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NEW QUESTION # 16
An AI practitioner wants to use a foundation model (FM) to design a search application. The search application must handle queries that have text and images.
Which type of FM should the AI practitioner use to power the search application?
Answer: B
Explanation:
A multi-modal embedding model is the correct type of foundation model (FM) for powering a search application that handles queries containing both text and images.
* Multi-Modal Embedding Model:
* Can process and integrate different types of data (e.g., text and images) into a common representation space, enabling a unified search capability.
* Suitable for applications where queries or content involve multiple data modalities.
* Why Option A is Correct:
* Handles Multiple Modalities: Supports both text and image data, aligning with the application's requirement.
* Improves Search Relevance: Allows for more accurate and relevant search results across different types of input data.
* Why Other Options are Incorrect:
* B. Text embedding model: Only handles text data, not images.
* C. Multi-modal generation model: Focuses on generating outputs rather than embedding for search tasks.
* D. Image generation model: Only handles image data, not suitable for text queries.
NEW QUESTION # 17
A financial company is creating an AI model for customer loan applications. The company wants to demonstrate the principles of human-centered design for explainable AI.
Which Amazon SageMaker AI feature meets these requirements?
Answer: B
Explanation:
The verified answer is B. Amazon SageMaker Clarify. The model is used for customer loan applications, where explainability, transparency, and human-centered decision support are critical. AWS documentation states that SageMaker Clarify provides tools to help explain how machine learning models make predictions.
These tools help ML developers, modelers, and internal stakeholders understand model characteristics before deployment and debug predictions after deployment.
SageMaker Clarify is specifically aligned with explainable AI because it provides feature attribution methods, including SHAP-based explanations. AWS documentation explains that Clarify can generate reports that contribute to model governance documentation and can help describe how a model works, what features influence its decisions, and how explanations are generated.
Amazon SageMaker Model Registry is incorrect because it manages model versions, approval status, and deployment tracking. It supports governance workflows, but it does not explain why a model made a decision.
Amazon SageMaker Pipelines is incorrect because it automates and orchestrates ML workflows such as training, evaluation, and deployment. It does not provide the core explainability function required here.
Amazon SageMaker Feature Store is incorrect because it stores, shares, and serves ML features for training and inference. It improves feature reuse and consistency, but it does not explain model decisions.
For a financial loan application model, stakeholders and regulators need to understand which factors influenced predictions. SageMaker Clarify is the AWS feature designed for this explainability requirement, so option B is correct.
NEW QUESTION # 18
A company has deployed an AI application in production on AWS. The application's responses have become less accurate over time. The company needs a solution to send alerts when the application performance drifts.
Which AWS service or feature will meet this requirement?
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Amazon SageMaker Model Monitor detects:
* Data drift
* Concept drift
* Model quality degradation
AWS MLOps guidance recommends Model Monitor to:
* Continuously evaluate production models
* Trigger alerts when performance deviates from baselines
* Maintain long-term model reliability
Why the other options are incorrect:
* Amazon A2I (A) adds human review workflows.
* Rekognition (C) is an image analysis service.
* Trusted Advisor (D) provides cost and security recommendations.
AWS AI document references:
* Amazon SageMaker Model Monitor Overview
* Detecting Model Drift on AWS
* Production ML Monitoring Best Practices
NEW QUESTION # 19
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
Answer:
Explanation:
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.
NEW QUESTION # 20
A media company wants to analyze viewer behavior and demographics to recommend personalized content.
The company wants to deploy a customized ML model in its production environment. The company also wants to observe if the model quality drifts over time.
Which AWS service or feature meets these requirements?
Answer: B
Explanation:
The requirement is to deploy a customized machine learning (ML) model and monitor its quality for potential drift over time in a production environment. Let's evaluate each option:
A). Amazon Rekognition: This service is designed for image and video analysis, such as object detection, facial recognition, and text extraction. It is not suited for deploying custom ML models or monitoring model quality drift.
B). Amazon SageMaker Clarify: This feature helps detect bias in ML models and explains model predictions.
While it addresses fairness and interpretability, it does not specifically focus on monitoring model quality drift over time in production.
C). Amazon Comprehend: This is a natural language processing (NLP) service for extracting insights from text, such as sentiment analysis or entity recognition. It does not support deploying custom ML models or monitoring model performance drift.
D). Amazon SageMaker Model Monitor: This feature is part of Amazon SageMaker and is specifically designed to monitor ML models in production. It tracks metrics such as data drift, model drift, and performance degradation over time, alerting users when issues are detected.
Exact Extract Reference: According to the AWS documentation on Amazon SageMaker, "Amazon SageMaker Model Monitor allows you to detect and remediate data and model quality issues in production. It continuously monitors the performance of deployed models, capturing data and model predictions to detect deviations from expected behavior, such as data drift or model performance degradation." (Source: AWS SageMaker Documentation - Model Monitoring, https://docs.aws.amazon.com/sagemaker/latest/dg/model- monitor.html).
This directly aligns with the requirement to observe model quality drift, making Amazon SageMaker Model Monitor the correct choice.
References:
AWS SageMaker Documentation: Model Monitoring (https://docs.aws.amazon.com/sagemaker/latest/dg
/model-monitor.html)
AWS AI Practitioner Study Guide (conceptual alignment with monitoring deployed ML models)
NEW QUESTION # 21
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