

A.  

Use Amazon Bedrock with provisioned throughput to host model versions. Configure an Amazon EventBridge rule to invoke an AWS Step Functions workflow when a new model version is released. Configure the workflow to shift traffic in stages, wait for a specified time period, and invoke an AWS Lambda function to check Amazon CloudWatch performance metrics. Configure the workflow to increase traffic if metrics meet thresholds and to trigger a tra

B.  

Use AWS Lambda functions to invoke various Amazon Bedrock model versions. Use an Amazon API Gateway HTTP API with stage variables and weighted routing to shift traffic gradually. Use Amazon CloudWatch to monitor performance. Use external logic to adjust traffic and roll back if performance falls below thresholds.

C.  

Use Amazon SageMaker AI endpoint variants to represent multiple Amazon Bedrock model versions. Use variant weights to shift traffic. Use Amazon CloudWatch and SageMaker Model Monitor to trigger rollbacks. Use EventBridge to roll back deployments if an anomaly is detected.

D.  

Use Amazon OpenSearch Service to track inference logs. Configure OpenSearch Service to invoke an AWS Systems Manager Automation runbook to update Amazon Bedrock model endpoints to shift traffic based on inference logs.

Discussion 0
Question # 32

An ecommerce company is building an internal platform to develop generative AI applications by using Amazon Bedrock foundation models (FMs). Developers need to select models based on evaluations that are aligned to ecommerce use cases. The platform must display accuracy metrics for text generation and summarization in dashboards. The company has custom ecommerce datasets to use as standardized evaluation inputs.

Which combination of steps will meet these requirements with the LEAST operational overhead? (Select TWO.)

Options:

A.  

Import the datasets to an Amazon S3 bucket. Provide appropriate IAM permissions and cross-origin resource sharing (CORS) permissions to give the evaluation jobs access to the datasets.

B.  

Import the datasets to an Amazon S3 bucket. Provide appropriate IAM permissions and a VPC endpoint configuration to give the evaluation jobs access to the datasets.

C.  

Configure an AWS Lambda function to create model evaluation jobs on a schedule in the Amazon Bedrock console. Provide the URI of the S3 bucket that contains the datasets as an input. Configure the evaluation jobs to measure the real world knowledge (RWK) score for text generation and BERTScore for summarization. Configure a second Lambda function to check the status of the jobs and publish custom logs to Amazon CloudWatch. Create a custom A

D.  

Use Amazon SageMaker Clarify on a schedule to create model evaluation jobs. Use open source frameworks to create and run standardized evaluations. Publish results to Amazon CloudWatch namespaces. Use an AWS Lambda function to check the status of the jobs and publish custom logs to Amazon CloudWatch. Create a custom Amazon CloudWatch Logs Insights dashboard.

E.  

Run an Amazon SageMaker AI notebook job on a schedule by using the fmvelos or ragas framework to run evaluations that use the datasets in the S3 bucket. Write Python code in the notebook that makes direct InvokeModel API calls to the FMs and processes their responses for evaluation. Publish job status and results to Amazon CloudWatch Logs to measure the real world knowledge (RWK) score for text generation and toxicity for summarization as m

Discussion 0
Question # 33

A company uses AWS Lake Formation to set up a data lake that contains databases and tables for multiple business units across multiple AWS Regions. The company wants to use a foundation model (FM) through Amazon Bedrock to perform fraud detection. The FM must ingest sensitive financial data from the data lake. The data includes some customer personally identifiable information (PII).

The company must design an access control solution that prevents PII from appearing in a production environment. The FM must access only authorized data subsets that have PII redacted from specific data columns. The company must capture audit trails for all data access.

Which solution will meet these requirements?

Options:

A.  

Create a separate dataset in a separate Amazon S3 bucket for each business unit and Region combination. Configure S3 bucket policies to control access based on IAM roles that are assigned to FM training instances. Use S3 access logs to track data access.

B.  

Configure the FM to authenticate by using AWS Identity and Access Management roles and Lake Formation permissions based on LF-Tag expressions. Define business units and Regions as LF-Tags that are assigned to databases and tables. Use AWS CloudTrail to collect comprehensive audit trails of data access.

C.  

Use direct IAM principal grants on specific databases and tables in Lake Formation. Create a custom application layer that logs access requests and further filters sensitive columns before sending data to the FM.

D.  

Configure the FM to request temporary credentials from AWS Security Token Service . Access the data by using presigned S3 URLs that are generated by an API that applies business unit and Regional filters. Use AWS CloudTrail to collect comprehensive audit trails of data access.

Discussion 0
Question # 34

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 Tran scribe for multimedia files. Store the processed content in Amazon S3 with versioning enabled. Store the metadata in Amazon DynamoD

B.  

Collaborate in real time by using AWS AppSync GraphQL subscriptions and DynamoD

B.  

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 0
Question # 35

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?

Options:

A.  

Enable Amazon Macie PII detection on the S3 bucket. Use an S3 trigger to invoke an AWS Lambda function that redacts PII from the reports. Configure the Lambda function to delete outdated documents and invoke knowledge base syncing.

B.  

Invoke an AWS Lambda function to sync the S3 bucket and the knowledge base when a new report is uploaded. Use a second Lambda function with Amazon Comprehend to redact PII for engineers. Use S3 Lifecycle rules to remove reports older than 3 years.

C.  

Set up an S3 Lifecycle configuration to remove reports that are older than 3 years. Schedule an AWS Lambda function to run daily syncs between the bucket and the knowledge base. 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.

D.  

Create a second knowledge base. Use Lambda and Amazon Comprehend to redact PII before syncing to the second knowledge base. Route users to the appropriate knowledge base based on Cognito group membership.

Discussion 0
Question # 36

A healthcare company is using Amazon Bedrock to build a Retrieval Augmented Generation (RAG) application that helps practitioners make clinical decisions. The application must achieve high accuracy for patient information retrievals, identify hallucinations in generated content, and reduce human review costs.

Which solution will meet these requirements?

Options:

A.  

Use Amazon Comprehend to analyze and classify RAG responses and to extract medical entities and relationships. Use AWS Step Functions to orchestrate automated evaluations. Configure Amazon CloudWatch metrics to track entity recognition confidence scores. Configure CloudWatch to send an alert when accuracy falls below specified thresholds.

B.  

Implement automated large language model (LLM)-based evaluations that use a specialized model that is fine-tuned for medical content to assess all responses. Deploy AWS Lambda functions to parallelize evaluations. Publish results to Amazon CloudWatch metrics that track relevance and factual accuracy.

C.  

Configure Amazon CloudWatch Synthetics to generate test queries that have known answers on a regular schedule, and track model success rates. Set up dashboards that compare synthetic test results against expected outcomes.

D.  

Deploy a hybrid evaluation system that uses an automated LLM-as-a-judge evaluation to initially screen responses and targeted human reviews for edge cases. Use a built-in Amazon Bedrock evaluation to track retrieval precision and hallucination rates.

Discussion 0
Question # 37

A company is developing a generative AI (GenAI)-powered customer support application that uses Amazon Bedrock foundation models (FMs). The application must maintain conversational context across multiple interactions with the same user. The application must run clarification workflows to handle ambiguous user queries. The company must store encrypted records of each user conversation to use for personalization. The application must be able to handle thousands of concurrent users while responding to each user quickly.

Which solution will meet these requirements?

Options:

A.  

Use an AWS Step Functions Express workflow to orchestrate conversation flow. Invoke AWS Lambda functions to run clarification logic. Store conversation history in Amazon RDS and use session IDs as the primary key.

B.  

Use an AWS Step Functions Standard workflow to orchestrate clarification workflows. Include Wait for a Callback patterns to manage the workflows. Store conversation history in Amazon DynamoD

B.  

Purchase on-demand capacity and configure server-side encryption.

C.  

Deploy the application by using an Amazon API Gateway REST API to route user requests to an AWS Lambda function to update and retrieve conversation context. Store conversation history in Amazon S3 and configure server-side encryption. Save each interaction as a separate JSON file.

D.  

Use AWS Lambda functions to call Amazon Bedrock inference APIs. Use Amazon SQS queues to orchestrate clarification steps. Store conversation history in an Amazon ElastiCache (Redis OSS) cluster. Configure encryption at rest.

Discussion 0
Question # 38

A healthcare company is developing an application to process medical queries. The application must answer complex queries with high accuracy by reducing semantic dilution. The application must refer to domain-specific terminology in medical documents to reduce ambiguity in medical terminology. The application must be able to respond to 1,000 queries each minute with response times less than 2 seconds.

Which solution will meet these requirements with the LEAST operational overhead?

Options:

A.  

Use Amazon API Gateway to route incoming queries to an Amazon Bedrock agent. Configure the agent to use an Anthropic Claude model to decompose queries and an Amazon Titan model to expand queries. Create an Amazon Bedrock knowledge base to store the reference medical documents.

B.  

Configure an Amazon Bedrock knowledge base to store the reference medical documents. Enable query decomposition in the knowledge base. Configure an Amazon Bedrock flow that uses a foundation model and the knowledge base to support the application.

C.  

Use Amazon SageMaker AI to host custom ML models for both query decomposition and query expansion. Configure Amazon Bedrock knowledge bases to store the reference medical documents. Encrypt the documents in the knowledge base.

D.  

Create an Amazon Bedrock agent to orchestrate multiple AWS Lambda functions to decompose queries. Create an Amazon Bedrock knowledge base to store the reference medical documents. Use the agent’s built-in knowledge base capabilities. Add deep research and reasoning capabilities to the agent to reduce ambiguity in the medical terminology.

Discussion 0
Question # 39

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 0
Question # 40

A GenAI developer is building a Retrieval Augmented Generation (RAG)-based customer support application that uses Amazon Bedrock foundation models (FMs). The application needs to process 50 GB of historical customer conversations that are stored in an Amazon S3 bucket as JSON files. The application must use the processed data as its retrieval corpus. The application’s data processing workflow must extract relevant data from customer support documents, remove customer personally identifiable information (PII), and generate embeddings for vector storage. The processing workflow must be cost-effective and must finish within 4 hours.

Which solution will meet these requirements with the LEAST operational overhead?

Options:

A.  

Use AWS Lambda and Amazon Comprehend to process files in parallel, remove PII, and call Amazon Bedrock APIs to generate vectors. Configure Lambda concurrency limits and memory settings to optimize throughput.

B.  

Create an AWS Glue ETL job to run PII detection scripts on the data. Use Amazon SageMaker Processing to run the HuggingFaceProcessor to generate embeddings by using a pre-trained model. Store the embeddings in Amazon OpenSearch Service .

C.  

Deploy an Amazon EMR cluster that runs Apache Spark with user-defined functions (UDFs) that call Amazon Comprehend to detect PII. Use Amazon Bedrock APIs to generate vectors. Store outputs in Amazon Aurora PostgreSQL with the pgvector extension.

D.  

Implement a data processing pipeline that uses AWS Step Functions to orchestrate a workload that uses Amazon Comprehend to detect PII and Amazon Bedrock to generate embeddings. Directly integrate the workflow with Amazon OpenSearch Serverless to store vectors and provide similarity search capabilities.

Discussion 0

Free Exams Sample Questions