AIP-C01 Exam: Absolutely Free Online Practice

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
- A medical device company wants to feed reports of medical procedures that used the company’s devices intoan AI assistant. To protect patient privacy, the AI assistant must expose patient personally identifiableinformation (PII) only to surgeons. The AI assistant must redact PII for engineers. The AI assistant mustreference 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 hasalready set up an Amazon Bedrock Knowledge Bases. The AI assistant uses Amazon Cognito to authenticateusers.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 3
- 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 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 is developing a customer support application that uses Amazon Bedrock foundation models(FMs) to provide real-time AI assistance to the company’s employees. The application must display AIgenerated responses character by character as the responses are generated. The application needs to supportthousands of concurrent users with minimal latency. The responses typically take 15 to 45 seconds to finish.Which solution will meet these requirements?
  A : Configure an Amazon API Gateway WebSocket API with an AWS Lambda integration. Configure theWebSocket API to invoke the Amazon Bedrock InvokeModelWithResponseStream API and streampartial responses through WebSocket connections.
  
  B : Configure an Amazon API Gateway REST API with an AWS Lambda integration. Configure the RESTAPI to invoke the Amazon Bedrock standard InvokeModel API and implement frontend client-sidepolling every 100 ms for complete response chunks.
  
  C : Implement direct frontend client connections to Amazon Bedrock by using IAM user credentials andthe InvokeModelWithResponseStream API without any intermediate gateway or proxy layer.
  
  D : Configure an Amazon API Gateway HTTP API with an AWS Lambda integration. Configure theHTTP API to cache complete responses in an Amazon DynamoDB table and serve the responsesthrough multiple paginated GET requests to frontend clients.
  
  Answer: A

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