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| Section | Weight | Objectives |
|---|---|---|
| AI Application Development | 30% | - Evaluate and optimize AI applications
|
| Responsible AI | 20% | - Explain the principles of responsible AI
|
| AI/ML Fundamentals | 24% | - Understand the AI/ML lifecycle
|
| AWS AI Services Overview | 26% | - Describe Amazon Bedrock capabilities
|
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NEW QUESTION # 175
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 # 176
A company uses Amazon SageMaker AI to generate article summaries in multiple languages. The company needs a metric to evaluate the quality of the summary translations in multiple languages. Which evaluation metric will meet these requirements?
Answer: B
Explanation:
BLEU (Bilingual Evaluation Understudy) is the standard metric for evaluating machine translation quality across multiple languages.
ROUGE is for summarization quality (not translation).
AUC is for classification model performance.
Precision is a general metric but not specific for evaluating translations.
Reference:
AWS Documentation - Evaluation Metrics for NLP
NEW QUESTION # 177
A company has a foundation model (FM) that was customized by using Amazon Bedrock to answer customer queries about products. The company wants to validate the model's responses to new types of queries. The company needs to upload a new dataset that Amazon Bedrock can use for validation.
Which AWS service meets these requirements?
Answer: A
Explanation:
Amazon S3 is the optimal choice for storing and uploading datasets used for machine learning model validation and training. It offers scalable, durable, and secure storage, making it ideal for holding datasets required by Amazon Bedrock for validation purposes.
Option A (Correct): "Amazon S3": This is the correct answer because Amazon S3 is widely used for storing large datasets that are accessed by machine learning models, including those in Amazon Bedrock.
Option B: "Amazon Elastic Block Store (Amazon EBS)" is incorrect because EBS is a block storage service for use with Amazon EC2, not for directly storing datasets for Amazon Bedrock.
Option C: "Amazon Elastic File System (Amazon EFS)" is incorrect as it is primarily used for file storage with shared access by multiple instances.
Option D: "AWS Snowcone" is incorrect because it is a physical device for offline data transfer, not suitable for directly providing data to Amazon Bedrock.
AWS AI Practitioner References:
Storing and Managing Datasets on AWS for Machine Learning: AWS recommends using S3 for storing and managing datasets required for ML model training and validation.
NEW QUESTION # 178
A financial company uses a generative AI model to assign credit limits to new customers. The company wants to make the decision-making process of the model more transparent to its customers.
Answer: B
Explanation:
The correct answer is B because explainable AI (XAI) provides transparency into how models reach specific decisions. According to AWS documentation, techniques such as SHAP values (SHapley Additive exPlanations) or LIME can identify which input features (e.g., income, debt ratio, or credit history) most influenced a model's prediction. This helps financial institutions comply with fairness and transparency requirements under regulatory frameworks like the Equal Credit Opportunity Act. AWS SageMaker Clarify is a built-in service that offers explainability reports and bias detection to enhance trust. Rule-based systems and UIs alone do not satisfy transparency standards, and accuracy improvements do not replace explainability. By implementing explainable AI, customers can understand and trust credit limit decisions, reducing bias concerns and ensuring compliance.
Referenced AWS AI/ML Documents and Study Guides:
Amazon SageMaker Clarify Documentation - Explainability and Feature Attribution AWS Responsible AI Practices - Transparency and Accountability
NEW QUESTION # 179
A company wants to create a chatbot to answer employee questions about company policies. Company policies are updated frequently. The chatbot must reflect the changes in near real time. The company wants to choose a large language model (LLM).
Answer: A
Explanation:
The correct answer is C because Retrieval-Augmented Generation (RAG) allows a large language model to provide responses based on up-to-date content from external data sources without the need to fine-tune the model.
According to the AWS Bedrock Developer Guide:
"Amazon Bedrock Knowledge Bases enables developers to augment foundation models (FMs) with company-specific data that is updated in real time or near real time. By separating retrieval from the model itself, RAG-based approaches avoid the need for frequent retraining or fine-tuning." This means a company can use a knowledge base with Amazon Bedrock to dynamically fetch the latest company policy information and feed it to the LLM in the prompt. This approach is ideal for use cases where the content (like policies) changes frequently, and latency for updates must be minimal.
Explanation of other options:
A . Fine-tuning an LLM with SageMaker is not optimal for frequently updated data. Fine-tuning involves retraining and redeploying the model, which is time-consuming and not suited for real-time updates. As stated in the SageMaker documentation:
"Fine-tuning is best used for use cases where the data changes infrequently and where highly specific model behavior is required." B . Selecting a foundation model alone does not fulfill the real-time requirement. The FM's base knowledge is static unless augmented through additional methods like RAG.
D . Amazon Q Business is intended for workplace productivity and enterprise use but is more opinionated in structure and doesn't provide the same flexibility as a custom RAG workflow for building a tailored chatbot application. While it supports some real-time data sync features, it's not purpose-built for LLM-based chat systems with dynamic data feeds like Knowledge Bases in Bedrock.
Therefore, the most appropriate and scalable solution aligned with AWS recommendations is C.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Developer Guide - Knowledge Bases and RAG (2024 Edition) AWS Certified Machine Learning Specialty Study Guide - Generative AI Section AWS Documentation: Choosing Between Fine-Tuning and RAG for LLM Applications Amazon SageMaker Documentation - Model Tuning and Deployment Best Practices (2024)
NEW QUESTION # 180
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