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질문 # 323
Which option is a characteristic of AI governance frameworks for building trust and deploying human-centered AI technologies?
정답：B
설명：
AI governance frameworks aim to build trust and deploy human-centered AI technologies by establishing guidelines and policies for data usage, transparency, responsible AI practices, and compliance with regulations. This ensures ethical and accountable AI development and deployment.
Exact Extract from AWS AI Documents:
From the AWS Documentation on Responsible AI:
"AI governance frameworks establish trust in AI technologies by developing policies and guidelines for data management, transparency, responsible AI practices, and compliance with regulatory requirements, ensuring human-centered and ethical AI deployment." (Source: AWS Documentation, Responsible AI Governance) Detailed Option A: Expanding initiatives across business units to create long-term business valueWhile expanding initiatives can drive value, it is not a core characteristic of AI governance frameworks focused on trust and human-centered AI.
Option B: Ensuring alignment with business standards, revenue goals, and stakeholder expectationsAlignment with business goals is important but not specific to AI governance frameworks for building trust and ethical AI deployment.
Option C: Overcoming challenges to drive business transformation and growthOvercoming challenges is a general business goal, not a defining characteristic of AI governance frameworks.
Option D: Developing policies and guidelines for data, transparency, responsible AI, and complianceThis is the correct answer. This option directly describes the core components of AI governance frameworks that ensure trust and ethical AI deployment.
Reference:
AWS Documentation: Responsible AI Governance (https://aws.amazon.com/machine-learning/responsible-ai/) AWS AI Practitioner Learning Path: Module on AI Governance AWS Well-Architected Framework: Machine Learning Lens (https://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/)
질문 # 324
An AI practitioner is developing a new ML model. After training the model, the AI practitioner evaluates the accuracy of the model's predictions. The model's accuracy is low when the model uses both the training dataset and the test dataset.
Which scenario is the MOST likely cause of this problem?
정답：D
설명：
Underfitting occurs when a machine learning model is too simple to capture the underlying patterns in the training data. AWS documentation explains that an underfit model performs poorly on both training and test datasets, which directly matches the scenario described.
In this case, the model shows low accuracy during training and evaluation, indicating that it has not learned sufficient relationships from the data. AWS identifies common causes of underfitting as insufficient model complexity, inadequate feature representation, overly aggressive regularization, or insufficient training time.
Underfitting is different from overfitting. Overfitting occurs when a model performs well on training data but poorly on test data, which is not the situation here. Hallucination applies to generative AI outputs, not supervised ML model accuracy. Cross-validation is a model evaluation technique, not a cause of poor performance.
AWS emphasizes the importance of diagnosing underfitting early in the model development lifecycle.
Remedies include increasing model complexity, adding relevant features, reducing regularization, or selecting a more expressive algorithm. These steps allow the model to better learn from the data and improve accuracy across both training and test sets.
AWS machine learning best practices clearly associate low performance on both datasets with underfitting, making this the most likely cause of the problem described.
질문 # 325
A company is building a mobile app for users who have a visual impairment. The app must be able to hear what users say and provide voice responses.
Which solution will meet these requirements?
정답：C
설명：
The mobile app for users with visual impairment needs to hear user speech and provide voice responses, requiring speech-to-text (speech recognition) and text-to-speech capabilities. Deep learning neural networks are widely used for speech recognition tasks, as they can effectively process and transcribe spoken language.
AWS services like Amazon Transcribe, which uses deep learning for speech recognition, can fulfill this requirement by converting user speech to text, and Amazon Polly can generate voice responses.
Exact Extract from AWS AI Documents:
From the AWS Documentation on Amazon Transcribe:
"Amazon Transcribe uses deep learning neural networks to perform automatic speech recognition (ASR), converting spoken language into text with high accuracy. This is ideal for applications requiring voice input, such as accessibility features for visually impaired users." (Source: Amazon Transcribe Developer Guide, Introduction to Amazon Transcribe) Detailed Explanation:
* Option A: Use a deep learning neural network to perform speech recognition.This is the correct answer. Deep learning neural networks are the foundation of modern speech recognition systems, as used in AWS services like Amazon Transcribe. They enable the app to hear and transcribe user speech, and a service like Amazon Polly can handle voice responses, meeting the requirements.
* Option B: Build ML models to search for patterns in numeric data.This option is irrelevant, as the task involves processing speech (audio data) and generating voice responses, not analyzing numeric data patterns.
* Option C: Use generative AI summarization to generate human-like text.Generative AI summarization focuses on summarizing text, not processing speech orgenerating voice responses. This option does not address the core requirement of speech recognition.
* Option D: Build custom models for image classification and recognition.Image classification and recognition are unrelated to processing speech or generating voice responses, making this option incorrect for an app focused on audio interaction.
References:
Amazon Transcribe Developer Guide: Introduction to Amazon Transcribe (https://docs.aws.amazon.com
/transcribe/latest/dg/what-is.html)
Amazon Polly Developer Guide: Text-to-Speech Overview (https://docs.aws.amazon.com/polly/latest/dg
/what-is.html)
AWS AI Practitioner Learning Path: Module on Speech Recognition and Synthesis
질문 # 326
A company deployed a model to production. After 4 months, the model inference quality degraded. The company wants to receive a notification if the model inference quality degrades. The company also wants to ensure that the problem does not happen again.
Which solution will meet these requirements?
정답：B
설명：
The company needs to address the degradation in model inference quality after 4 months in production and prevent future occurrences by receiving notifications. Retraining the model can address the current degradation, likely caused by data drift (changes in the data distribution over time). Amazon SageMaker Model Monitor is designed to detect and monitor model drift, alerting the company when inference quality degrades, thus meeting both requirements.
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"Amazon SageMaker Model Monitor enables you to monitor machine learning models in production for data drift, model performance degradation, and other quality issues. It can detect drift in feature distributions and inference quality, sending notifications when deviations are detected, allowing you to take corrective actions such as retraining the model." (Source: Amazon SageMaker Developer Guide, Monitoring Models with SageMaker Model Monitor) Detailed Explanation:
* Option A: Retrain the model. Monitor model drift by using Amazon SageMaker Clarify.
SageMaker Clarify is used for bias detection and explainability, not for monitoring model drift or inference quality in production. This option does not fully meet the requirements.
* Option B: Retrain the model. Monitor model drift by using Amazon SageMaker Model Monitor.
This is the correct answer. Retraining addresses the current degradation, and SageMaker Model Monitor can detect future drift in inference quality, sending notifications to prevent recurrence, as required.
* Option C: Build a new model. Monitor model drift by using Amazon SageMaker Feature Store.
SageMaker Feature Store is for managing and sharing features, not for monitoring model drift or inference quality. Building a new model may not be necessary if retraining can address the issue.
* Option D: Build a new model. Monitor model drift by using Amazon SageMaker JumpStart.
SageMaker JumpStart provides pre-trained models and solutions for quick deployment, but it does not offer specific tools for monitoring model drift or inference quality in production.
References:
Amazon SageMaker Developer Guide: Monitoring Models with SageMaker Model Monitor (https://docs.aws.
amazon.com/sagemaker/latest/dg/model-monitor.html)
AWS AI Practitioner Learning Path: Module on Model Monitoring and Maintenance AWS Documentation: Addressing Model Drift in Production (https://aws.amazon.com/sagemaker/)
질문 # 327
A company is using Amazon SageMaker to develop AI models.
Select the correct SageMaker feature or resource from the following list for each step in the AI model lifecycle workflow. Each SageMaker feature or resource should be selected one time or not at all. (Select TWO.) SageMaker Clarify SageMaker Model Registry SageMaker Serverless Inference
정답：
설명：
Reference:
AWS SageMaker Documentation: Model Registry (https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry.html) AWS SageMaker Documentation: Serverless Inference (https://docs.aws.amazon.com/sagemaker/latest/dg/serverless-inference.html) AWS AI Practitioner Study Guide (conceptual alignment with SageMaker features for model lifecycle management and inference) Let's format this question according to the specified structure and provide a detailed, verified answer based on AWS AI Practitioner knowledge and official AWS documentation. The question focuses on selecting an AWS database service that supports storage and queries of embeddings as vectors, which is relevant to generative AI applications.
질문 # 328
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