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Amazon AIF-C01 Exam Syllabus Topics:

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

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Amazon AWS Certified AI Practitioner 認定 AIF-C01 試験問題 (Q164-Q169):

質問 # 164
An AI practitioner is using an Amazon Bedrock base model to summarize session chats from the customer service department. The AI practitioner wants to store invocation logs to monitor model input and output data.
Which strategy should the AI practitioner use?

A. Configure AWS Audit Manager as the logs destination for the model.
B. Enable invocation logging in Amazon Bedrock.
C. Configure AWS CloudTrail as the logs destination for the model.
D. Configure model invocation logging in Amazon EventBridge.

正解：B

解説：
Amazon Bedrock provides an option to enable invocation logging to capture and store the input and output data of the models used. This is essential for monitoring and auditing purposes, particularly when handling customer data.
Option B (Correct): "Enable invocation logging in Amazon Bedrock": This is the correct answer as it directly enables the logging of all model invocations, ensuring transparency and traceability.
Option A: "Configure AWS CloudTrail" is incorrect because CloudTrail logs API calls but does not provide specific logging for model inputs and outputs.
Option C: "Configure AWS Audit Manager" is incorrect as Audit Manager is used for compliance reporting, not specific invocation logging for AI models.
Option D: "Configure model invocation logging in Amazon EventBridge" is incorrect as EventBridge is for event-driven architectures, not specifically designed for logging AI model inputs and outputs.
AWS AI Practitioner Reference:
Amazon Bedrock Logging Capabilities: AWS emphasizes using built-in logging features in Bedrock to maintain data integrity and transparency in model operations.

質問 # 165
A company is developing an ML model to predict customer churn.
Which evaluation metric will assess the model's performance on a binary classification task such as predicting chum?

A. F1 score
B. Mean squared error (MSE)
C. R-squared
D. Time used to train the model

正解：A

解説：
The company is developing an ML model to predict customer churn, a binary classification task (churn or no churn). The F1 score is an evaluation metric that balances precision and recall, making it suitable for assessing the performance of binary classification models, especially when dealing with imbalanced datasets, which is common in churn prediction.
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"The F1 score is a metric for evaluating binary classification models, combining precision and recall into a single value. It is particularly useful for tasks like churn prediction, where class imbalance may exist, ensuring the model performs well on both positive and negative classes." (Source: Amazon SageMaker Developer Guide, Model Evaluation Metrics) Detailed Explanation:
* Option A: F1 scoreThis is the correct answer. The F1 score is ideal for binary classification tasks like churn prediction, as it measures the model's ability to correctly identify both churners and non-churners.
* Option B: Mean squared error (MSE)MSE is used for regression tasks to measure the average squared difference between predicted and actual values, not for binary classification.
* Option C: R-squaredR-squared is a metric for regression models, indicating how well the model explains the variability of the target variable. It is not applicable to classification tasks.
* Option D: Time used to train the modelTraining time is not an evaluation metric for model performance; it measures the duration of training, not the model's accuracy or effectiveness.
References:
Amazon SageMaker Developer Guide: Model Evaluation Metrics (https://docs.aws.amazon.com/sagemaker
/latest/dg/model-evaluation.html)
AWS AI Practitioner Learning Path: Module on Model Performance and Evaluation AWS Documentation: Metrics for Classification (https://aws.amazon.com/machine-learning/)

質問 # 166
A company needs to apply numerical transformations to a set of images to transpose and rotate the images.

A. Create a deep neural network by using the images as input.
B. Use an Amazon Bedrock large language model (LLM) with a high temperature.
C. Create an AWS Lambda function to perform the transformations.
D. Use AWS Glue Data Quality to make corrections to each image.

正解：C

解説：
The correct answer is B. AWS Lambda can efficiently perform image processing and transformations, such as rotating, resizing, or transposing, in a serverless and event-driven manner. According to AWS documentation, Lambda functions can trigger automatically when new images are uploaded to Amazon S3, perform necessary transformations using libraries like Pillow or OpenCV, and store the processed outputs. This approach minimizes infrastructure management and scales automatically. Deep neural networks (option A) are excessive for simple transformations, while LLMs (option C) and Glue Data Quality (option D) are unrelated-LLMs handle text and Glue is for tabular data validation. Lambda is the AWS-recommended service for lightweight, automated image preprocessing tasks.
Referenced AWS AI/ML Documents and Study Guides:
AWS Lambda Developer Guide - Image Processing Use Cases
AWS ML Specialty Guide - Preprocessing and Automation

質問 # 167
An AI practitioner is determining the appropriate data type for various use cases.
Select the correct data type from the following list for each use case. Select each data type one time.

正解：

解説：

Explanation:
Sentiment analysis # Text data
Traffic sign recognition # Image data
Customer demographics & purchase history # Tabular data
Stock price forecasting # Time series data
AWS classifies NLP tasks like sentiment analysis under text data
Computer vision tasks such as object and sign recognition use image data Structured rows and columns (demographics, transactions) are tabular data Sequential data indexed by time (prices, metrics) is time series data

質問 # 168
A company wants to implement a single environment for both data and AI development. Developers across different teams must be able to access the environment and work together. The developers must be able to build and share models and generative AI applications securely in the environment.
Which AWS solution will meet these requirements?

A. Amazon SageMaker Unified Studio
B. Amazon Bedrock PartyRock
C. Amazon Q Developer
D. Amazon Lex

正解：A

解説：
Amazon SageMaker Unified Studio provides a collaborative, secure, and centralized environment for end- to-end data, machine learning, and generative AI development. AWS documentation describes Unified Studio as a single interface where teams can prepare data, build models, train and deploy machine learning solutions, and develop generative AI applications.
In this use case, multiple teams must collaborate in a shared environment. SageMaker Unified Studio supports role-based access control, shared workspaces, and secure resource management, allowing developers to safely collaborate without compromising data or models. AWS highlights that Unified Studio integrates notebooks, pipelines, model development tools, and generative AI workflows into a consistent experience.
The service also supports model sharing, versioning, and reuse, enabling teams to build upon each other's work. This directly satisfies the requirement to build and share both traditional ML models and generative AI applications securely.
The other options are not suitable. Amazon Lex is a conversational AI service, not a development environment. Amazon Bedrock PartyRock is a no-code generative AI playground and is not intended for enterprise collaboration. Amazon Q Developer focuses on developer productivity and code assistance, not unified AI development environments.
AWS positions SageMaker Unified Studio as the foundation for collaborative AI development at scale, making it the correct choice.

質問 # 169
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