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| Section | Weight | Objectives |
|---|---|---|
| Fundamentals of AI and ML | 20% | - Identify types of ML: supervised, unsupervised, reinforcement learning - Define artificial intelligence (AI), machine learning (ML), and deep learning - Recognize key concepts: data, models, training, inference, evaluation - Describe common ML workflows and lifecycle |
| Applications of Foundation Models | 28% | - Recognize tools for building and deploying generative AI solutions - Identify AWS services for generative AI: Amazon Bedrock, Amazon Titan - Describe use cases for text, image, video, and code generation - Explain integration of foundation models into applications |
| Guidelines for Responsible AI | 14% | - Identify risks and mitigation strategies for AI systems - Explain bias detection and reduction - Define responsible AI principles: fairness, transparency, privacy, safety - Describe ethical and societal impacts of AI |
| Security, Compliance, and Governance for AI Solutions | 14% | - Compliance requirements and regulations - Governance frameworks for AI lifecycle - Data protection and privacy in AI workflows - Security controls for AI data and models |
| Fundamentals of Generative AI | 24% | - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) - Explain capabilities and use cases of generative AI - Define generative AI and foundation models - Differentiate between generative AI and traditional ML |
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NEW QUESTION # 243
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 # 244
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:
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.
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:
Reference:
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 # 245
Select the correct AI term from the following list for each statement. Each AI term should be selected one time. (Select THREE.)
* AI
* Deep learning
* ML
Answer:
Explanation:
NEW QUESTION # 246
Which term is an example of output vulnerability?
Answer: C
NEW QUESTION # 247
A company wants to develop ML applications to improve business operations and efficiency.
Select the correct ML paradigm from the following list for each use case. Each ML paradigm should be selected one or more times. (Select FOUR.)
* Supervised learning
* Unsupervised learning
Answer:
Explanation:
Reference:
AWS AI Practitioner Learning Path: Module on Machine Learning Strategies Amazon SageMaker Developer Guide: Supervised and Unsupervised Learning (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html) AWS Documentation: Introduction to Machine Learning Paradigms (https://aws.amazon.com/machine-learning/)
NEW QUESTION # 248
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