AIF-C01関連復習問題集、AIF-C01問題と解答

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Amazon AIF-C01 Exam Syllabus Topics:

Section	Weight	Objectives
AI/ML Fundamentals	24%	- Identify foundational terminology and definitions 1. Explain training, inference, and fine-tuning 2. Describe underfitting and overfitting 3. Define models, algorithms, and parameters 4. Define common ML metrics (accuracy, precision, recall, F1 score) - Explain the fundamental concepts of AI and ML 1. Identify common use cases for AI/ML 2. Explain supervised and unsupervised learning 3. Differentiate between AI, ML, and deep learning 4. Define generative AI and its key concepts - Understand the AI/ML lifecycle 1. Identify phases of the ML lifecycle 2. Recognize data collection, preprocessing, and feature engineering stages 3. Describe model evaluation and deployment considerations
AI Application Development	30%	- Understand foundational concepts for building AI applications 1. Explain prompts, tokens, and context windows 2. Explain function calling and tool use in AI applications 3. Identify retrieval-augmented generation (RAG) concepts 4. Describe prompt engineering techniques - Implement AI applications using AWS services 1. Implement prompt templates and chain-of-thought reasoning 2. Use knowledge bases for context-aware responses 3. Build applications using Amazon Bedrock APIs and SDKs 4. Configure model parameters (temperature, top-p, top-k) - Evaluate and optimize AI applications 1. Assess application outputs for relevance and accuracy 2. Identify performance bottlenecks and optimization strategies 3. Apply evaluation frameworks for AI applications 4. Implement caching and cost optimization techniques
Responsible AI	20%	- Explain the principles of responsible AI 1. Define fairness, transparency, and privacy in AI systems 2. Identify potential biases in AI/ML models 3. Describe strategies for bias mitigation - Implement responsible AI best practices 1. Implement appropriate guardrails for AI applications 2. Evaluate AI outputs for quality and safety 3. Apply human oversight in AI decision-making - Understand governance and compliance requirements 1. Explain data privacy regulations affecting AI 2. Describe model interpretability and explainability 3. Recognize regulatory and ethical considerations
AWS AI Services Overview	26%	- Identify appropriate AWS AI services for given scenarios 1. Determine when to use pre-trained models vs. custom models 2. Compare AWS AI services by capability and use case 3. Evaluate use cases for Amazon Bedrock, SageMaker, and other AWS AI offerings - Describe Amazon Bedrock capabilities 1. Explain foundation models (FMs) available in Bedrock 2. Describe model invocation, prompts, and responses 3. Identify Bedrock features (agents, knowledge bases, guardrails) 4. Explain security, privacy, and compliance features - Describe AWS AI services for specific use cases 1. Identify services for NLP, computer vision, and recommendations 2. Explain AWS HealthScribe and other domain-specific services 3. Describe Amazon Polly, Rekognition, Transcribe, and Translate - Describe Amazon SageMaker capabilities 1. Identify SageMaker features for ML workflows 2. Describe built-in algorithms and SageMaker JumpStart 3. Explain model training and deployment options 4. Recognize MLOps capabilities in SageMaker

>> AIF-C01関連復習問題集 <<

Amazon AIF-C01問題と解答、AIF-C01日本語版問題集

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Amazon AWS Certified AI Practitioner 認定 AIF-C01 試験問題 (Q59-Q64):

質問 # 59
A food service company wants to collect a dataset to predict customer food preferences. The company wants to ensure that the food preferences of all demographics are included in the data.

A. Accuracy
B. Reliability
C. Recency bias
D. Diversity

正解：D

解説：
* Diversity in datasets ensures representation of all demographics, reducing bias and improving fairness.
* Accuracy is model performance.
* Recency bias skews towards recent data.
* Reliability measures consistency of results, not representation.
# Reference:
AWS Responsible AI Guidelines - Data Diversity

質問 # 60
An AI practitioner who has minimal ML knowledge wants to predict employee attrition without writing code. Which Amazon SageMaker feature meets this requirement?

A. SageMaker Model Monitor
B. SageMaker Data Wrangler
C. SageMaker Canvas
D. SageMaker Clarify

正解：C

解説：
The correct answer is A because Amazon SageMaker Canvas is designed specifically for users with little or no machine learning or programming experience. It provides a visual interface to build ML models by simply uploading data, performing analysis, and generating predictions using a no-code environment.
From the AWS documentation:
"Amazon SageMaker Canvas enables business analysts and other users to generate accurate ML predictions using a visual, point-and-click interface without writing code or having prior ML experience." This feature allows the user to:
Import datasets (e.g., HR data)
Automatically explore the data
Select the prediction column (e.g., attrition)
Train the model
Generate and export predictions
Explanation of other options:
B . SageMaker Clarify is used to detect bias and explain ML predictions but not to build models or make predictions without code.
C . SageMaker Model Monitor monitors model quality in production but doesn't build or train models.
D . SageMaker Data Wrangler is used for data preprocessing and transformation but still requires some technical configuration.
Referenced AWS AI/ML Documents and Study Guides:
Amazon SageMaker Canvas Developer Guide
AWS Certified Machine Learning Specialty Study Guide - AutoML and No-Code Tools Section AWS Machine Learning Blog: "Predict Employee Attrition with SageMaker Canvas"

質問 # 61
A software company wants to use a large language model (LLM) for workflow automation. The application will transform user messages into JSON files. The company will use the JSON files as inputs for data pipelines.
The company has a labeled dataset that contains user messages and output JSON files.
Which solution will train the LLM for workflow automation?

A. Unsupervised learning
B. Reinforcement learning from human feedback (RLHF)
C. Continued pre-training
D. Fine-tuning

正解：D

解説：
Fine-tuning is the process of training a pre-trained LLM with a labeled dataset specific to a desired task-in this case, mapping user messages to JSON outputs. Fine-tuning leverages supervised learning to specialize the model's outputs.
C is correct:
"Fine-tuning is a supervised learning approach in which a model is further trained on a custom, labeled dataset to adapt to a specific use case." (Reference: Amazon Bedrock Fine-Tuning, AWS Certified AI Practitioner Study Guide)
"Fine-tuning is a supervised learning approach in which a model is further trained on a custom, labeled dataset to adapt to a specific use case." (Reference: Amazon Bedrock Fine-Tuning, AWS Certified AI Practitioner Study Guide) A is incorrect-unsupervised learning does not use labeled data.
B (continued pre-training) uses unlabeled data.
D (RLHF) uses reward signals and human feedback, not direct labeled input/output pairs.

質問 # 62
A company wants to label training datasets by using human feedback to fine-tune a foundation model (FM). The company does not want to develop labeling applications or manage a labeling workforce.
Which AWS service or feature meets these requirements?

A. Amazon Transcribe
B. Amazon Macie
C. Amazon SageMaker Ground Truth Plus
D. Amazon SageMaker Data Wrangler

正解：C

解説：
* Amazon SageMaker Ground Truth Plus provides a fully managed data labeling service where AWS manages the workforce, tools, and processes.
* Data Wrangler is for data preparation and transformation.
* Transcribe is for speech-to-text.
* Macie is for sensitive data discovery, not labeling.
# Reference:
AWS Documentation - SageMaker Ground Truth Plus

質問 # 63
A company needs an automated solution to group Its customers Into multiple categories. The company does not want to manually define the categories.
Which ML technique should the company use?

A. Classification
B. Logistic regression
C. Linear regression
D. Clustering

正解：D

質問 # 64
......

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