AIP-C01 Exam: Absolutely Free Online Practice

Question 1
- An insurance company has deployed a Generative AI chatbot using Amazon Bedrock to explain policy details to customers. To comply with strict financial regulations, the solution requires a continuous monitoring and automated remediation system for two specific governance issues:Misuse Remediation: If a specific user triggers the "Hate Speech" content filter in Amazon Bedrock Guardrails more than 5 times in a single minute, their access must be automatically and immediately suspended.Bias Monitoring: The system must strictly monitor the model's responses over time to detect if the terminology becomes statistically biased against specific demographics compared to a pre-established "Golden Set" baseline.Which combination of mechanisms satisfies these requirements?
  A : Implement a scheduled AWS Step Functions workflow that uses Amazon SageMaker Clarify to analyze the aggregated inference logs stored in Amazon S3. Configure the job to calculate bias metrics against the baseline and publish findings to CloudWatch.
  
  B : Deploy Amazon SageMaker Model Monitor on the Amazon Bedrock endpoint. Enable the "Real-time Concept Drift" feature to block any inference response that deviates more than 5% from the training data distribution.
  
  C : Configure an Amazon EventBridge Rule to match the aws.bedrock.GuardrailIntervention event pattern. Set the target as an AWS Systems Manager Run Command to disable the user's account in the identity provider.
  
  D : Enable Amazon Macie on the bucket containing the inference logs. configure a custom data identifier for "Biased Terminology" and set up an automated remediation job to delete non-compliant logs.
  
  E : Create an Amazon CloudWatch Alarm that monitors the GuardrailInterventionCount metric with a distinct dimension for the user ID. Configure the alarm action to trigger an AWS Lambda function that revokes the user's IAM permissions or API keys.
  
  Answer: A,E
Question 2
- A bank uses an AWS CodePipeline to deploy a GenAI loan approval application. The application consists of an Amazon Bedrock Knowledge Base and a set of Lambda functions. The compliance team requires that before any new Knowledge Base configuration (e.g., chunking strategy change) is promoted to Production, it must pass a "Golden Set" evaluation.If the evaluation score (F1 score) is below 0.85, the pipeline must automatically rollback.Which solution meets these requirements?
  A : Use Amazon Inspector within the pipeline to scan the Knowledge Base configuration for misconfigurations and vulnerabilities.
  
  B : Add a Manual Approval action to the pipeline. Configure an Amazon SNS notification to the compliance team with a link to the Bedrock Console so they can manually test the agent and click "Approve."
  
  C : Add a Test Stage to the pipeline. Use AWS CodeBuild to run a script that invokes the new Knowledge Base Retrieve API against the golden questions. Calculate the score and return a non-zero exit code if it fails, causing the pipeline to stop.
  
  D : Use AWS CloudFormation Guard. Define a policy that checks the VectorIndexConfiguration parameter in the template. Fail the stack deployment if the chunking strategy has changed.
  
  Answer: C
Question 3
- A financial services company wants to develop an Amazon Bedrock application that gives analysts the ability to query quarterly earnings reports and financial statements. The financial documents are typically 5–100 pages long and contain both tabular data and text. The application must provide contextually accurate responses that preserve the relationship between financial metrics and their explanatory text. To support accurate and scalable retrieval, the application must incorporate document segmentation and context management strategies.Which solution will meet these requirements?
  A : Use a direct model invocation approach that uses Anthropic Claude to process each financial documentas a single input. Use fine-tuned prompts that instruct the model to parse tables and text separately.
  
  B : Use Amazon Bedrock Knowledge Bases to create a Retrieval Augmented Generation (RAG)application that retrieves relevant information from contextually chunked sections of financialdocuments. Segment documents based on their structural layout. Include citations that reference theoriginal source materials.
  
  C : Deploy an Amazon Bedrock agent that has an action group that calls custom AWS Lambda functions toanalyze financial documents. Configure the Lambda functions to perform fixed-size chunking when auser submits a query about financial metrics.
  
  D : Create one specialized Amazon Bedrock application that is optimized for structured data. Create asecond application that is optimized for unstructured data. Configure each application to use a tailoredchunking strategy that is suited to the application's content type. Implement logic to link queries to theappropriate sources.
  
  Answer: B
Question 4
- A financial services company has 15 development teams building different microservices using Amazon Bedrock. Code reviews reveal inconsistent error handling patterns, varying retry logic implementations, and different approaches to streaming responses across services. The architecture team defined standard Bedrock integration patterns, but manual enforcement through code reviews is slow and inconsistent. The company needs a solution that automatically identifies non-standard implementations and helps developers refactor code to match approved patterns across the entire multi-repository codebase.Which solution enforces consistent integration patterns MOST effectively?
  A : Create an AWS CodeCommit repository with approved code templates and integration examples. Distribute the repository link to all teams with instructions to reference it when implementing Bedrock features.
  
  B : Implement pre-commit hooks in Git that run custom Python scripts to validate Bedrock API calls against a JSON schema defining allowed patterns before allowing commits.
  
  C : Deploy SonarQube on Amazon EC2 to perform static code analysis. Configure custom rules that flag non-standard Bedrock integration patterns and generate reports for teams to manually fix.
  
  D : Configure Amazon Q Developer to scan the codebase, identify Bedrock integration anti-patterns, generate refactored code following company standards, and apply the consistent changes across all microservice repositories.
  
  Answer: D
Question 5
- A company needs a system to automatically generate study materials from multiple content sources. Thecontent sources include document files (PDF files, PowerPoint presentations, and Word documents) andmultimedia files (recorded videos). The system must process more than 10,000 content sources daily withpeak loads of 500 concurrent uploads. The system must also extract key concepts from document files andmultimedia files and create contextually accurate summaries. The generated study materials must support realtime collaboration with version control.Which solution will meet these requirements?
  A : Use Amazon Bedrock Data Automation (BDA) with AWS Lambda functions to orchestrate documentfile processing. Use Amazon Bedrock Knowledge Bases to process all multimedia. Store the content inAmazon DocumentDB with replication. Collaborate by using Amazon SNS topic subscriptions. Trackchanges by using Amazon Bedrock Agents.
  
  B : Use Amazon Bedrock Data Automation (BDA) with foundation models (FMs) to process documentfiles. Integrate BDA with Amazon Textract for PDF extraction and with Amazon Transcribe formultimedia files. Store the processed content in Amazon S3 with versioning enabled. Store themetadata in Amazon DynamoDB. Collaborate in real time by using AWS AppSync GraphQLsubscriptions and DynamoDB.
  
  C : Use Amazon Bedrock Data Automation (BDA) with Amazon SageMaker AI endpoints to host contentextraction and summarization models. Use Amazon Bedrock Guardrails to extract content from all filetypes. Store document files in Amazon Neptune for time series analysis. Collaborate by using AmazonBedrock Chat for real-time messaging.
  
  D : Use Amazon Bedrock Data Automation (BDA) with AWS Lambda functions to process batches ofcontent files. Fine-tune foundation models (FMs) in Amazon Bedrock to classify documents across allcontent types. Store the processed data in Amazon ElastiCache (Redis OSS) by using Cluster Modewith sharding. Use Prompt management in Amazon Bedrock for version control.
  
  Answer: B

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