Amazon AIP-C01 Online Practice Questions and Exam Preparation

Amazon AIP-C01 Online Questions & Answers

• Question 21:

  A company has a customer service application that uses Amazon Bedrock to generate personalized responses to customer inquiries. The company needs to establish a quality assurance process to evaluate prompt effectiveness and model configurations across updates. The process must automatically compare outputs from multiple prompt templates, detect response quality issues, provide quantitative metrics, and allow human reviewers to give feedback on responses. The process must prevent configurations that do not meet a predefined quality threshold from being deployed.

  Which solution will meet these requirements?

  A. Create an AWS Lambda function that sends sample customer inquiries to multiple Amazon Bedrock model configurations and stores responses in Amazon S3. Use Amazon QuickSight to visualize response patterns. Manually review outputs daily. Use AWS CodePipeline to deploy configurations that meet the quality threshold.
  B. Use Amazon Bedrock evaluation jobs to compare model outputs by using custom prompt datasets. Configure AWS CodePipeline to run the evaluation jobs when prompt templates change. Configure CodePipeline to deploy only configurations that exceed the predefined quality threshold.
  C. Set up Amazon CloudWatch alarms to monitor response latency and error rates from Amazon Bedrock. Use Amazon EventBridge rules to notify teams when thresholds are exceeded. Configure a manual approval workflow in AWS Systems Manager.
  D. Use AWS Lambda functions to create an automated testing framework that samples production traffic and routes duplicate requests to the updated model version. Use Amazon Comprehend sentiment analysis to compare results. Block deployment if sentiment scores decrease.
  B. Use Amazon Bedrock evaluation jobs to compare model outputs by using custom prompt datasets. Configure AWS CodePipeline to run the evaluation jobs when prompt templates change. Configure CodePipeline to deploy only configurations that exceed the predefined quality threshold.

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  Explanation

  The selected response matches the requirement by using this approach: Use Amazon Bedrock evaluation jobs to compare model outputs by using custom prompt datasets. Configure AWS CodePipeline to run the evaluation jobs when prompt templates change. Configure CodePipeline to deploy only configurations that exceed the predefined quality threshold. This fits the performance requirement because it keeps serving capacity warm, reduces startup delay, and can improve time to first token for interactive users. The key AWS cue is Amazon Bedrock, which aligns with the scenario instead of forcing unrelated services into the design. The alternatives focus on Amazon Bedrock, Amazon S3, AWS Lambda, Amazon CloudWatch, Amazon EventBridge or on a different operating model, but they leave at least one required constraint unresolved. The exam takeaway is to identify the dominant constraint in the stem, then choose the managed AWS service or design pattern that satisfies that constraint with secure, reliable, and maintainable implementation effort.

• Question 22:

  A company has set up Amazon Q Developer Pro licenses for all developers at the company. The company maintains a list of approved resources that developers must use when developing applications. The approved resources include internal libraries, proprietary algorithmic techniques, and sample code with approved styling.

  A new team of developers is using Amazon Q Developer to develop a new Java-based application. The company must ensure that the new developer team uses the company's approved resources. The company does not want to make project-level modifications.

  Which solution will meet these requirements?

  A. Create a Git repository that contains all of the approved internal libraries, algorithms, and code samples. Include this Git repository in the application project locally as part of the workspace. Ensure that the developers use the workspace context to retrieve suggestions from the Git repository.
  B. In the project root folder, create a folder named amazonq/rules. Add the approved internal libraries, algorithms, and code samples to the folder.
  C. Create a folder in the application project named rules. Store the guidelines and code in the folder for Amazon Q Developer to reference for code suggestions.
  D. Create an Amazon Q Developer customization that includes the approved data sources. Ensure that the developers use the customization to develop the application.
  D. Create an Amazon Q Developer customization that includes the approved data sources. Ensure that the developers use the customization to develop the application.

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  Explanation

  The selected response matches the requirement by using this approach: Create an Amazon Q Developer customization that includes the approved data sources. Ensure that the developers use the customization to develop the application. This fits the retrieval requirement because the model receives targeted context instead of broad, stale, or irrelevant content. The key AWS cue is Amazon Q Developer, which aligns with the scenario instead of forcing unrelated services into the design. The alternatives focus on Amazon Q Developer or on a different operating model, but they leave at least one required constraint unresolved.

  The exam takeaway is to identify the dominant constraint in the stem, then choose the managed AWS service or design pattern that satisfies that constraint with secure, reliable, and maintainable implementation effort.

• Question 23:

  A hotel company wants to enhance a legacy Java-based property management system (PMS) by adding AI capabilities. The company wants to use Amazon Bedrock Knowledge Bases to provide staff with room availability information and hotel-specific details. The solution must maintain separate access controls for each hotel that the company manages. The solution must provide room availability information in near real time and must maintain consistent performance during peak usage periods.

  Which solution will meet these requirements?

  A. Deploy a single Amazon Bedrock knowledge base that contains combined data for all hotels. Configure AWS Lambda functions to synchronize data from each hotel's PMS database through direct API connections. Implement AWS CloudTrail logging with hotel-specific filters to audit access logs for each hotel's data.
  B. Create an Amazon EventBridge rule for each hotel that is invoked by changes to the PMS database. Configure the rule to send updates to a centralized Amazon Bedrock knowledge base in a management AWS account. Configure resource-based policies to enforce hotel-specific access controls.
  C. Implement one Amazon Bedrock knowledge base for each hotel in a multi-account structure. Use direct data ingestion to provide near real-time room availability information. Schedule regular synchronization for less critical information.
  D. Build a centralized Amazon Bedrock Agents solution that uses multiple knowledge bases. Implement AWS IAM Identity Center with hotel-specific permission sets to control staff access.
  C. Implement one Amazon Bedrock knowledge base for each hotel in a multi-account structure. Use direct data ingestion to provide near real-time room availability information. Schedule regular synchronization for less critical information.

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  Explanation

  The selected response matches the requirement by using this approach: Implement one Amazon Bedrock knowledge base for each hotel in a multi-account structure. Use direct data ingestion to provide near real-time room availability information. Schedule regular synchronization for less critical information. This fits the security requirement because ownership, permissions, and service boundaries remain explicit and enforceable. The key AWS cue is Amazon Bedrock, which aligns with the scenario instead of forcing unrelated services into the design. The alternatives focus on Amazon Bedrock, AWS Lambda, AWS CloudTrail, Amazon EventBridge, AWS IAM or on a different operating model, but they leave at least one required constraint unresolved. The exam takeaway is to identify the dominant constraint in the stem, then choose the managed AWS service or design pattern that satisfies that constraint with secure, reliable, and maintainable implementation effort.

• Question 24:

  A company is building a real-time voice assistant system to assist customer service representatives during customer calls. The system must convert audio calls to text with end-to-end latency of less than 500 ms.

  The system must use generative AI (GenAI) to produce response suggestions. Human supervisors must be able to rate the system's suggestions during a live customer call. The company must store all customer interactions to comply with auditing policies.

  Which solution will meet these requirements?

  A. Use the Amazon Transcribe streaming API with standard settings to convert speech to text. Use Amazon Bedrock batch processing to perform inference. Store call recordings and metadata in Amazon S3. Use S3 Lifecycle policies to manage the storage.
  B. Use the Amazon Transcribe streaming API with 100-ms audio chunks to optimize latency for the voice assistant. Call the Amazon Bedrock InvokeModelWithResponseStream operation to process client inquiries in real time. Store supervisor ratings in an Amazon DynamoDB table.
  C. Use Amazon Transcribe batch processing to perform post-call analysis. Configure AWS Lambda functions to generate responses by using the Amazon Bedrock InvokeModel operation. Use Amazon CloudWatch to log supervisor feedback.
  D. Use Amazon Transcribe to convert speech to text and to perform real-time analytics. Use Amazon Comprehend to perform sentiment analysis. Use Amazon SQS to queue processing tasks. Run the Amazon Bedrock InvokeModel operation to generate responses.
  B. Use the Amazon Transcribe streaming API with 100-ms audio chunks to optimize latency for the voice assistant. Call the Amazon Bedrock InvokeModelWithResponseStream operation to process client inquiries in real time. Store supervisor ratings in an Amazon DynamoDB table.

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  Explanation

  The selected response matches the requirement by using this approach: Use the Amazon Transcribe streaming API with 100-ms audio chunks to optimize latency for the voice assistant. Call the Amazon Bedrock InvokeModelWithResponseStream operation to process client inquiries in real time. Store supervisor ratings in an Amazon DynamoDB table. This fits the performance requirement because it keeps serving capacity warm, reduces startup delay, and can improve time to first token for interactive users. The key AWS cue is Amazon Bedrock, Amazon Transcribe, Amazon DynamoDB, which aligns with the scenario instead of forcing unrelated services into the design. The alternatives focus on Amazon Bedrock, Amazon S3, Amazon Transcribe, AWS Lambda, Amazon CloudWatch or on a different operating model, but they leave at least one required constraint unresolved. The exam takeaway is to identify the dominant constraint in the stem, then choose the managed AWS service or design pattern that satisfies that constraint with secure, reliable, and maintainable implementation effort.

• Question 25:

  A financial services company is developing a Retrieval Augmented Generation (RAG) application to help investment analysts query complex financial relationships across multiple investment vehicles, market sectors, and regulatory environments. The dataset contains highly interconnected entities that have multi-hop relationships. Analysts must examine relationships holistically to provide accurate investment guidance. The application must deliver comprehensive answers that capture indirect relationships between financial entities and must respond in less than 3 seconds.

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

  A. Use Amazon Bedrock Knowledge Bases with GraphRAG and Amazon Neptune Analytics to store financial data. Analyze multi-hop relationships between entities and automatically identify related information across documents.
  B. Use Amazon Bedrock Knowledge Bases and an Amazon OpenSearch Service vector store to implement custom relationship identification logic that uses AWS Lambda to query multiple vector embeddings in sequence.
  C. Use Amazon OpenSearch Serverless vector search with k-nearest neighbor (k-NN). Implement manual relationship mapping in an application layer that runs on Amazon EC2 Auto Scaling.
  D. Use Amazon DynamoDB to store financial data in a custom indexing system. Use AWS Lambda to query relevant records. Use Amazon SageMaker to generate responses.
  A. Use Amazon Bedrock Knowledge Bases with GraphRAG and Amazon Neptune Analytics to store financial data. Analyze multi-hop relationships between entities and automatically identify related information across documents.

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  Explanation

  The selected response matches the requirement by using this approach: Use Amazon Bedrock Knowledge Bases with GraphRAG and Amazon Neptune Analytics to store financial data. Analyze multi-hop relationships between entities and automatically identify related information across documents. This fits the retrieval requirement because the model receives targeted context instead of broad, stale, or irrelevant content. The key AWS cue is Amazon Bedrock, which aligns with the scenario instead of forcing unrelated services into the design. The alternatives focus on Amazon Bedrock, Amazon OpenSearch Service, AWS Lambda, Amazon SageMaker, Amazon DynamoDB or on a different operating model, but they leave at least one required constraint unresolved. The exam takeaway is to identify the dominant constraint in the stem, then choose the managed AWS service or design pattern that satisfies that constraint with secure, reliable, and maintainable implementation effort.

• Question 26:

  A healthcare company wants to develop a proof-of-concept application that uses Amazon Bedrock to automatically summarize medical documents. The company has 3 weeks to validate the application's accuracy. The application must comply with the company's data privacy policies. The application must include metrics to evaluate summarization accuracy and processing time.

  Which solution will meet these requirements?

  A. Create a dataset that includes 50-100 anonymized patient records. Implement Retrieval Augmented Generation (RAG) with a secure knowledge base. Use a judge model to evaluate accuracy metrics across three foundation models (FMs).
  B. Fine-tune a single foundation model (FM) on patient records. Deploy the FM on Amazon Bedrock. Use Amazon Bedrock AgentCore to configure the FM as an agent. Conduct user testing on 500 company staff members.
  C. Select the most powerful available AWS foundation model (FM). Create a chat interface by using Converse APIs. Test the application on 50-100 actual patient records by using only qualitative feedback from stakeholders. Use a custom web interface to gather real-world performance metrics.
  D. Use the Strands SDK to deploy multiple agents that connect to multiple knowledge bases that contain specialized medical documents. Compare the responses of the agents. Evaluate the integration of the agents with the company's existing systems.
  A. Create a dataset that includes 50-100 anonymized patient records. Implement Retrieval Augmented Generation (RAG) with a secure knowledge base. Use a judge model to evaluate accuracy metrics across three foundation models (FMs).

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  Explanation

  The selected response matches the requirement by using this approach: Create a dataset that includes 50- 100 anonymized patient records. Implement Retrieval Augmented Generation (RAG) with a secure knowledge base. Use a judge model to evaluate accuracy metrics across three foundation models (FMs).

  This fits the retrieval requirement because the model receives targeted context instead of broad, stale, or irrelevant content. The alternatives focus on Amazon Bedrock or on a different operating model, but they leave at least one required constraint unresolved. The exam takeaway is to identify the dominant constraint in the stem, then choose the managed AWS service or design pattern that satisfies that constraint with secure, reliable, and maintainable implementation effort.

• Question 27:

  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?

  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.
  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.

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  Explanation

  The selected response matches the requirement by using this approach: 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. This fits the performance requirement because it keeps serving capacity warm, reduces startup delay, and can improve time to first token for interactive users. The key AWS cue is Amazon Bedrock, Amazon API Gateway, Amazon Transcribe, which aligns with the scenario instead of forcing unrelated services into the design.

  The alternatives focus on Amazon Bedrock, Amazon Transcribe, Amazon Comprehend, Amazon DynamoDB, AWS Lambda or on a different operating model, but they leave at least one required constraint unresolved. The exam takeaway is to identify the dominant constraint in the stem, then choose the managed AWS service or design pattern that satisfies that constraint with secure, reliable, and maintainable implementation effort.

• Question 28:

  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?

  A. 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.
  B. 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 to monitor demographic fairness. Set up Amazon CloudWatch alarms on the GuardrailContentSource dimension by using InvocationsIntervened metrics to detect recommendation discrepancy threshold violations.
  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.
  D. 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.
  B. 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 to monitor demographic fairness. Set up Amazon CloudWatch alarms on the GuardrailContentSource dimension by using InvocationsIntervened metrics to detect recommendation discrepancy threshold violations.

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  Explanation

  The selected response matches the requirement by using this approach: 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 to monitor demographic fairness. Set up Amazon CloudWatch alarms on the GuardrailContentSource dimension by using. This fits the governance requirement because controls are applied consistently around the model workflow and the data owner remains in control instead of relying on informal prompt wording or scattered application code. The key AWS cue is Amazon Bedrock, Amazon CloudWatch, which aligns with the scenario instead of forcing unrelated services into the design. The alternatives focus on Amazon Bedrock, AWS Lambda, Amazon CloudWatch, Amazon EventBridge, Amazon SageMaker or on a different operating model, but they leave at least one required constraint unresolved. The exam takeaway is to identify the dominant constraint in the stem, then choose the managed AWS service or design pattern that satisfies that constraint with secure, reliable, and maintainable implementation effort.

• Question 29:

  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?

  A. 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.
  B. 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.
  C. 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.
  D. 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.
  B. 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.

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  Explanation

  The selected response matches the requirement by using this approach: 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. This fits the governance requirement because controls are applied consistently around the model workflow and the data owner remains in control instead of relying on informal prompt wording or scattered application code. The key AWS cue is Amazon Bedrock, which aligns with the scenario instead of forcing unrelated services into the design. The alternatives focus on Amazon Bedrock or on a different operating model, but they leave at least one required constraint unresolved. The exam takeaway is to identify the dominant constraint in the stem, then choose the managed AWS service or design pattern that satisfies that constraint with secure, reliable, and maintainable implementation effort.

• Question 30:

  A healthcare company uses Amazon Bedrock to deploy an application that generates summaries of clinical documents. The application experiences inconsistent response quality with occasional factual hallucinations. Monthly costs exceed the company's projections by 40%. A GenAI developer must implement a near real-time monitoring solution to detect hallucinations, identify abnormal token consumption, and provide early warnings of cost anomalies. The solution must require minimal custom development work and maintenance overhead.

  Which solution will meet these requirements?

  A. Configure Amazon CloudWatch alarms to monitor InputTokenCount and OutputTokenCount metrics to detect anomalies. Store model invocation logs in an Amazon S3 bucket. Use AWS Glue and Amazon Athena to identify potential hallucinations.
  B. Run Amazon Bedrock evaluation jobs that use LLM-based judgments to detect hallucinations. Configure Amazon CloudWatch to track token usage. Create an AWS Lambda function to process CloudWatch metrics. Configure the Lambda function to send usage pattern notifications.
  C. Configure Amazon Bedrock to store model invocation logs in an Amazon S3 bucket. Enable text output logging. Configure Amazon Bedrock guardrails to run contextual grounding checks to detect hallucinations. Create Amazon CloudWatch anomaly detection alarms for token usage metrics.
  D. Use AWS CloudTrail to log all Amazon Bedrock API calls. Create a custom dashboard in Amazon QuickSight to visualize token usage patterns. Use Amazon SageMaker Model Monitor to detect quality drift in generated summaries.
  C. Configure Amazon Bedrock to store model invocation logs in an Amazon S3 bucket. Enable text output logging. Configure Amazon Bedrock guardrails to run contextual grounding checks to detect hallucinations. Create Amazon CloudWatch anomaly detection alarms for token usage metrics.

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  Explanation

  The selected response matches the requirement by using this approach: Configure Amazon Bedrock to store model invocation logs in an Amazon S3 bucket. Enable text output logging. Configure Amazon Bedrock guardrails to run contextual grounding checks to detect hallucinations. Create Amazon CloudWatch anomaly detection alarms for token usage metrics. This fits the governance requirement because controls are applied consistently around the model workflow and the data owner remains in control instead of relying on informal prompt wording or scattered application code. The key AWS cue is Amazon Bedrock, Amazon S3, Amazon CloudWatch, which aligns with the scenario instead of forcing unrelated services into the design. The alternatives focus on Amazon S3, Amazon CloudWatch, AWS Glue, Amazon Bedrock, AWS Lambda or on a different operating model, but they leave at least one required constraint unresolved. The exam takeaway is to identify the dominant constraint in the stem, then choose the managed AWS service or design pattern that satisfies that constraint with secure, reliable, and maintainable implementation effort.