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NEW QUESTION # 405
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: A
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 # 406
An online learning company with large volumes of educational materials wants to use enterprise search. Which AWS service meets these requirements?
Answer: A
Explanation:
* Amazon Kendra is an enterprise search service that uses machine learning to index and search unstructured data such as documents, manuals, and course materials.
* Amazon Comprehend performs NLP tasks like sentiment analysis and entity recognition, not enterprise search.
* Amazon Textract extracts structured text from scanned docs and PDFs.
* Amazon Personalize builds recommendation systems, not search engines.
# Reference:
AWS Documentation - Amazon Kendra
NEW QUESTION # 407
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?
Answer: B
Explanation:
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/)
NEW QUESTION # 408
A company is using a pre-trained large language model (LLM). The LLM must perform multiple tasks that require specific domain knowledge. The LLM does not have information about several technical topics in the domain. The company has unlabeled data that the company can use to fine-tune the model.
Which fine-tuning method will meet these requirements?
Answer: B
Explanation:
The correct answer is C because Continued Pre-training (also known as domain-adaptive pre-training) involves training a pre-trained model further on unlabeled domain-specific data. This method helps adapt the LLM to a specific domain without needing labeled datasets, making it ideal for cases where the goal is to enhance the model's understanding of technical language or terminology.
From AWS documentation:
"Continued pre-training allows an LLM to ingest large volumes of domain-specific text without labels to improve contextual understanding in a particular area. This is effective when adapting a foundation model to new knowledge without altering the model architecture." Explanation of other options:
A). Full training refers to building a model from scratch, which is extremely resource-intensive and unnecessary if a strong base model already exists.
B). Supervised fine-tuning requires labeled data, which the scenario explicitly lacks.
D). RAG is a method to retrieve external information at inference time, not a training technique using unlabeled data.
Referenced AWS AI/ML Documents and Study Guides:
* AWS Bedrock Model Customization Documentation - Continued Pre-training
* Amazon SageMaker JumpStart - Domain Adaptation Techniques
* AWS Machine Learning Specialty Study Guide - Foundation Model Customization Section
NEW QUESTION # 409
A company is building a solution to generate images for protective eyewear. The solution must have high accuracy and must minimize the risk of incorrect annotations.
Which solution will meet these requirements?
Answer: C
Explanation:
Amazon SageMaker Ground Truth Plus is a managed data labeling service that includes human-in-the-loop (HITL) validation. This solution ensures high accuracy by involving human reviewers to validate the annotations and reduce the risk of incorrect annotations.
* Amazon SageMaker Ground Truth Plus:
* It allows for the creation of high-quality training datasets with human oversight, which minimizes errors in labeling and increases accuracy.
* Human-in-the-loop workflows help verify the correctness of annotations, ensuring that generated images for protective eyewear meet high-quality standards.
* Why Option A is Correct:
* High Accuracy: Human-in-the-loop validation provides the ability to catch and correct errors in annotations, ensuring high-quality data.
* Minimized Risk of Incorrect Annotations: Human review adds a layer of quality assurance, which is especially important in use cases like generating precise images for protective eyewear.
* Why Other Options are Incorrect:
* B. Amazon Bedrock: Does not offer a knowledge base for data augmentation; it focuses on running foundation models.
* C. Amazon Rekognition: Provides image recognition and analysis, not a solution for minimizing annotation errors.
* D. Amazon QuickSight: A data visualization tool, not relevant to image annotation or generation tasks.
Thus, A is the correct answer for generating high-accuracy images with minimized annotation risks.
NEW QUESTION # 410
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