AIF-C01 Bedrock Practice Questions
The free AIF-C01: AWS Certified AI Practitioner questions that deal with bedrock, with answers and explanations. The full bank and the timed practice test cover every topic the exam asks about.
Question #1
A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company wants to know how much information can fit into one prompt. Which consideration will inform the company's decision?
Correct answer: B
Explanation
The context window defines the maximum number of tokens a model can accept in a single prompt, so it determines how much information fits in one prompt. Temperature, batch size and model size do not set that limit.
Question #6
A medical company deployed a disease detection model on Amazon Bedrock. To comply with privacy policies, the company wants to prevent the model from including personal patient information in its responses. The company also wants to receive notification when policy violations occur. Which solution meets these requirements?
Correct answer: C
Explanation
Guardrails for Amazon Bedrock provide mechanisms to filter and control the content generated by models to comply with privacy and policy requirements. Using guardrails ensures that sensitive or personal information is not included in the model's responses. Additionally, integrating Amazon CloudWatch alarms allows for real-time notification when a policy violation occurs. • Option C (Correct): "Use Guardrails for Amazon Bedrock to filter content. Set up Amazon CloudWatch alarms for notification of policy violations": This is the correct answer because it directly addresses both the prevention of policy violations and the requirement to receive notifications when such violations occur. • Option A: "Use Amazon Macie to scan the model's output for sensitive data" is incorrect because Amazon Macie is designed to monitor data in S3, not to filter real-time model outputs. • Option B: "Configure AWS CloudTrail to monitor the model's responses" is incorrect because CloudTrail tracks API activity and is not suited for content moderation. • Option D: "Implement Amazon SageMaker Model Monitor to detect data drift" is incorrect because data drift detection does not address content moderation or privacy compliance. AWS AI Practitioner References: • Guardrails in Amazon Bedrock: AWS provides guardrails to ensure AI models comply with content policies, and using CloudWatch for alerting integrates monitoring capabilities.
Question #7
How can companies use large language models (LLMs) securely on Amazon Bedrock?
Correct answer: A
Explanation
To securely use large language models (LLMs) on Amazon Bedrock, companies should design clear and specific prompts to avoid unintended outputs and ensure proper configuration of AWS Identity and Access Management (IAM) roles and policies with the principle of least privilege. This approach limits access to sensitive resources and minimizes the potential impact of security incidents. • Option A (Correct): "Design clear and specific prompts. Configure AWS Identity and Access Management (IAM) roles and policies by using least privilege access": This is the correct answer as it directly addresses both security practices in prompt design and access management. • Option B: "Enable AWS Audit Manager for automatic model evaluation jobs" is incorrect because Audit Manager is for compliance and auditing, not directly related to secure LLM usage. • Option C: "Enable Amazon Bedrock automatic model evaluation jobs" is incorrect because Bedrock does not provide automatic model evaluation jobs specifically for security purposes. • Option D: "Use Amazon CloudWatch Logs to make models explainable and to monitor for bias" is incorrect because CloudWatch Logs are used for monitoring and not directly for making models explainable or secure. AWS AI Practitioner References: • Secure AI Practices on AWS: AWS recommends configuring IAM roles and using least privilege access to ensure secure usage of AI models.
Question #10
A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality. Which action must the company take to use the custom model through Amazon Bedrock?
Correct answer: A
Explanation
Amazon Bedrock custom models cannot be used on demand; the company must purchase Provisioned Throughput for the custom model before invoking it. SageMaker endpoints and Model Registry are outside Bedrock, and access grants apply to base models.
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