FREE PDF QUIZ 2025 AIF-C01: AWS CERTIFIED AI PRACTITIONER–HIGH PASS-RATE RELIABLE EXAM PATTERN

Free PDF Quiz 2025 AIF-C01: AWS Certified AI Practitioner–High Pass-Rate Reliable Exam Pattern

Free PDF Quiz 2025 AIF-C01: AWS Certified AI Practitioner–High Pass-Rate Reliable Exam Pattern

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Amazon AIF-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Security, Compliance, and Governance for AI Solutions: This domain covers the security measures, compliance requirements, and governance practices essential for managing AI solutions. It targets security professionals, compliance officers, and IT managers responsible for safeguarding AI systems, ensuring regulatory compliance, and implementing effective governance frameworks.
Topic 2
  • Applications of Foundation Models: This domain examines how foundation models, like large language models, are used in practical applications. It is designed for those who need to understand the real-world implementation of these models, including solution architects and data engineers who work with AI technologies to solve complex problems.
Topic 3
  • Guidelines for Responsible AI: This domain highlights the ethical considerations and best practices for deploying AI solutions responsibly, including ensuring fairness and transparency. It is aimed at AI practitioners, including data scientists and compliance officers, who are involved in the development and deployment of AI systems and need to adhere to ethical standards.
Topic 4
  • Fundamentals of Generative AI: This domain explores the basics of generative AI, focusing on techniques for creating new content from learned patterns, including text and image generation. It targets professionals interested in understanding generative models, such as developers and researchers in AI.
Topic 5
  • Fundamentals of AI and ML: This domain covers the fundamental concepts of artificial intelligence (AI) and machine learning (ML), including core algorithms and principles. It is aimed at individuals new to AI and ML, such as entry-level data scientists and IT professionals.

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Amazon AWS Certified AI Practitioner Sample Questions (Q46-Q51):

NEW QUESTION # 46
How can companies use large language models (LLMs) securely on Amazon Bedrock?

  • A. Enable AWS Audit Manager for automatic model evaluation jobs.
  • B. Enable Amazon Bedrock automatic model evaluation jobs.
  • C. Use Amazon CloudWatch Logs to make models explainable and to monitor for bias.
  • D. Design clear and specific prompts. Configure AWS Identity and Access Management (IAM) roles and policies by using least privilege access.

Answer: D


NEW QUESTION # 47
A company wants to build an ML model by using Amazon SageMaker. The company needs to share and manage variables for model development across multiple teams.
Which SageMaker feature meets these requirements?

  • A. Amazon SageMaker Data Wrangler
  • B. Amazon SageMaker Model Cards
  • C. Amazon SageMaker Clarify
  • D. Amazon SageMaker Feature Store

Answer: D


NEW QUESTION # 48
A company has built an image classification model to predict plant diseases from photos of plant leaves. The company wants to evaluate how many images the model classified correctly.
Which evaluation metric should the company use to measure the model's performance?

  • A. Learning rate
  • B. Accuracy
  • C. R-squared score
  • D. Root mean squared error (RMSE)

Answer: B


NEW QUESTION # 49
A company is using a pre-trained large language model (LLM) to build a chatbot for product recommendations. The company needs the LLM outputs to be short and written in a specific language.
Which solution will align the LLM response quality with the company's expectations?

  • A. Adjust the prompt.
  • B. Choose an LLM of a different size.
  • C. Increase the temperature.
  • D. Increase the Top K value.

Answer: A


NEW QUESTION # 50
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?

  • A. Configure AWS CloudTrail to monitor the model's responses and create alerts for any detected personal information.
  • B. Use Amazon Macie to scan the model's output for sensitive data and set up alerts for potential violations.
  • C. Implement Amazon SageMaker Model Monitor to detect data drift and receive alerts when model quality degrades.
  • D. Use Guardrails for Amazon Bedrock to filter content. Set up Amazon CloudWatch alarms for notification of policy violations.

Answer: D

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.


NEW QUESTION # 51
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