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NEW QUESTION # 354
A company needs to build its own large language model (LLM) based on only the company's private data.
The company is concerned about the environmental effect of the training process.
Which Amazon EC2 instance type has the LEAST environmental effect when training LLMs?
Answer: B
Explanation:
The Amazon EC2 Trn series (Trainium) instances are designed for high-performance, cost-effective machine learning training while being energy-efficient. AWS Trainium-powered instances are optimized for deep learning models and have been developed to minimize environmental impact by maximizing energy efficiency.
* Option D (Correct): "Amazon EC2 Trn series": This is the correct answer because the Trn series is purpose-built for training deep learning models with lower energy consumption, which aligns with the company's concern about environmental effects.
* Option A: "Amazon EC2 C series" is incorrect because it is intended for compute-intensive tasks but not specifically optimized for ML training with environmental considerations.
* Option B: "Amazon EC2 G series" (Graphics Processing Unit instances) is optimized for graphics- intensive applications but does not focus on minimizing environmental impact for training.
* Option C: "Amazon EC2 P series" is designed for ML training but does not offer the same level of energy efficiency as the Trn series.
AWS AI Practitioner References:
* AWS Trainium Overview: AWS promotes Trainium instances as their most energy-efficient and cost- effective solution for ML model training.
NEW QUESTION # 355
A student at a university is copying content from generative AI to write essays.
Which challenge of responsible generative AI does this scenario represent?
Answer: B
NEW QUESTION # 356
A company uses a third-party model on Amazon Bedrock to analyze confidential documents. The company is concerned about data privacy. Which statement describes how Amazon Bedrock protects data privacy?
Answer: A
Explanation:
Comprehensive and Detailed Explanation from AWS AI Documents:
Amazon Bedrock ensures data privacy and security by not sharing customer inputs or outputs with third-party model providers.
The models are accessed via Bedrock's API isolation layer, meaning that model providers do not see your data.
Customer data is not used for training or improving foundation models unless customers explicitly opt in.
From AWS Docs:
"Amazon Bedrock does not share your inputs and outputs with third-party model providers. Your data remains private, and is not used to improve the foundation models." This ensures full data privacy, especially for sensitive use cases like confidential documents.
Reference:
AWS Documentation - Data privacy in Amazon Bedrock
NEW QUESTION # 357
An AI company periodically evaluates its systems and processes with the help of independent software vendors (ISVs). The company needs to receive email message notifications when an ISV's compliance reports become available.
Which AWS service can the company use to meet this requirement?
Answer: A
Explanation:
AWS Data Exchange is a service that allows companies to securely exchange data with third parties, such as independent software vendors (ISVs). AWS Data Exchange can be configured to provide notifications, including email notifications, when new datasets or compliance reports become available.
* Option D (Correct): "AWS Data Exchange": This is the correct answer because it enables the company to receive notifications, including email messages, when ISVs' compliance reports are available.
* Option A: "AWS Audit Manager" is incorrect because it focuses on assessing an organization's own compliance, not receiving third-party compliance reports.
* Option B: "AWS Artifact" is incorrect as it provides access to AWS's compliance reports, not ISVs'.
* Option C: "AWS Trusted Advisor" is incorrect as it offers optimization and best practices guidance, not compliance report notifications.
AWS AI Practitioner References:
* AWS Data Exchange Documentation: AWS explains how Data Exchange allows organizations to subscribe to third-party data and receive notifications when updates are available.
NEW QUESTION # 358
A real estate company is developing an ML model to predict house prices by using sales and marketing data.
The company wants to use feature engineering to build a model that makes accurate predictions.
Which approach will meet these requirements?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Feature engineering focuses on:
* Creating new features
* Selecting the most relevant existing features
* Improving model signal and accuracy
AWS ML best practices identify feature engineering as a key driver of predictive performance.
Why the other options are incorrect:
* Visualization (A) helps understanding, not feature creation.
* Hyperparameter tuning (B) optimizes models, not features.
* Data collection (D) expands datasets but does not engineer features.
AWS AI document references:
* Feature Engineering Best Practices
* Improving Model Accuracy on AWS
* ML Model Development Lifecycle
NEW QUESTION # 359
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