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NEW QUESTION # 316
A company wants to use AI for budgeting. The company made one budget manually and one budget by using an AI model. The company compared the budgets to evaluate the performance of the AI model. The AI model budget produced incorrect numbers.
Which option represents the AI model's problem?
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Hallucinations occur when an AI model generates incorrect, fabricated, or misleading outputs that appear plausible but are factually wrong.
AWS generative AI guidance identifies hallucinations as:
* A common limitation of generative models
* A risk when models generate numerical or factual data
* A key reason for validation and human review in critical use cases
Why the other options are incorrect:
* Safety (B) relates to harmful or restricted content.
* Interpretability (C) refers to understanding how a model makes decisions.
* Cost (D) concerns operational expenses.
AWS AI document references:
* Generative AI Risks and Limitations
* Responsible Use of Foundation Models
* Model Validation Best Practices
NEW QUESTION # 317
A company is implementing the Amazon Titan foundation model (FM) by using Amazon Bedrock. The company needs to supplement the model by using relevant data from the company's private data sources.
Which solution will meet this requirement?
Answer: D
Explanation:
Creating an Amazon Bedrock knowledge base allows the integration of external or private data sources with a foundation model (FM) like Amazon Titan. This integration helps supplement the model with relevant data from the company's private data sources to enhance its responses.
Option C (Correct): "Create an Amazon Bedrock knowledge base": This is the correct answer as it enables the company to incorporate private data into the FM to improve its effectiveness.
Option A: "Use a different FM" is incorrect because it does not address the need to supplement the current model with private data.
Option B: "Choose a lower temperature value" is incorrect as it affects output randomness, not the integration of private data.
Option D: "Enable model invocation logging" is incorrect because logging does not help in supplementing the model with additional data.
AWS AI Practitioner Reference:
Amazon Bedrock and Knowledge Integration: AWS explains how creating a knowledge base allows Amazon Bedrock to use external data sources to improve the FM's relevance and accuracy.
NEW QUESTION # 318
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:
Explanation:
The company is developing ML applications for various use cases, and the task is to select the correct ML paradigm (supervised or unsupervised learning) for each. Supervised learning involves training a model on labeled data to make predictions, while unsupervised learning identifies patterns or structures in unlabeled data. Each use case aligns with one of these paradigms based on its requirements.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"Supervised learning uses labeled data to train models for tasks like classification (e.g., binary or multi-class classification), where the model predicts a category. Unsupervised learning works with unlabeled data for tasks like clustering (e.g., K-means clustering) or dimensionality reduction, identifying patternsor reducing data complexity without predefined labels." (Source: AWS AI Practitioner Learning Path, Module on Machine Learning Strategies) Detailed Explanation:
* Binary classification: Supervised learningBinary classification involves predicting one of two classes (e.g., yes/no, spam/not spam) using labeled data, making it a supervised learning task. The model learns from examples where the correct class is provided.
* Multi-class classification: Supervised learningMulti-class classification extends binary classification to predict one of multiple classes (e.g., categorizing items into several groups). Like binary classification, it requires labeled data, so it falls under supervised learning.
* K-means clustering: Unsupervised learningK-means clustering groups data into clusters based on similarity, without requiring labeled data. This is a classic unsupervised learning task, as the algorithm identifies patterns in the data on its own.
* Dimensionality reduction: Unsupervised learningDimensionality reduction (e.g., using techniques like PCA) reduces the number of features in a dataset while preserving important information. It does not require labeled data, making it an unsupervised learning task.
Hotspot Selection Analysis:
The hotspot lists four use cases, each with a dropdown containing "Select...," "Supervised learning," and
"Unsupervised learning." The correct selections are:
* Binary classification: Supervised learning
* Multi-class classification: Supervised learning
* K-means clustering: Unsupervised learning
* Dimensionality reduction: Unsupervised learning
Each paradigm (supervised and unsupervised learning) is used twice, as the question allows for paradigms to be selected one or more times.
References:
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 # 319
Which scenario indicates that an ML model is overfitting?
Answer: B
Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
Overfitting occurs when an ML model learns patterns too closely from training data, including noise, and fails to generalize to unseen data. According to AWS ML fundamentals, a key indicator of overfitting is high training accuracy combined with reduced accuracy on new or test data.
Option A directly reflects this behavior: the model performs well during training but loses accuracy when evaluated on new data, which is a textbook symptom of overfitting.
Why the other options are not overfitting:
B describes an under-featured model (underfitting risk).
C indicates poor data representativeness, not overfitting.
D reflects limited feature selection, which may lead to underfitting or bias, not overfitting.
AWS AI Study Guide Reference:
AWS Machine Learning concepts: bias vs. variance
AWS training and evaluation best practices
NEW QUESTION # 320
A company is building a mobile app for users who have a visual impairment. The app must be able to hear what users say and provide voice responses.
Which solution will meet these requirements?
Answer: D
Explanation:
The mobile app for users with visual impairment needs to hear user speech and provide voice responses, requiring speech-to-text (speech recognition) and text-to-speech capabilities. Deep learning neural networks are widely used for speech recognition tasks, as they can effectively process and transcribe spoken language. AWS services like Amazon Transcribe, which uses deep learning for speech recognition, can fulfill this requirement by converting user speech to text, and Amazon Polly can generate voice responses.
Exact Extract from AWS AI Documents:
From the AWS Documentation on Amazon Transcribe:
"Amazon Transcribe uses deep learning neural networks to perform automatic speech recognition (ASR), converting spoken language into text with high accuracy. This is ideal for applications requiring voice input, such as accessibility features for visually impaired users." (Source: Amazon Transcribe Developer Guide, Introduction to Amazon Transcribe) Detailed Option A: Use a deep learning neural network to perform speech recognition.This is the correct answer. Deep learning neural networks are the foundation of modern speech recognition systems, as used in AWS services like Amazon Transcribe. They enable the app to hear and transcribe user speech, and a service like Amazon Polly can handle voice responses, meeting the requirements.
Option B: Build ML models to search for patterns in numeric data.This option is irrelevant, as the task involves processing speech (audio data) and generating voice responses, not analyzing numeric data patterns.
Option C: Use generative AI summarization to generate human-like text.Generative AI summarization focuses on summarizing text, not processing speech orgenerating voice responses. This option does not address the core requirement of speech recognition.
Option D: Build custom models for image classification and recognition.Image classification and recognition are unrelated to processing speech or generating voice responses, making this option incorrect for an app focused on audio interaction.
Reference:
Amazon Transcribe Developer Guide: Introduction to Amazon Transcribe (https://docs.aws.amazon.com/transcribe/latest/dg/what-is.html) Amazon Polly Developer Guide: Text-to-Speech Overview (https://docs.aws.amazon.com/polly/latest/dg/what-is.html) AWS AI Practitioner Learning Path: Module on Speech Recognition and Synthesis
NEW QUESTION # 321
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