Free Amazon AIP-C01 Exam Questions

Question 1

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 2

A company is building an AI advisory application by using Amazon Bedrock. The application will providerecommendations to customers. The company needs the application to explain its reasoning process and citespecific sources for data. The application must retrieve information from company data sources and show stepby-step reasoning for recommendations. The application must also link data claims to source documents andmaintain response latency under 3 seconds.Which solution will meet these requirements with the LEAST operational overhead?

A : Use Amazon Bedrock Knowledge Bases with source attribution enabled. Use the Anthropic ClaudeMessages API with RAG to set high-relevance thresholds for source documents. Store reasoning andcitations in Amazon S3 for auditing purposes.

B : Use Amazon Bedrock with Anthropic Claude models and extended thinking. Configure a 4,000-tokenthinking budget. Store reasoning traces and citations in Amazon DynamoDB for auditing purposes.

C : Configure Amazon SageMaker AI with a custom Anthropic Claude model. Use the model’s reasoningparameter and AWS Lambda to process responses. Add source citations from a separate Amazon RDSdatabase.

D : Use Amazon Bedrock with Anthropic Claude models and chain-of-thought reasoning. Configurecustom retrieval tracking with the Amazon Bedrock Knowledge Bases API. Use Amazon CloudWatchto monitor response latency metrics.

Answer: A

Question 3

A financial services company uses Amazon Bedrock to generate investment analysis reports. The prompt engineering team frequently updates prompts to improve output quality, but recent production incidents showed that prompt changes caused incorrect financial calculations.The company requires a solution that enables prompt versioning with the ability to rollback to previous versions, tracks which prompt version generated each report for audit compliance, sends alerts when prompt performance degrades (measured by downstream validation failures), and maintains a complete audit trail of all prompt changes for regulatory reviews.Which solution meets ALL these requirements with the LEAST operational overhead?

A : Use Bedrock Prompt Management to create and version prompts. Store each prompt version with metadata tags indicating the version number. Configure CloudWatch Logs to capture all Bedrock InvokeModel requests with prompt details. Create a Lambda function that parses CloudWatch Logs, detects validation failures, and publishes alerts to SNS. Store prompt change history in DynamoDB for audit reports.

B : Use Bedrock Prompt Management to create versioned prompts with immutable versions and aliases. Tag each report with the prompt version/alias used during generation and log this to CloudWatch Logs. Enable CloudTrail to capture all Bedrock API calls including prompt updates. Create CloudWatch metric filters on validation failure logs and configure CloudWatch Alarms to trigger SNS notifications when failure rates exceed thresholds. Use CloudTrail Event History and CloudWatch Logs Insights for audit trail queries.

C : Store prompts as text files in S3 with version control enabled. Implement a Lambda-based prompt versioning system that reads prompts from S3, tracks versions in DynamoDB, and passes prompts to Bedrock InvokeModel API. Configure S3 Event Notifications to trigger Lambda functions that log prompt changes to CloudWatch. Use CloudWatch Synthetics to test prompt quality and send alerts via SNS when tests fail.

D : Use AWS Systems Manager Parameter Store to manage prompts with versioning enabled. Create a Step Functions workflow that retrieves the prompt version from Parameter Store, invokes Bedrock, and logs the prompt version and output to CloudWatch Logs. Enable Parameter Store change notifications via EventBridge to track prompt updates. Create CloudWatch Alarms based on custom metrics published by the Step Functions workflow when validation failures occur.

Answer: B

Question 4

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 5

A fintech company is launching a GenAI-powered "Spending Analyzer" feature. The application uses a Large Language Model (LLM) on Amazon Bedrock to explain transaction history to users. The project has two critical governance requirements:Compliance: The model must strictly refuse to answer any questions related to "Tax Advice" or "Legal Liability," as the company is not licensed to provide these services.Agility: The non-technical Product Team needs the ability to update the "Persona" and "Output Format" (e.g., switching from paragraphs to bullet points) of the analyzer weekly without requiring the engineering team to deploy new application code.Which combination of AWS GenAI features should the developer implement to meet these requirements? (Choose two)

A : Fine-tune the Foundation Model using Amazon SageMaker JumpStart on a dataset of "Refusal" examples. Train the model to recognize tax-related questions and output a standard disclaimer.

B : Implement Amazon Bedrock Prompt Management. Create a parameterized prompt template that defines the System Prompt (Persona) and output structure. Grant the Product Team permissions to iterate, test, and version the prompt within the Bedrock console.

C : Implement a post-processing AWS Lambda function. Use a keyword list (e.g., "tax", "audit", "lawyer") to scan the generated text. If a restricted keyword is found, overwrite the response with a hardcoded error message.

D : Use AWS Systems Manager Parameter Store to hold the System Prompt string. Update the backend Lambda function to fetch the latest prompt version on every invocation. Give the Product Team access to the Systems Manager console to edit the string.

E : Create an Amazon Bedrock Guardrail. Configure a "Denied Topics" policy that explicitly defines "Tax Advice" and "Legal Advice" with description examples. Attach this guardrail to the model invocation to intercept and block restricted queries.

Answer: B,E