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Amazon AIF-C01 Exam Questions
- Topic 1: 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.
- Topic 2: 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 3: 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 4: 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 5: 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.
Free Amazon AIF-C01 Exam Actual Questions
Note: Premium Questions for AIF-C01 were last updated On Mar. 26, 2025 (see below)
A company wants to classify human genes into 20 categories based on gene characteristics. The company needs an ML algorithm to document how the inner mechanism of the model affects the output.
Which ML algorithm meets these requirements?
Decision trees are an interpretable machine learning algorithm that clearly documents the decision-making process by showing how each input feature affects the output. This transparency is particularly useful when explaining how the model arrives at a certain decision, making it suitable for classifying genes into categories.
Option A (Correct): 'Decision trees': This is the correct answer because decision trees provide a clear and interpretable representation of how input features influence the model's output, making it ideal for understanding the inner mechanisms affecting predictions.
Option B: 'Linear regression' is incorrect because it is used for regression tasks, not classification.
Option C: 'Logistic regression' is incorrect as it does not provide the same level of interpretability in documenting decision-making processes.
Option D: 'Neural networks' is incorrect because they are often considered 'black boxes' and do not easily explain how they arrive at their outputs.
AWS AI Practitioner Reference:
Interpretable Machine Learning Models on AWS: AWS supports using interpretable models, such as decision trees, for tasks that require clear documentation of how input data affects output decisions.
A company wants to make a chatbot to help customers. The chatbot will help solve technical problems without human intervention. The company chose a foundation model (FM) for the chatbot. The chatbot needs to produce responses that adhere to company tone.
Which solution meets these requirements?
Experimenting and refining the prompt is the best approach to ensure that the chatbot using a foundation model (FM) produces responses that adhere to the company's tone.
Prompt Engineering:
Adjusting and refining the prompt allows for better control over the FM's outputs, ensuring they align with the desired tone and style.
This iterative process involves testing different prompts and modifying them based on the model's responses to achieve the desired outcome.
Why Option C is Correct:
Directly Influences Output Quality: Allows for fine-tuning of the model's responses to match the company's tone.
Cost-Effective: Does not require modifying the model itself, only the inputs to it.
Why Other Options are Incorrect:
A . Low limit on tokens: Limits response length but not the adherence to company tone.
B . Batch inferencing: Relates to processing multiple inputs, not controlling response tone.
D . Higher temperature: Increases randomness in responses, which could deviate from the desired tone.
Which AWS feature records details about ML instance data for governance and reporting?
Amazon SageMaker Model Cards provide a centralized and standardized repository for documenting machine learning models. They capture key details such as the model's intended use, training and evaluation datasets, performance metrics, ethical considerations, and other relevant information. This documentation facilitates governance and reporting by ensuring that all stakeholders have access to consistent and comprehensive information about each model. While Amazon SageMaker Debugger is used for real-time debugging and monitoring during training, and Amazon SageMaker Model Monitor tracks deployed models for data and prediction quality, neither offers the comprehensive documentation capabilities of Model Cards. Amazon SageMaker JumpStart provides pre-built models and solutions but does not focus on governance documentation.
A student at a university is copying content from generative AI to write essays.
Which challenge of responsible generative AI does this scenario represent?
A company has thousands of customer support interactions per day and wants to analyze these interactions to identify frequently asked questions and develop insights.
Which AWS service can the company use to meet this requirement?
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