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NEW QUESTION # 228
Which option is an example of unsupervised learning?
Answer: C
Explanation:
* Unsupervised learning involves discovering hidden patterns without labeled data. Example: clustering
.
* Image recognition (B) is supervised learning.
* House price prediction (C) is regression (supervised).
NEW QUESTION # 229
A financial company is using ML to help with some of the company's tasks.
Which option is a use of generative AI models?
Answer: A
Explanation:
Generative AI models (such as large language models) are designed to generate new content, such as text, summaries, images, and more. Summarizing text-like customer complaints-is a classic application of generative AI.
A is correct:
"Text summarization is a core generative AI use case, as it involves generating new, concise content from a larger body of text." (Reference: AWS Generative AI Use Cases)
"Text summarization is a core generative AI use case, as it involves generating new, concise content from a larger body of text." (Reference: AWS Generative AI Use Cases) B and C are standard ML classification/segmentation tasks.
D is a regression/prediction task, not generative.
NEW QUESTION # 230
A real estate company is developing an ML model to predict house prices by using sales and marketing data. The company wants to use feature engineering to build a model that makes accurate predictions.
Which approach will meet these requirements?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Feature engineering focuses on:
Creating new features
Selecting the most relevant existing features
Improving model signal and accuracy
AWS ML best practices identify feature engineering as a key driver of predictive performance.
Why the other options are incorrect:
Visualization (A) helps understanding, not feature creation.
Hyperparameter tuning (B) optimizes models, not features.
Data collection (D) expands datasets but does not engineer features.
AWS AI document references:
Feature Engineering Best Practices
Improving Model Accuracy on AWS
ML Model Development Lifecycle
NEW QUESTION # 231
A company plans to use a generative AI model to provide real-time service quotes to users.
Which criteria should the company use to select the correct model for this use case?
Answer: C
Explanation:
The correct answer is D because low latency and optimized inference speed are critical for real-time applications. For delivering real-time service quotes, the system must respond in milliseconds or a few seconds at most, making latency a primary concern when choosing the model.
From AWS Bedrock documentation:
"When selecting a foundation model for real-time applications, inference speed and latency are key evaluation metrics to ensure responsive user experiences." Explanation of other options:
A . Model size affects performance and cost but doesn't directly guarantee low latency.
B . Training data quality is important for accuracy, but it doesn't address real-time performance requirements.
C . GPU availability matters in infrastructure planning, not in model selection for latency optimization.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Model Selection Guide - Real-time Use Case Considerations AWS ML Specialty Guide - Foundation Model Performance Criteria
NEW QUESTION # 232
A company is using a generative AI model to develop a digital assistant. The model's responses occasionally include undesirable and potentially harmful content. Select the correct Amazon Bedrock filter policy from the following list for each mitigation action. Each filter policy should be selected one time. (Select FOUR.)
* Content filters
* Contextual grounding check
* Denied topics
* Word filters
Answer:
Explanation:
Block input prompts or model responses that contain harmful content such as hate, insults, violence, or misconduct:Content filters Avoid subjects related to illegal investment advice or legal advice:Denied topics Detect and block specific offensive terms:Word filters Detect and filter out information in the model's responses that is not grounded in the provided source information:Contextual grounding check The company is using a generative AI model on Amazon Bedrock and needs to mitigate undesirable and potentially harmful content in the model's responses. Amazon Bedrock provides several guardrail mechanisms, including content filters, denied topics, word filters, and contextual grounding checks, to ensure safe and accurate outputs. Each mitigation action in the hotspot aligns with a specific Bedrock filter policy, and each policy must be used exactly once.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
*"Amazon Bedrock guardrails provide mechanisms to control model outputs, including:
Content filters: Block harmful content such as hate speech, violence, or misconduct.
Denied topics: Prevent the model from generating responses on specific subjects, such as illegal activities or advice.
Word filters: Detect and block specific offensive or inappropriate terms.
Contextual grounding check: Ensure responses are grounded in the provided source information, filtering out ungrounded or hallucinated content."*(Source: AWS Bedrock User Guide, Guardrails for Responsible AI) Detailed Explanation:
Block input prompts or model responses that contain harmful content such as hate, insults, violence, or misconduct: Content filtersContent filters in Amazon Bedrock are designed to detect and block harmful content, such as hate speech, insults, violence, or misconduct, ensuring the model's outputs are safe and appropriate. This matches the first mitigation action.
Avoid subjects related to illegal investment advice or legal advice: Denied topicsDenied topics allow users to specify subjects the model should avoid, such as illegal investment advice or legal advice, which could have regulatory implications. This policy aligns with the second mitigation action.
Detect and block specific offensive terms: Word filtersWord filters enable the detection and blocking of specific offensive or inappropriate terms defined by the user, making them ideal for this mitigation action focused on specific terms.
Detect and filter out information in the model's responses that is not grounded in the provided source information: Contextual grounding checkThe contextual grounding check ensures that the model's responses are based on the provided source information, filtering out ungrounded or hallucinated content. This matches the fourth mitigation action.
Hotspot Selection Analysis:
The hotspot lists four mitigation actions, each with the same dropdown options: "Select...," "Content filters,"
"Contextual grounding check," "Denied topics," and "Word filters." The correct selections are:
First action: Content filters
Second action: Denied topics
Third action: Word filters
Fourth action: Contextual grounding check
Each filter policy is used exactly once, as required, and aligns with Amazon Bedrock's guardrail capabilities.
References:
AWS Bedrock User Guide: Guardrails for Responsible AI (https://docs.aws.amazon.com/bedrock/latest
/userguide/guardrails.html)
AWS AI Practitioner Learning Path: Module on Responsible AI and Model Safety Amazon Bedrock Developer Guide: Configuring Guardrails (https://aws.amazon.com/bedrock/)
NEW QUESTION # 233
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