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質問 # 190
A company has an ML model. The company wants to know how the model makes predictions. Which term refers to understanding model predictions?
正解：B
解説：
The correct answer is A because model interpretability refers to the ability to understand and explain how an ML model arrives at a particular prediction or decision.
From AWS documentation:
"Model interpretability is the degree to which a human can understand the cause of a decision made by a machine learning model. Interpretability techniques help explain which features influenced a prediction and how much they contributed." This is essential in areas like financial services, healthcare, or compliance-heavy industries, where decision transparency is critical.
Explanation of other options:
B . Model training refers to the process of teaching a model from data and doesn't explain how predictions are made.
C . Model interoperability refers to the ability of systems or models to work across different platforms or environments.
D . Model performance refers to how accurate or effective the model is but doesn't relate to the explanation of its decisions.
Referenced AWS AI/ML Documents and Study Guides:
Amazon SageMaker Clarify Documentation - Explainability and Bias Detection AWS Machine Learning Specialty Study Guide - Responsible AI and Model Explainability
質問 # 191
A company wants to develop ML applications to improve business operations and efficiency.
Select the correct ML paradigm from the following list for each use case. Each ML paradigm should be selected one or more times. (Select FOUR.)
* Supervised learning
* Unsupervised learning
正解：
解説：
Explanation:
The company is developing ML applications for various use cases, and the task is to select the correct ML paradigm (supervised or unsupervised learning) for each. Supervised learning involves training a model on labeled data to make predictions, while unsupervised learning identifies patterns or structures in unlabeled data. Each use case aligns with one of these paradigms based on its requirements.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"Supervised learning uses labeled data to train models for tasks like classification (e.g., binary or multi-class classification), where the model predicts a category. Unsupervised learning works with unlabeled data for tasks like clustering (e.g., K-means clustering) or dimensionality reduction, identifying patternsor reducing data complexity without predefined labels." (Source: AWS AI Practitioner Learning Path, Module on Machine Learning Strategies) Detailed Explanation:
Binary classification: Supervised learningBinary classification involves predicting one of two classes (e.g., yes
/no, spam/not spam) using labeled data, making it a supervised learning task. The model learns from examples where the correct class is provided.
Multi-class classification: Supervised learningMulti-class classification extends binary classification to predict one of multiple classes (e.g., categorizing items into several groups). Like binary classification, it requires labeled data, so it falls under supervised learning.
K-means clustering: Unsupervised learningK-means clustering groups data into clusters based on similarity, without requiring labeled data. This is a classic unsupervised learning task, as the algorithm identifies patterns in the data on its own.
Dimensionality reduction: Unsupervised learningDimensionality reduction (e.g., using techniques like PCA) reduces the number of features in a dataset while preserving important information. It does not require labeled data, making it an unsupervised learning task.
Hotspot Selection Analysis:
The hotspot lists four use cases, each with a dropdown containing "Select...," "Supervised learning," and
"Unsupervised learning." The correct selections are:
Binary classification: Supervised learning
Multi-class classification: Supervised learning
K-means clustering: Unsupervised learning
Dimensionality reduction: Unsupervised learning
Each paradigm (supervised and unsupervised learning) is used twice, as the question allows for paradigms to be selected one or more times.
References:
AWS AI Practitioner Learning Path: Module on Machine Learning Strategies Amazon SageMaker Developer Guide: Supervised and Unsupervised Learning (https://docs.aws.amazon.com
/sagemaker/latest/dg/algos.html)
AWS Documentation: Introduction to Machine Learning Paradigms (https://aws.amazon.com/machine- learning/)
質問 # 192
Why does overfilting occur in ML models?
正解：D
解説：
Overfitting occurs when an ML model learns the training data too well, including noise and patterns that do not generalize to new data. A key cause of overfitting is when the training dataset does not represent all possible input values, leading the model to over-specialize on the limited data it was trained on, failing to generalize to unseen data.
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"Overfitting often occurs when the training dataset is not representative of the broader population of possible inputs, causing the model to memorize specific patterns, including noise, rather than learning generalizable features." (Source: Amazon SageMaker Developer Guide, Model Evaluation and Overfitting) Detailed Explanation:
* Option A: The training dataset does not represent all possible input values.This is the correct answer. If the training dataset lacks diversity and does not cover the range of possible inputs, the model overfits by learning patterns specific to the training data, failing to generalize.
* Option B: The model contains a regularization method.Regularization methods (e.g., L2 regularization) are used to prevent overfitting, not cause it. This option is incorrect.
* Option C: The model training stops early because of an early stopping criterion.Early stopping is a technique to prevent overfitting by halting training when performance on a validation set degrades. It does not cause overfitting.
* Option D: The training dataset contains too many features.While too many features can contribute to overfitting (e.g., by increasing model complexity), this is less directly tied to overfitting than a non- representative dataset. The dataset's representativeness is the primary cause.
References:
Amazon SageMaker Developer Guide: Model Evaluation and Overfitting (https://docs.aws.amazon.com
/sagemaker/latest/dg/model-evaluation.html)
AWS AI Practitioner Learning Path: Module on Model Performance and Evaluation AWS Documentation: Understanding Overfitting (https://aws.amazon.com/machine-learning/)
質問 # 193
A loan company is building a generative AI-based solution to offer new applicants discounts based on specific business criteri a. The company wants to build and use an AI model responsibly to minimize bias that could negatively affect some customers.
Which actions should the company take to meet these requirements? (Select TWO.)
正解：C、D
質問 # 194
A company wants to improve multiple ML models.
Select the correct technique from the following list of use cases. Each technique should be selected one time or not at all. (Select THREE.) Few-shot learning Fine-tuning Retrieval Augmented Generation (RAG) Zero-shot learning
正解：
解説：
質問 # 195
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