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NEW QUESTION # 311
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: C
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.
:
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 # 312
An airline company wants to use a generative AI model to convert a flight booking system from one coding language into another coding language. The company must select a model for this task.
Which criteria should the company use to select the correct generative AI model for this task?
Answer: B
Explanation:
The correct answer is A because code translation tasks require a model with deep understanding of syntax, semantic meaning, and the ability to maintain functional equivalence in different programming languages.
Some foundation models offered via Amazon Bedrock (like CodeWhisperer or Meta's Code LLMs) are designed with these capabilities in mind.
From AWS documentation:
"Generative AI models used for code generation and translation must understand programming semantics and syntax to generate accurate and secure code. These models are trained to recognize language-specific patterns and preserve logic when converting between languages." Explanation of other options:
B). Speed and error handling are secondary to correctness and comprehension in code translation tasks.
C). Creative content generation is not relevant for deterministic tasks like source code translation.
D). Model size may affect performance or latency but is not a primary selection criterion for translation accuracy.
Referenced AWS AI/ML Documents and Study Guides:
* Amazon Bedrock Model Selection Guide - Code Generation and Translation
* AWS Developer Tools - CodeWhisperer Capabilities
* AWS ML Specialty Study Guide - LLM Use Cases in Software Engineering
NEW QUESTION # 313
A company wants to improve multiple ML models.
Select the correct technique from the following list of use cases. Each technique should be selected one time or not at all. (Select THREE.) Few-shot learning Fine-tuning Retrieval Augmented Generation (RAG) Zero-shot learning
Answer:
Explanation:
AWS References:
Amazon Bedrock - Retrieval Augmented Generation (RAG)
AWS Generative AI Guide - Zero-shot & Few-shot learning
NEW QUESTION # 314
A company is developing a new model to predict the prices of specific items. The model performed well on the training dataset. When the company deployed the model to production, the model's performance decreased significantly.
What should the company do to mitigate this problem?
Answer: A
Explanation:
When a model performs well on the training data but poorly in production, it is often due to overfitting. Overfitting occurs when a model learns patterns and noise specific to the training data, which does not generalize well to new, unseen data in production. Increasing the volume of data used in training can help mitigate this problem by providing a more diverse and representative dataset, which helps the model generalize better.
Option C (Correct): "Increase the volume of data that is used in training": Increasing the data volume can help the model learn more generalized patterns rather than specific features of the training dataset, reducing overfitting and improving performance in production.
Option A: "Reduce the volume of data that is used in training" is incorrect, as reducing data volume would likely worsen the overfitting problem.
Option B: "Add hyperparameters to the model" is incorrect because adding hyperparameters alone does not address the issue of data diversity or model generalization.
Option D: "Increase the model training time" is incorrect because simply increasing training time does not prevent overfitting; the model needs more diverse data.
AWS AI Practitioner Reference:
Best Practices for Model Training on AWS: AWS recommends using a larger and more diverse training dataset to improve a model's generalization capability and reduce the risk of overfitting.
NEW QUESTION # 315
A company uses Amazon SageMaker and various models fa Its AI workloads. The company needs to understand If Its AI workloads are ISO compliant.
Which AWS service or feature meets these requirements?
Answer: D
NEW QUESTION # 316
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