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| Section | Objectives |
|---|---|
| Topic 1: Fundamentals of Artificial Intelligence and Machine Learning | - Data fundamentals for AI/ML - Core AI and ML concepts - Common ML workflows and lifecycle |
| Topic 2: Fundamentals of Generative AI | - Foundation models and prompt engineering basics - Large language models (LLMs) concepts - AWS generative AI services overview (e.g., Amazon Bedrock) |
| Topic 3: Responsible AI and Security | - Security, privacy, and governance in AI systems - AI ethics and responsible use - Bias, fairness, and explainability |
| Topic 4: Applications of Foundation Models | - Use cases for generative AI in business - AI-powered assistants and automation - Content generation and summarization |
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NEW QUESTION # 140
A financial institution is building an AI solution to make loan approval decisions by using a foundation model (FM). For security and audit purposes, the company needs the AI solution's decisions to be explainable.
Which factor relates to the explainability of the AI solution's decisions?
Answer: C
Explanation:
The financial institution needs an AI solution for loan approval decisions to be explainable for security and audit purposes. Explainability refers to the ability to understand and interpret how a model makes decisions.
Model complexity directly impacts explainability: simpler models (e.g., logistic regression) are more interpretable, while complex models (e.g., deep neural networks) are harder to explain, often behaving like
"black boxes."
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"Model complexity affects the explainability of AI solutions. Simpler models, such as linear regression, are inherently more interpretable, while complex models, such as deep neural networks, may require additional tools like SageMaker Clarify to provide insights into their decision-making processes." (Source: Amazon SageMaker Developer Guide, Explainability with SageMaker Clarify) Detailed Explanation:
Option A: Model complexityThis is the correct answer. The complexity of the model directly influences how easily its decisions can be explained, a critical factor for audit and security purposes in loan approvals.
Option B: Training timeTraining time refers to how long it takes to train the model, which does not directly impact the explainability of its decisions.
Option C: Number of hyperparametersWhile hyperparameters affect model performance, they do not directly relate to explainability. A model with many hyperparameters might still be explainable if it is a simple model.
Option D: Deployment timeDeployment time refers to the time taken to deploy the model to production, which is unrelated to the explainability of its decisions.
References:
Amazon SageMaker Developer Guide: Explainability with SageMaker Clarify (https://docs.aws.amazon.com
/sagemaker/latest/dg/clarify-explainability.html)
AWS AI Practitioner Learning Path: Module on Responsible AI and Explainability AWS Documentation: Explainable AI (https://aws.amazon.com/machine-learning/responsible-ai/)
NEW QUESTION # 141
A company is using custom models in Amazon Bedrock for a generative AI application. The company wants to use a company-managed encryption key to encrypt the model artifacts that the model customization jobs create. Which AWS service meets these requirements?
Answer: D
Explanation:
Comprehensive and Detailed
AWS KMS provides customer-managed encryption keys (CMKs) that can be used to encrypt model artifacts and other sensitive data at rest.
Bedrock integrates with AWS KMS to allow encryption of customized models with your own keys.
Amazon Inspector is for vulnerability scanning, Amazon Macie for sensitive data discovery, AWS Secrets Manager for storing credentials and secrets.
Reference:
AWS Documentation - Using KMS with Amazon Bedrock
NEW QUESTION # 142
A company is building a generative Al application and is reviewing foundation models (FMs). The company needs to consider multiple FM characteristics.
Select the correct FM characteristic from the following list for each definition. Each FM characteristic should be selected one time. (Select THREE.) Concurrency Context windows Latency
Answer:
Explanation:
Explanation:
AWS References:
Amazon Bedrock - Model parameters and context window
AWS ML Inference - Latency and Throughput
AWS Scalability - Concurrency
NEW QUESTION # 143
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 # 144
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:
Explanation:
SageMaker Model Registry, SageMaker Serverless interference
This question requires selecting the appropriate Amazon SageMaker feature for two distinct steps in the AI model lifecycle. Let's break down each step and evaluate the options:
Step 1: Managing different versions of the model
The goal here is to identify a SageMaker feature that supports version control and management of machine learning models. Let's analyze the options:
SageMaker Clarify: This feature is used to detect bias in models and explain model predictions, helping with fairness and interpretability. It does not provide functionality for managing model versions.
SageMaker Model Registry: This is a centralized repository in Amazon SageMaker that allows users to catalog, manage, and track different versions of machine learning models. It supports model versioning, approval workflows, and deployment tracking, making it ideal for managing different versions of a model.
SageMaker Serverless Inference: This feature enables users to deploy models for inference without managing servers, automatically scaling based on demand. It is focused on inference (predictions), not on managing model versions.
Conclusion for Step 1: The SageMaker Model Registry is the correct choice for managing different versions of the model.
Exact Extract Reference: According to the AWS SageMaker documentation, "The SageMaker Model Registry allows you to catalog models for production, manage model versions, associate metadata, and manage approval status for deployment." (Source: AWS SageMaker Documentation - Model Registry,
https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry.html).
Step 2: Using the current model to make predictions
The goal here is to identify a SageMaker feature that facilitates making predictions (inference) with a deployed model. Let's evaluate the options:
SageMaker Clarify: As mentioned, this feature focuses on bias detection and explainability, not on performing inference or making predictions.
SageMaker Model Registry: While the Model Registry helps manage and catalog models, it is not used directly for making predictions. It can store models, but the actual inference process requires a deployment mechanism.
SageMaker Serverless Inference: This feature allows users to deploy models for inference without managing infrastructure. It automatically scales based on traffic and is specifically designed for making predictions in a cost-efficient, serverless manner.
Conclusion for Step 2: SageMaker Serverless Inference is the correct choice for using the current model to make predictions.
Exact Extract Reference: The AWS documentation states, "SageMaker Serverless Inference is a deployment option that allows you to deploy machine learning models for inference without configuring or managing servers. It automatically scales to handle inference requests, making it ideal for workloads with intermittent or unpredictable traffic." (Source: AWS SageMaker Documentation - Serverless Inference, https://docs.aws.
amazon.com/sagemaker/latest/dg/serverless-inference.html).
Why Not Use the Same Feature Twice?
The question specifies that each SageMaker feature or resource should be selected one time or not at all. Since SageMaker Model Registry is used for version management and SageMaker Serverless Inference is used for predictions, each feature is selected exactly once. SageMaker Clarify is not applicable to either step, so it is not selected at all, fulfilling the question's requirements.
References:
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 # 145
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