100% Free Amazon AIP-C01 Practice Exam Questions Online 2026

Question 1

A legal services firm is building an AI contract review system using Amazon Bedrock Agents. The workflow requires multiple specialized agents: one agent extracts key contract terms, another identifies potential legal risks, and a third generates recommended revisions. Before finalizing any contract recommendations, a senior attorney must review and approve the AI-generated suggestions. The system must pause execution, send notifications to attorneys, wait for approval decisions, and either proceed with approved changes or halt the workflow. The solution needs to track approval status and provide audit trails of all decision points.

Which solution orchestrates this multi-agent workflow with human approval MOST effectively?

A : Use AWS Step Functions to orchestrate the three Bedrock Agents sequentially, integrate a Task Token pattern for human approval with Amazon SNS notifications, and resume execution based on attorney decisions captured through API Gateway.

B : Deploy AWS Lambda functions to invoke each Bedrock Agent in sequence. Store intermediate results in Amazon DynamoDB and configure EventBridge scheduled rules to poll for attorney approval flags in the database.

C : Configure Amazon Bedrock Agent Collaboration to chain the three agents together. Implement a custom Lambda authorizer that pauses agent execution and sends approval requests via Amazon SES before allowing continuation.

D : Create an Amazon SQS queue for each agent's output. Deploy Lambda functions to process queue messages sequentially, write results to S3, and use S3 Event Notifications to trigger approval emails to attorneys.

Answer: A

Question 2

A legal tech company uses Amazon Bedrock to extract specific liability clauses from uploaded contracts. Users have reported instances where the model "hallucinates" clauses that do not exist in the source text.

Which mechanisms should the developer implement to verify accuracy and reduce these hallucinations?

A : Define a JSON Schema representing the expected liability data structure and include it in the model instructions to enforce constrained, deterministic output.

B : Implement a post-processing workflow that calculates the semantic similarity score between the generated response and the source text, rejecting responses that fall below a defined confidence threshold.

C : Configure Amazon Bedrock Guardrails with a "Sensitive Information" filter to redact names and addresses found in the contract clauses.

D : Increase the inference temperature parameter to 0.9 and enable Top-P sampling to broaden the diversity of the generated clauses.

E : Use Amazon CloudWatch to monitor the InvocationLatency and ModelTokenCount metrics to identify anomalies in the model's processing speed.

Answer: A,B

Question 3

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 theflow 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 inputto the model for review.

B : Create an AWS Step Functions Express workflow state machine. Use an Amazon S3 integrationGetObject step to retrieve the original communications. Use an intrinsic function Pass step to parse thecommunications and to pass the results to an Amazon Bedrock InvokeModel step. Configure anAmazon 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 theagent should parse the communications. Configure the action group to retrieve the communicationsfrom the S3 bucket, invoke the Amazon Bedrock model, and store the model responses back to the S3bucket.

D : Create an Amazon Bedrock agent that has a single action group. Configure three AWS Lambdafunctions in the action group. Configure the functions to retrieve the communications from the S3bucket, parse the communications and invoke the Amazon Bedrock model, and store the modelresponses back to the S3 bucket.

Answer: A

Question 4

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?

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

B : Invoke an AWS Lambda function to sync the S3 bucket and the knowledge base when a new report isuploaded. Use a second Lambda function with Amazon Comprehend to redact PII for engineers. Use S3Lifecycle 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 AWSLambda function to run daily syncs between the bucket and the knowledge base. When users interactwith the AI assistant, apply a guardrail configuration selected based on the user’s Cognito user group toredact PII from responses when required.

D : Create a second knowledge base. Use Lambda and Amazon Comprehend to redact PII before syncing tothe second knowledge base. Route users to the appropriate knowledge base based on Cognito groupmembership.

Answer: C

Question 5

An online gaming platform is launching a GenAI-powered support assistant using multiple Foundation Models (FMs) available in Amazon Bedrock (including Amazon Titan and Anthropic Claude). The security team has issued strict requirements for the assistant's input handling:

PII Protection: Users must be prevented from submitting email addresses or phone numbers.

Toxicity: Hate speech and insults must be blocked immediately.

Custom Blocking: A specific list of known "cheat codes" and "exploit keywords" must be blocked from entering the model context.

Consistency: These rules must apply uniformly across all used FMs without rewriting application logic for each model.

Which solution should you implement to meet these requirements with the LEAST operational overhead?

A : Develop a pre-processing AWS Lambda function using the Python re library to detect PII patterns and cheat codes. Use Amazon Comprehend DetectSentiment API to identify hate speech. Configure the application to call this Lambda function and verify the result before calling InvokeModel on Amazon Bedrock.

B : Implement a standardized system prompt across all FMs that instructs the models to refuse to answer if the input contains emails, phone numbers, hate speech, or words from the cheat code list. Use the Amazon Bedrock Converse API to ensure the system prompt is strictly enforced by the model’s reasoning engine.

C : Use AWS WAF (Web Application Firewall) attached to the application load balancer handling the chat traffic. Create custom regex rules to block PII patterns and cheat codes. Enable the AWS Managed Rules for Anonymous IP List to reduce toxicity risks from bad actors.

D : Create an Amazon Bedrock Guardrail. Configure PII filters to block emails and phone numbers, and enable Content filters for hate speech. Use the Word filters feature to upload the list of cheat codes and exploit keywords. Associate the Guardrail with the InvokeModel API calls for all used FMs.

Answer: D

Free Amazon AIP-C01 Practice Exam Questions and Answers 2026