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質問 # 97
A student at a university is copying content from generative AI to write essays.
Which challenge of responsible generative AI does this scenario represent?
正解：B
質問 # 98
Why does overfilting occur in ML models?
正解：B
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 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.
Reference:
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 # 99
Which type of ML technique provides the MOST explainability?
正解：D
質問 # 100
A financial institution is building an AI solution to make loan approval decisions by using a foundation model (FM). For security and audit purposes, the company needs the AI solution's decisions to be explainable.
Which factor relates to the explainability of the AI solution's decisions?
正解：C
解説：
The financial institution needs an AI solution for loan approval decisions to be explainable for security and audit purposes. Explainability refers to the ability to understand and interpret how a model makes decisions. Model complexity directly impacts explainability: simpler models (e.g., logistic regression) are more interpretable, while complex models (e.g., deep neural networks) are harder to explain, often behaving like "black boxes." Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"Model complexity affects the explainability of AI solutions. Simpler models, such as linear regression, are inherently more interpretable, while complex models, such as deep neural networks, may require additional tools like SageMaker Clarify to provide insights into their decision-making processes." (Source: Amazon SageMaker Developer Guide, Explainability with SageMaker Clarify) Detailed Option A: Model complexityThis is the correct answer. The complexity of the model directly influences how easily its decisions can be explained, a critical factor for audit and security purposes in loan approvals.
Option B: Training timeTraining time refers to how long it takes to train the model, which does not directly impact the explainability of its decisions.
Option C: Number of hyperparametersWhile hyperparameters affect model performance, they do not directly relate to explainability. A model with many hyperparameters might still be explainable if it is a simple model.
Option D: Deployment timeDeployment time refers to the time taken to deploy the model to production, which is unrelated to the explainability of its decisions.
Reference:
Amazon SageMaker Developer Guide: Explainability with SageMaker Clarify (https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-explainability.html) AWS AI Practitioner Learning Path: Module on Responsible AI and Explainability AWS Documentation: Explainable AI (https://aws.amazon.com/machine-learning/responsible-ai/)
質問 # 101
A company wants to develop a large language model (LLM) application by using Amazon Bedrock and customer data that is uploaded to Amazon S3. The company's security policy states that each team can access data for only the team's own customers.
Which solution will meet these requirements?
正解：B
解説：
To comply with the company's security policy, which restricts each team to access data for only their own customers, creating an Amazon Bedrock custom service role for each team is the correct solution.
* Custom Service Role Per Team:
* A custom service role for each team ensures that the access control is granular, allowing only specific teams to access their own customer data in Amazon S3.
* This setup aligns with the principle of least privilege, ensuring teams can only interact with data they are authorized to access.
* Why Option A is Correct:
* Access Control: Allows precise access permissions for each team's data.
* Security Compliance: Directly meets the company's security policy requirements by ensuring data segregation.
* Why Other Options are Incorrect:
* B. Custom service role with customer name specification: This approach is impractical as it relies on manual input, which is prone to errors and does not inherently enforce data access controls.
* C. Redacting personal data and updating S3 bucket policy: Redaction does not solve the requirement for team-specific access, and updating bucket policies is less granular than creating roles.
* D. One Bedrock role with full S3 access and IAM roles for teams: This setup does not meet the least privilege principle, as having a single role with full access is contrary to the company's security policy.
Thus, A is the correct answer to meet the company's security requirements.
質問 # 102
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