This is the free Amazon AIP-C01 practice question bank —
50 of 97 total questions, each with a full explanation, free to
read with no signup required. Updated 2026-08-04.
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Question 1
A company is developing a customer communication platform that uses an AI assistant powered by an Amazon Bedrock foundation model (FM). The AI assistant summarizes customer messages and generates initial response drafts.
The company wants to use Amazon Comprehend to implement layered content filtering. The layered content filtering must prevent sharing of offensive content, protect customer privacy, and detect potential inappropriate advice solicitation. Inappropriate advice solicitation includes requests for unethical practices, harmful activities, or manipulative behaviors. The solution must maintain acceptable overall response times, so all pre-processing filters must finish before the content reaches the FM.
Which solution will meet these requirements?
A. Use parallel processing with asynchronous API calls. Use toxicity detection for offensive content. Use prompt safety classification for inappropriate advice solicitation. Use personally identifiable information (PII) detection without redaction.
B. Use custom classification to build an FM that detects offensive content and inappropriate advice solicitation. Apply personally identifiable information (PII) detection as a secondary filter only when messages pass the custom classifier.
C. Deploy a multi-stage process. Configure the process to use prompt safety classification first, then toxicity detection on safe prompts only, and finally personally identifiable information (PII) detection in streaming mode. Route flagged messages through Amazon EventBridge for human review.
D. Use toxicity detection with thresholds configured to 0.5 for all categories. Use parallel processing for both prompt safety classification and personally identifiable information (PII) detection with entity redaction. Apply Amazon CloudWatch alarms to filter metrics.
Show Answer
Correct Answer: D
Explanation: The requirements map to Amazon Comprehend toxicity detection (offensive content), prompt safety classification (unsafe or inappropriate advice solicitation), and PII detection with redaction (protect customer privacy before the prompt reaches the FM). Running the independent filters in parallel minimizes preprocessing latency while ensuring all checks complete before inference. Option A omits PII redaction, which does not adequately protect privacy. Option B incorrectly suggests building an FM with custom classification and delays PII checks. Option C adds unnecessary sequential processing and EventBridge review, increasing latency without being required.
Question 2
A financial services company is deploying a generative AI (GenAI) application that uses Amazon Bedrock to assist customer service representatives to provide personalized investment advice to customers. The company must implement a comprehensive governance solution that follows responsible AI practices and meets regulatory requirements.
The solution must detect and prevent hallucinations in recommendations. The solution must have safety controls for customer interactions. The solution must also monitor model behavior drift in real time and maintain audit trails of all prompt-response pairs for regulatory review.
The company must deploy the solution within 60 days. The solution must integrate with the company's existing compliance dashboard and respond to customers within 200 ms.
Which solution will meet these requirements with the LEAST operational overhead?
A. Configure Amazon Bedrock guardrails to apply custom content filters and toxicity detection. Use Amazon Bedrock Model Evaluation to detect hallucinations. Store prompt-response pairs in Amazon DynamoDB to capture audit trails and set a TTL. Integrate Amazon CloudWatch custom metrics with the existing compliance dashboard.
B. Deploy Amazon Bedrock and use AWS PrivateLink to access the application securely. Use AWS Lambda functions to implement custom prompt validation. Store prompt-response pairs in an Amazon S3 bucket and configure S3 Lifecycle policies. Create custom Amazon CloudWatch dashboards to monitor model performance metrics.
C. Use Amazon Bedrock Agents and Amazon Bedrock Knowledge Bases to ground responses. Use Amazon Bedrock Guardrails to enforce content safety. Use Amazon OpenSearch Service to store and index prompt-responses pairs. Integrate OpenSearch Service with Amazon QuickSight to create compliance reports and to detect model behavior drift.
D. Use Amazon SageMaker Model Monitor to detect model behavior drift. Use AWS WAF to filter content. Store customer interactions in an encrypted Amazon RDS database. Use Amazon API Gateway to create custom HTTP APIs to integrate with the compliance dashboard.
Show Answer
Correct Answer: A
Explanation: Option A is the best fit with the least operational overhead because it relies primarily on managed Amazon Bedrock capabilities for responsible AI governance. Bedrock Guardrails provide built-in safety controls and hallucination detection/prevention capabilities, DynamoDB offers low-latency storage for prompt/response audit trails, and CloudWatch metrics can integrate with an existing compliance dashboard. The other options require substantially more custom implementation or introduce services that do not directly address the stated responsible AI requirements.
Sources:
https://aws.amazon.com/bedrock/guardrails
https://aws.amazon.com/bedrock/security-privacy-responsible-ai
Question 3
A software as a service (SaaS) company is building a recommendation model that uses Amazon SageMaker AI to support an application that recommends airline cabin upgrades to customers. The company will host SageMaker AI models on Amazon Bedrock by using Amazon Bedrock Custom Model Import. Airline companies will use the application to send customized offers to customers.
The model must examine the travel history of customers to help make more relevant recommendations. The company stores customer travel history data in an Amazon RDS database. The company must ensure that the application delivers consistent, relevant, and accurate results across multiple airlines and customer populations.
Which solution will meet these requirements?
A. Use Amazon Bedrock Knowledge Bases to implement a RAG architecture to analyze customer travel history data to give the application semantic search capabilities. Use the semantic search capabilities to retrieve relevant booking patterns, preferences, and loyalty information to generate personalized cabin upgrade recommendations. Apply Amazon Bedrock guardrails to filter content. Use AWS Step Functions and AWS Lambda functions to orchestrate validation workflows to reduce hallucinations.
B. Implement text-to-SQL transformations with SQL validations to accurately retrieve relevant booking patterns, preferences, and loyalty information from the RDS database. Use the results to generate personalized cabin upgrade recommendations. Apply Amazon Bedrock guardrails to filter content. Use AWS Step Functions and AWS Lambda functions to orchestrate validation workflows to reduce hallucinations.
C. Use Amazon OpenSearch Service to implement vector searches of customer travel history embeddings. Use the vector searches to give the application the ability to perform similarity-based retrieval of booking patterns, preferences, and loyalty information to generate personalized cabin upgrade recommendations. Apply Amazon Bedrock guardrails to filter responses. Use confidence scoring and semantic similarity searches to reduce hallucinations.
D. Implement text-to-SQL transformations with SQL validations to accurately retrieve relevant booking patterns, preferences, and loyalty information from the RDS database. Use the results to generate personalized cabin upgrade recommendations. Apply Amazon Bedrock guardrails to filter responses. Use confidence scoring and semantic similarity searches to reduce hallucinations.
Show Answer
Correct Answer: B
Explanation: The travel history is stored in Amazon RDS as structured relational data, so text-to-SQL with SQL validation is the most appropriate retrieval approach. This provides deterministic access to booking patterns, preferences, and loyalty information without converting relational data into embeddings. Using Step Functions and Lambda to orchestrate validation workflows aligns with the requirement to improve consistency and reduce hallucinations. Bedrock Guardrails add content filtering, while Knowledge Bases and vector search are better suited to unstructured or embedded data rather than directly querying relational databases.
Question 4
A company runs a Retrieval Augmented Generation (RAG) application that uses Amazon Bedrock Knowledge Bases to perform regulatory compliance queries. The application uses the RetrieveAndGenerateStream API. The application retrieves relevant documents from a knowledge base that contains more than 50,000 regulatory documents, legal precedents, and policy updates.
The RAG application is producing suboptimal responses because the initial retrieval often returns semantically similar but contextually irrelevant documents. The poor responses are causing model hallucinations and incorrect regulatory guidance. The company needs to improve the performance of the RAG application so it returns more relevant documents.
Which solution will meet this requirement with the LEAST operational overhead?
A. Deploy an Amazon SageMaker endpoint to run a fine-tuned ranking model. Use an Amazon API Gateway REST API to route requests. Configure the application to make requests through the REST API to rerank the results.
B. Use Amazon Comprehend to classify documents and apply relevance scores. Integrate the RAG application’s reranking process with Amazon Textract to run document analysis. Use Amazon Neptune to perform graph-based relevance calculations.
C. Implement a retrieval pipeline that uses the Amazon Bedrock Knowledge Bases Retrieve API to perform initial document retrieval. Call the Amazon Bedrock Rerank API to rerank the results. Invoke the InvokeModelWithResponseStream operation to generate responses.
D. Use the latest Amazon reranker model through the reranking configuration within Amazon Bedrock Knowledge Bases. Use the model to improve document relevance scoring and to reorder results based on contextual assessments.
Show Answer
Correct Answer: D
Explanation: Amazon Bedrock Knowledge Bases supports native reranking configuration with supported reranker models. Because the application already uses the RetrieveAndGenerateStream API, enabling reranking within the knowledge base improves retrieval relevance with minimal operational overhead and no custom infrastructure. Option C requires redesigning the flow to use separate Retrieve, Rerank, and generation APIs, increasing integration effort. Options A and B add significant infrastructure and operational complexity.
Question 5
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 base. The AI assistant uses Amazon Cognito to authenticate users.
Which solution will meet these requirements?
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 from the bucket and to 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 to the bucket. Use a second Lambda function to invoke Amazon Comprehend to detect and redact PII if a user is part of the engineer Cognito user group. Set up an S3 Lifecycle configuration to remove reports that are older than 3 years from the bucket.
C. Set up an S3 Lifecycle configuration on the bucket 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, call the ApplyGuardrail configuration that matches the user's Cognito user group to redact PII from the agent’s responses if appropriate.
D. Create a second knowledge base. Set up an S3 Lifecycle configuration on the bucket to remove reports that are older than 3 years. Invoke an AWS Lambda function that syncs the bucket with the original knowledge base when a new report is uploaded to the bucket. Use Amazon Comprehend to detect and redact PII before syncing the bucket with the second knowledge base. When a user interacts with the AI assistant, redirect the model to the appropriate knowledge base depending on the user’s Cognito user group.
Show Answer
Correct Answer: C
Explanation: Use S3 Lifecycle to ensure reports older than 3 years are removed. Keep the knowledge base synchronized, and apply Amazon Bedrock Guardrails at response time based on the authenticated user's Cognito group so surgeons can see PII while engineers receive redacted responses. This preserves a single source of truth while enforcing role-based redaction.
Question 6
A financial services company wants to use Amazon Bedrock foundation models (FMs) to analyze call center recordings. When calls end, the call center stores recordings as MP3 files in an Amazon S3 bucket. The company needs to generate summaries and sentiment analysis for the recordings in a structured format as soon as new files are created. The recordings average 20 MB in size.
Which combination of solutions will meet these requirements? (Choose two.)
A. Use AWS Step Functions to orchestrate a workflow to process the recordings. Configure steps to invoke Amazon Transcribe to convert audio to text, validate job completion, and to invoke an AWS Lambda function to process the text by using Amazon Bedrock FMs to generate structured analysis output.
B. Use AWS Step Functions to orchestrate a workflow to process the recordings. Configure steps to invoke Amazon Transcribe to convert audio to text, validate job completion, and to directly invoke Amazon Bedrock FMs to generate summaries and sentiment analysis in JSON format.
C. Use AWS Step Functions to orchestrate a workflow to process the recordings. Configure steps to invoke Amazon Transcribe to convert audio to text, validate job completion, and to invoke an AWS Lambda function to create a prompt to invoke Amazon Bedrock FMs to generate structured analysis output.
D. Configure the source S3 bucket to send events to Amazon EventBridge. Create an EventBridge rule to invoke the Step Functions workflow when an object is created in the bucket.
E. Configure the source S3 bucket to send notifications to the Step Functions workflow when an object is created in the bucket.
Show Answer
Correct Answer: C, D
Explanation: Amazon Transcribe is required to convert the MP3 recordings to text. Because Transcribe is asynchronous, AWS Step Functions is appropriate to orchestrate the job and wait for completion. A Lambda function is then used to retrieve the transcript if needed, construct the prompt, and invoke Amazon Bedrock to produce structured JSON summaries and sentiment analysis. For event-driven execution, Amazon S3 object creation events are commonly routed through Amazon EventBridge, which can start the Step Functions state machine. S3 does not directly target Step Functions via bucket notification destinations.
Question 7
A GenAI developer is evaluating Amazon Bedrock foundation models (FMs) to enhance a Europe-based company's internal business application. The company has a multi-account landing zone in AWS Control Tower. The company uses SCPs to allow its accounts to use only the eu-north-1 Region and the eu-west-1 Region. All customer data must remain in private networks within the approved AWS Regions.
The GenAI developer selects an FM based on analysis and testing and hosts the model in the eu-central-1 Region and the eu-west-3 Region. The GenAI developer must enable access to the FM for the company’s employees. The GenAI developer must ensure that requests to the FM are private and remain with the same Regions as the FM.
Which solution will meet these requirements?
A. Deploy an AWS Lambda function that is exposed by a private Amazon API Gateway REST API to a VPC in eu-north-1. Create a VPC endpoint for the selected FM in eu-central-1 and eu-west-3. Extend existing SCPs to allow employees to use the FM. Integrate the REST API with the business application.
B. Deploy the FM on Amazon EC2 instances in eu-north-1. Deploy a private Amazon API Gateway REST API in front of the EC2 instances. Configure an Amazon Bedrock VPC endpoint. Integrate the REST API with the business application.
C. Configure the FM to use cross-Region inference through an eu.amazon.* endpoint to ensure that all calls remain within Europe. Configure an Amazon Bedrock VPC endpoint. Extend existing SCPs to allow employees to use the FM through inference profiles in Europe-based Regions where the FM is available. Use an inference profile to integrate Amazon Bedrock with the business application.
D. Deploy the FM in Amazon SageMaker AI in eu-north-1. Configure a SageMaker AI VPC endpoint. Extend existing SCPs to allow employees to use the SageMaker AI endpoint. Integrate the FM in SageMaker AI with the business application.
Show Answer
Correct Answer: C
Explanation: Cross-Region inference with the Europe geographic endpoint (eu.amazon.*) and an inference profile is the Amazon Bedrock feature designed to route requests only among supported European Regions while keeping traffic within Europe. Using an Amazon Bedrock VPC endpoint provides private connectivity. Updating the SCPs to permit the required Bedrock inference profile access enables use from the organization's allowed accounts without replacing the architecture or self-hosting the model.
Question 8
A large ecommerce company has deployed a foundation model (FM) to generate product descriptions. The company's engineering team monitors technical metrics such as token usage, latency, and error rates by using Amazon CloudWatch. The company's marketing team tracks business metrics such as conversion rates and revenue impact in its own systems.
The company needs a unified observability solution that correlates technical performance with business outcomes. The solution must provide automatic alerts to stakeholders when operational metrics indicate degradation. The solution must provide comprehensive visibility across both technical and business metrics.
Which solution will meet these requirements?
A. Create CloudWatch dashboards that include technical metrics and imported business metrics. Configure CloudWatch composite alarms that combine technical data and business data. Use Amazon SNS to set up notifications to stakeholders.
B. Use Amazon Managed Grafana to visualize technical metrics from CloudWatch with business metrics from external sources. Configure Amazon Managed Grafana alerts to invoke AWS Lambda functions. Configure the Lambda functions to remediate issues automatically when metrics exceed predefined thresholds.
C. Stream CloudWatch metrics to Amazon S3 by using CloudWatch metric streams. Create Amazon QuickSight dashboards to visualize the combined technical metrics and business metrics. Set up Amazon EventBridge rules to send notifications to stakeholders when metrics exceed predefined thresholds.
D. Configure CloudWatch custom dashboards that integrate operational metrics with imported business metrics. Set up CloudWatch composite alarms with anomaly detection. Use Amazon SNS to create alarm actions to notify stakeholders when correlated metrics indicate performance issues.
Show Answer
Correct Answer: D
Explanation: CloudWatch can combine native operational metrics with imported/custom business metrics on dashboards. CloudWatch composite alarms, together with anomaly detection, provide automated detection of correlated operational issues and Amazon SNS can notify stakeholders. This satisfies unified visibility and automatic alerting without requiring additional visualization or remediation services.
Question 9
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?
A. 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.
B. 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.
C. 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.
D. 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.
Show Answer
Correct Answer: D
Explanation: The requirement is semantic search over fewer than 1 million documents with minimal operational overhead. Amazon Titan Embeddings in Amazon Bedrock provides a fully managed embedding model, avoiding the need to deploy and manage ML infrastructure. Aurora PostgreSQL Serverless with the pgvector extension supports vector similarity search while minimizing database administration through serverless scaling. Amazon Comprehend topic extraction is not semantic vector search, SageMaker requires managing model endpoints, and OpenSearch Service introduces a dedicated search cluster, which is more operationally involved than Aurora Serverless for this scale.
Question 10
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.
Show Answer
Correct Answer: B
Explanation: Sensitive information filters are the appropriate Bedrock guardrail feature for preventing exposure of personal information. Using mask mode during development and testing allows evaluation of what would have been filtered while preserving visibility into model behavior. Switching to block mode in production provides stronger protection. To satisfy data residency requirements, the guardrail should be deployed separately in each AWS Region rather than using a cross-Region configuration.
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