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Amazon AWS Certified AI Practitioner Sample Questions (Q158-Q163):

NEW QUESTION # 158
A company has installed a security camer
a. The company uses an ML model to evaluate the security camera footage for potential thefts. The company has discovered that the model disproportionately flags people who are members of a specific ethnic group.
Which type of bias is affecting the model output?

A. Observer bias
B. Sampling bias
C. Measurement bias
D. Confirmation bias

Answer: B

Explanation:
Sampling bias is the correct type of bias affecting the model output when it disproportionately flags people from a specific ethnic group.
Sampling Bias:
Occurs when the training data is not representative of the broader population, leading to skewed model outputs.
In this case, if the model disproportionately flags people from a specific ethnic group, it likely indicates that the training data was not adequately balanced or representative.
Why Option B is Correct:
Reflects Data Imbalance: A biased sample in the training data could result in unfair outcomes, such as disproportionately flagging a particular group.
Common Issue in ML Models: Sampling bias is a known problem that can lead to unfair or inaccurate model predictions.
Why Other Options are Incorrect:
A . Measurement bias: Involves errors in data collection or measurement, not sampling.
C . Observer bias: Refers to bias introduced by researchers or data collectors, not the model's output.
D . Confirmation bias: Involves favoring information that confirms existing beliefs, not relevant to model output bias.

NEW QUESTION # 159
A company needs to choose a model from Amazon Bedrock to use internally. The company must identify a model that generates responses in a style that the company's employees prefer.
What should the company do to meet these requirements?

A. Use public model leaderboards to identify the model.
B. Use the model InvocationLatency runtime metrics in Amazon CloudWatch when trying models.
C. Evaluate the models by using built-in prompt datasets.
D. Evaluate the models by using a human workforce and custom prompt datasets.

Answer: D

NEW QUESTION # 160
A social media company wants to use a large language model (LLM) for content moderation. The company wants to evaluate the LLM outputs for bias and potential discrimination against specific groups or individuals.
Which data source should the company use to evaluate the LLM outputs with the LEAST administrative effort?

A. Moderation logs
B. User-generated content
C. Benchmark datasets
D. Content moderation guidelines

Answer: C

Explanation:
Benchmark datasets are pre-validated datasets specifically designed to evaluate machine learning models for bias, fairness, and potential discrimination. These datasets are the most efficient tool for assessing an LLM's performance against known standards with minimal administrative effort.
Option D (Correct): "Benchmark datasets": This is the correct answer because using standardized benchmark datasets allows the company to evaluate model outputs for bias with minimal administrative overhead.
Option A: "User-generated content" is incorrect because it is unstructured and would require significant effort to analyze for bias.
Option B: "Moderation logs" is incorrect because they represent historical data and do not provide a standardized basis for evaluating bias.
Option C: "Content moderation guidelines" is incorrect because they provide qualitative criteria rather than a quantitative basis for evaluation.
AWS AI Practitioner Reference:
Evaluating AI Models for Bias on AWS: AWS supports using benchmark datasets to assess model fairness and detect potential bias efficiently.

NEW QUESTION # 161
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?

A. Build custom models for image classification and recognition.
B. Use generative AI summarization to generate human-like text.
C. Use a deep learning neural network to perform speech recognition.
D. Build ML models to search for patterns in numeric data.

Answer: C

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 Explanation:
* 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.
References:
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 # 162
A company wants to build an ML model by using Amazon SageMaker. The company needs to share and manage variables for model development across multiple teams.
Which SageMaker feature meets these requirements?

A. Amazon SageMaker Feature Store
B. Amazon SageMaker Model Cards
C. Amazon SageMaker Clarify
D. Amazon SageMaker Data Wrangler

Answer: A

NEW QUESTION # 163
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