This is the free Amazon AIP-C01 practice question bank —
50 of 100 total questions, each with a full explanation, free to
read with no signup required. Updated 2026-10-04.
Every answer is verified against official Amazon documentation —
see our methodology.
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: D covers all three risks: toxicity detection flags offensive content, prompt safety classification identifies inappropriate requests, and PII detection with redaction prevents sensitive information from reaching the FM. The checks can run in parallel to limit latency, provided the application waits for every result and applies redaction before sending content to Bedrock. Option A detects PII but does not redact it, so it does not adequately protect customer privacy.
Sources:
https://docs.aws.amazon.com/aws-certification/latest/ai-professional-01/ai-professional-01-domain3.html
Question 1
A financial services company is building a fraud detection system by using Amazon Bedrock. The system will monitor activity in multiple stock trading applications that run in the United States and Europe. The system must process 1,000 transactions every second with sub-500 ms response times. The system must also maintain high availability during connectivity disruptions.
The company must ensure that data for European customers is processed only in AWS Regions that are based in Europe.
Which solution will meet these requirements?
A. Configure AWS Lambda functions and Amazon EKS applications to use the InvokeModel API with a global inference profile. Deploy an automated failover system that uses Amazon Route 53 health checks. Create a dedicated European inference profile and enable Geographic cross-Region inference for European applications. Use Amazon CloudWatch alarms to monitor utilization metrics.
B. Configure all applications to use the InvokeModel API with provisioned throughput for an Anthropic Claude model in each Region separately. Set up a custom Application Load Balancer to distribute traffic based on Regional capacity and response times. Implement a Regional failover mechanism that uses Amazon EventBridge rules to handle connectivity disruptions.
C. Configure all applications to use the InvokeModelWithResponseStream API with on-demand throughput. Deploy an Amazon API Gateway REST API with Regional endpoints in each location where the company operates to route requests to the closest Amazon Bedrock endpoint. Create separate IAM roles for applications that run in the United States and Europe. Grant the IAM roles Region-specific permissions.
D. Configure applications that run in the United States to use provisioned throughput with the InvokeModel API. Configure European applications to use a Europe-specific geographic inference profile to ensure data sovereignty. Configure automatic scaling for provisioned capacity based on utilization metrics. Use Amazon EventBridge and AWS Lambda functions to implement cross-Region failover mechanisms.
Show Answer
Correct Answer: A
Explanation: A uses cross-Region inference to distribute high-volume requests and improve availability. The Europe-specific geographic inference profile keeps European customer data processing within European AWS Regions, while Route 53 health checks support failover during disruptions. The other options lack that combination of European processing restrictions and resilient capacity; in particular, provisioned throughput cannot be automatically scaled as D proposes.
Question 2
A company is creating a workflow to review customer-facing communications before the company sends the communications. The company uses a pre-defined message template to generate the communications and stores the communications in an Amazon S3 bucket. The workflow needs to capture a specific portion from the template and send it to an Amazon Bedrock model. The workflow must store model responses back to the original S3 bucket.
Which solution will meet these requirements?
A. Create a flow in Amazon Bedrock Flows. Configure S3 action nodes at the beginning and end of the flow to retrieve and store the communications and the model responses. In the middle of the flow, configure an expression to parse each communication. Configure an agent step to send the parsed input to the model for review.
B. Create an AWS Step Functions Express workflow state machine. Use an Amazon S3 integration GetObject step to retrieve the original communications. Use an intrinsic function Pass step to parse the communications and to pass the results to an Amazon Bedrock InvokeModel step. Configure an Amazon S3 integration PutObject step to store the model responses back to the S3 bucket.
C. Create an Amazon Bedrock agent that has an action group. Configure instructions to define how the agent should parse the communications. Configure the action group to retrieve the communications from the S3 bucket, invoke the Amazon Bedrock model, and store the model responses back to the S3 bucket.
D. Create an Amazon Bedrock agent that has a single action group. Configure three AWS Lambda functions in the action group. Configure the functions to retrieve the communications from the S3 bucket, parse the communications and invoke the Amazon Bedrock model, and store the model responses back to the S3 bucket.
Show Answer
Correct Answer: A
Explanation: Amazon Bedrock Flows can retrieve the communication from S3, use an expression node to extract the required portion, pass it to an agent for model-based review, and store the response in S3. A Step Functions Pass state’s intrinsic functions are not a general-purpose way to parse a message template.
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 2
A hospital is building an AI application to help medical clinicians to make treatment decisions. The application uses Amazon Bedrock to analyze patient case histories and suggest diagnoses. The application must maintain sub-500 ms response times to integrate with the hospital’s existing real-time clinical workflow. To comply with privacy regulations, the application must log all per-sonally identifiable information (PII) handling decisions for audits. The application must detect and remove PII from responses with at least 99% accuracy.
After initial deployment, clinicians report that diagnostic summaries from the application occa-sionally include patient names and medical record numbers that were not present in the original case history inputs. An investigation reveals that Amazon Comprehend Medical successfully de-tects and removes PII from inputs with 95% accuracy, and the application replaces all detected entities with tokens before it sends inputs to Amazon Bedrock. However, the application contin-ues to generate patient-identifying information in approximately 3-5% of outputs.
The company needs a solution to prevent the application from displaying PII in outputs while meeting all other operational requirements.
Which solution will meet these requirements?
A. Configure Amazon Bedrock guardrails with sensitive information filters to detect and block PII in model outputs.
B. Implement a secondary PII detection layer by using regular expressions and custom entity recognition to detect identifiers that Amazon Comprehend Medical misses before sending inputs to Amazon Bedrock.
C. Remove detailed medical context from case histories during pre-processing to prevent the model from generating patient-specific information based on clinical pattern associations.
D. Enable session isolation in Amazon Bedrock API calls. Clear conversation history between requests to prevent patient information from persisting across multiple case analyses.
Show Answer
Correct Answer: A
Explanation: Amazon Bedrock Guardrails’ sensitive information filters can detect PII in generated responses and block or redact it before it is displayed. This addresses PII the model introduces in its output, which input-only redaction cannot prevent. Guardrail assessments can also be captured for auditing. The other options do not reliably prevent newly generated PII from reaching clinicians.
Question 3
A company is designing an API for a generative AI (GenAI) application that uses a foundation model (FM) that is hosted on a managed model service. The API must stream responses to reduce latency, enforce token limits to manage compute resource usage, and implement retry logic to handle model timeouts and partial responses.
Which solution will meet these requirements with the LEAST operational overhead?
A. Integrate an Amazon API Gateway HTTP API with an AWS Lambda function to invoke Amazon Bedrock. Use Lambda response streaming to stream responses. Enforce token limits within the Lambda function. Implement retry logic for model timeouts by using Lambda and API Gateway timeout configurations.
B. Connect an Amazon API Gateway HTTP API directly to Amazon Bedrock. Simulate streaming by using client-side polling. Enforce token limits on the frontend. Configure retry behavior by using API Gateway integration settings.
C. Connect an Amazon API Gateway WebSocket API to an Amazon ECS service that hosts a containerized inference server. Stream responses by using the WebSocket protocol. Enforce token limits within Amazon ECS. Handle model timeouts by using ECS task lifecycle hooks and restart policies.
D. Integrate an Amazon API Gateway REST API with an AWS Lambda function that invokes Amazon Bedrock. Use Lambda response streaming to stream responses. Enforce token limits within the Lambda function. Implement retry logic by using Lambda and API Gateway timeout configurations.
Show Answer
Correct Answer: D
Explanation: API Gateway REST APIs support Lambda response streaming, so this option can return generated content incrementally while the Lambda function enforces token limits and invokes the managed model. The Lambda code should implement retries for retryable timeouts or incomplete responses; timeout settings alone do not perform retries. The HTTP API option does not provide the required native response-streaming path, while the ECS option adds unnecessary infrastructure to operate.
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 3
A company is creating a generative AI (GenAI) application that uses Amazon Bedrock foundation models (FMs). The application must use Microsoft Entra ID to authenticate. All FM API calls must stay on private network paths. Access to the application must be limited by department to specific model families. The company also needs a comprehensive audit trail of model interactions.
Which solution will meet these requirements?
A. Configure SAML federation between Microsoft Entra ID and IAM. Create department-specific IAM roles that allow only the required ModelId values. Create AWS PrivateLink interface VPC endpoints for Amazon Bedrock runtime services. Enable AWS CloudTrail to capture Amazon Bedrock API calls. Configure Amazon Bedrock model invocation logging to record detailed model interactions.
B. Create an identity provider (IdP) connection in IAM to authenticate by using Microsoft Entra ID. Assign department permission sets to control access to specific model families. Deploy AWS Lambda functions in private subnets with a NAT gateway for egress to Amazon Bedrock public endpoints. Enable CloudWatch Logs to capture model interactions for auditing purposes.
C. Create a SAML identity provider (IdP) in IAM to authenticate by using Microsoft Entra ID. Use IAM permissions boundaries to limit department roles’ access to specific model families. Configure public Amazon Bedrock API endpoints with VPC routing to maintain private network connectivity. Set up AWS CloudTrail with Amazon S3 Lifecycle rules to manage audit logs of model interactions.
D. Configure OpenID Connect (OIDC) federation between Microsoft Entra ID and IAM. Use attribute-based access control to map department attributes to specific model access permissions. Apply SCP policies to restrict access to Amazon Bedrock FM families based on department. Use Microsoft Entra ID’s built-in logging capabilities to maintain an audit trail of model interactions.
Show Answer
Correct Answer: A
Explanation: SAML federation lets Microsoft Entra ID authenticate users into department-specific IAM roles, which can restrict access to approved Bedrock model families. Bedrock Runtime PrivateLink endpoints keep FM API traffic on private network paths. CloudTrail records API activity, while Bedrock model invocation logging captures detailed interaction data for a comprehensive audit trail.
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 4
A company is building a generative AI (GenAI) application that uses Amazon Bedrock APIs to process complex customer inquiries. During peak usage periods, the application experiences intermittent API timeouts that cause issues such as broken response chunks and delayed data delivery. The application struggles to ensure that prompts remain within token limits when handling complex customer inquiries of varying lengths. Users have reported truncated inputs and incomplete responses. The company has also observed foundation model (FM) invocation failures.
The company needs a retry strategy that automatically handles transient service errors and prevents overwhelming Amazon Bedrock during peak usage periods. The strategy must also adapt to changing service availability and support response streaming and token-aware request handling.
Which solution will meet these requirements?
A. Implement a standard retry strategy that uses a 1-second fixed delay between attempts and a 3-retry maximum for all errors. Handle streaming response timeouts by restarting streams. Cap token usage for each session.
B. Implement an adaptive retry strategy that uses exponential backoff with jitter and a circuit breaker pattern that temporarily disables retries when error rates exceed a predefined threshold. Implement a streaming response handler that monitors for chunk delivery timeouts. Configure the handler to buffer successfully received chunks and intelligently resume streaming from the last received chunk when connections are re-established.
C. Use the AWS SDK to configure a retry strategy in standard mode. Wrap Amazon Bedrock API calls in try-catch blocks that handle timeout exceptions. Return cached completions for failed streaming requests. Enforce a global token limit for all users. Add jitter-based retry logic and lightweight token trimming for each request. Resume broken streams by requesting only the missing chunks from the point of failure. Maintain a small in-memory buffer of the most recent chunks to minimize redundant data transfer.
D. Set Amazon Bedrock client request timeouts to 30 seconds. Implement client-side load shedding. Buffer partial results and stop new requests when the application performance begins to degrade. Set static token usage caps for all requests. Configure exponential backoff retries, dynamic chunk sizing, and context-aware token limits.
Show Answer
Correct Answer: B
Explanation: An adaptive retry strategy with exponential backoff and jitter responds to changing service conditions, while a circuit breaker helps prevent repeated failures from adding load during an outage. Buffering received stream chunks and resuming after a delivery interruption addresses broken streaming responses. B does not explicitly describe token-aware request handling, so the prompt’s requirements are not fully represented in any option; token counting and prompt trimming would also be needed to prevent token-limit truncation.
$19
Get all 100 questions with detailed answers and explanations
Instant download HTML + PDF delivered the moment payment clears.
Secure Stripe checkout we never see or store your card details.
7-day refund if files are defective see our refund policy.