100% Free Amazon AIP-C01 Practice Test Questions

Question 1

A company is designing a canary deployment strategy for a payment processing API. The system mustsupport automated gradual traffic shifting between multiple Amazon Bedrock models based on real-timeinference metrics, historical traffic patterns, and service health. The solution must be able to graduallyincrease traffic to new model versions. The system must increase traffic if metrics remain healthy anddecrease traffic if the performance degrades below acceptable thresholds.The company needs to comprehensively monitor inference latency and error rates during the deploymentphase. The company must also be able to halt deployments and revert to a previous model version without anymanual intervention.Which solution will meet these requirements?

A : Use Amazon Bedrock with provisioned throughput to host model versions. Configure an AmazonEventBridge 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 AWSLambda function to check Amazon CloudWatch performance metrics. Configure the workflow toincrease traffic if metrics meet thresholds and to trigger a traffic rollback if performance metrics fallbelow thresholds.

B : Use AWS Lambda functions to invoke various Amazon Bedrock model versions. Use an Amazon APIGateway HTTP API with stage variables and weighted routing to shift traffic gradually. Use AmazonCloudWatch to monitor performance. Use external logic to adjust traffic and roll back if performancefalls 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 triggerrollbacks. 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 anAWS Systems Manager Automation runbook to update Amazon Bedrock model endpoints to shifttraffic based on inference logs.

Answer: A

Question 2

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

Question 3

A company is creating a workflow to review customer-facing communications before the company sends thecommunications. The company uses a pre-defined message template to generate the communications andstores the communications in an Amazon S3 bucket. The workflow needs to capture a specific portion fromthe template and send it to an Amazon Bedrock model. The workflow must store model responses back to theoriginal 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 financial services institution is building a proprietary 175-billion parameter Foundation Model (FM) to power a real-time fraud detection assistant. The project lifecycle involves two critical phases with distinct infrastructure requirements:Training Phase: The model must be pre-trained on petabytes of encrypted transaction logs. This requires synchronizing gradients across thousands of GPUs with minimal network latency to accelerate convergence.Deployment Phase: The fraud detection assistant must analyze transactions interactively with sub-millisecond latency. The traffic is highly variable, requiring the infrastructure to scale out automatically during market hours and scale in at night.Which combination of architectural strategies should the GenAI Developer implement to meet these requirements?

A : Deploy the model to Amazon SageMaker Asynchronous Inference. Configure the endpoint to buffer the incoming transactions in an internal queue, allowing the model to process them in large batches to maximize throughput.

B : Deploy the trained model to an Amazon SageMaker Real-time inference endpoint. Configure Target Tracking scaling policies to verify the SageMakerVariantInvocationsPerInstance metric and automatically adjust the number of instances (data parallelism) to handle the fluctuating traffic.

C : Launch the training job on Amazon EC2 UltraClusters (e.g., p4d instances) within Amazon SageMaker. Enable the SageMaker Distributed Data Parallel (SMDDP) library to maximize throughput and efficiently synchronize gradients across the high-speed Elastic Fabric Adapter (EFA) network.

D : Implement SageMaker Model Parallelism (SMP) on a fleet of Amazon EC2 Spot Instances for the inference deployment. Use the lowest-price allocation strategy to ensure the real-time endpoint remains cost-effective during peak market hours.

E : Use Amazon SageMaker Serverless Inference for the training phase. Configure the MaxConcurrency parameter to parallelize the data processing across thousands of ephemeral Lambda-based compute environments to reduce costs.

Answer: B,C

Question 5

A company uses Amazon Bedrock to build a Retrieval Augmented Generation (RAG) system. The RAG system uses an Amazon Bedrock Knowledge Bases that is based on an Amazon S3 bucket as the data source for emergency news video content. The system retrieves transcripts, archived reports, and related documents from the S3 bucket. The RAG system uses state-of-the-art embedding models and a high-performing retrieval setup. However, users report slow responses and irrelevant results, which cause decreased user satisfaction. The company notices that vector searches are evaluating too many documents across too many content types and over long periods of time. The company determines that the underlying models will not benefit from additional fine-tuning. The company must improve retrieval accuracy by applying smarter constraints and wants a solution that requires minimal changes to the existing architecture. Which solution will meet these requirements?

A : Enhance embeddings by using a domain-adapted model that is specifically trained on emergency newscontent for improved vector similarity.

B : Migrate to Amazon OpenSearch Service. Use vector fields and metadata filters to define the scope ofresults retrieval.

C : Enable metadata-aware filtering within the Amazon Bedrock knowledge base by indexing S3 objectmetadata.

D : Migrate to an Amazon Q Business index to perform structured metadata filtering and documentcategorization during retrieval.

Answer: C

100% Free AIP-C01 Practice Exam Questions (2026)