Practice Free AIF-C01 Exam Online Questions – Page 12

Question #111

Which AWS feature records details about ML instance data for governance and reporting?

A . Amazon SageMaker Model Cards
B . Amazon SageMaker Debugger
C . Amazon SageMaker Model Monitor
D . Amazon SageMaker JumpStart

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Question #112

Which strategy will determine if a foundation model (FM) effectively meets business objectives?

A . Evaluate the model’s performance on benchmark datasets.
B . Analyze the model’s architecture and hyperparameters.
C . Assess the model’s alignment with specific use cases.
D . Measure the computational resources required for model deployment.

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Question #113

An AI company periodically evaluates its systems and processes with the help of independent software vendors (ISVs). The company needs to receive email notifications when an ISV’s compliance reports become available.

Which AWS service can the company use to meet this requirement?

A . AWS Audit Manager
B . AWS Artifact
C . AWS Trusted Advisor
D . AWS Data Exchange

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Question #114

Sentiment analysis is a subset of which broader field of AI?

A . Computer vision
B . Robotics
C . Natural language processing (NLP)
D . Time series forecasting

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Question #115

An AI practitioner is developing a new ML model. After training the model, the AI practitioner evaluates the accuracy of the model’s predictions. The model’s accuracy is low when the model uses both the training dataset and the test dataset.

Which scenario is the MOST likely cause of this problem?

A . Overfitting
B . Hallucination
C . Underfitting
D . Cross-validation

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Question #116

A company wants to use an AI model to generate labels for online news articles that the company publishes. The company selects a foundation model (FM) instead of a conventional ML model for this task.

What is one advantage of using an FM instead of a conventional ML model to meet this requirement?

A . An FM does not require training.
B . An FM is smaller and faster.
C . An FM is more transparent.
D . An FM is not biased.

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Question #117

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

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Correct Answer: A
A

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 or generating 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

Question #118

A company has an ML model. The company wants to know how the model makes predictions.

Which term refers to understanding model predictions?

A . Model interpretability
B . Model training
C . Model interoperability
D . Model performance

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Question #119

A company wants to improve the accuracy of the responses from a generative AI application. The application uses a foundation model (FM) on Amazon Bedrock.

Which solution meets these requirements MOST cost-effectively?

A . Fine-tune the FM.
B . Retrain the FM.
C . Train a new FM.
D . Use prompt engineering.

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Correct Answer: D
D

Explanation:

The company wants to improve the accuracy of a generative AI application using a foundation model (FM) on Amazon Bedrock in the most cost-effective way. Prompt engineering involves optimizing the input prompts to guide the FM to produce more accurate responses without modifying the model itself. This approach is cost-effective because it does not require additional computational resources or training, unlike fine-tuning or retraining.

Exact Extract from AWS AI Documents:

From the AWS Bedrock User Guide:

"Prompt engineering is a cost-effective technique to improve the performance of foundation models. By crafting precise and context-rich prompts, users can guide the model to generate more accurate and relevant responses without the need for fine-tuning or retraining."

(Source: AWS Bedrock User Guide, Prompt Engineering for Foundation Models)

Detailed

Option A: Fine-tune the FM. Fine-tuning involves retraining the FM on a custom dataset, which requires computational resources, time, and cost (e.g., for Amazon Bedrock fine-tuning jobs). It is not the most cost-effective solution.

Option B: Retrain the FM. Retraining an FM from scratch is highly resource-intensive and expensive, as it requires large datasets and significant compute power. This is not cost-effective.

Option C: Train a new FM. Training a new FM is the most expensive option, as it involves building a model from the ground up, requiring extensive data, compute resources, and expertise. This is not cost-effective.

Option D: Use prompt engineering. This is the correct answer. Prompt engineering adjusts the input prompts to improve the FM’s responses without incurring additional compute costs, making it the most cost-effective solution for improving accuracy on Amazon Bedrock.

Reference: AWS Bedrock User Guide: Prompt Engineering for Foundation Models (https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-engineering.html)

AWS AI Practitioner Learning Path: Module on Generative AI Optimization

Amazon Bedrock Developer Guide: Cost Optimization for Generative AI (https://aws.amazon.com/bedrock/)

Question #120

A company wants to create a new solution by using AWS Glue. The company has minimal programming experience with AWS Glue.

Which AWS service can help the company use AWS Glue?

A . Amazon Q Developer
B . AWS Config
C . Amazon Personalize
D . Amazon Comprehend

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Correct Answer: A
A

Explanation:

AWS Glue is a serverless data integration service that enables users to extract, transform, and load (ETL) data. For a company with minimal programming experience, Amazon Q Developer provides an AI-powered assistant that can generate code, explain AWS services, and guide users through tasks like creating AWS Glue jobs. This makes it an ideal tool to help the company use AWS Glue effectively.

Exact Extract from AWS AI Documents:

From the AWS Documentation on Amazon Q Developer:

"Amazon Q Developer is an AI-powered assistant that helps developers by generating code, answering questions about AWS services, and providing step-by-step guidance for tasks such as building ETL pipelines with AWS Glue. It is designed to assist users with varying levels of expertise, including those with minimal programming experience."

(Source: AWS Documentation, Amazon Q Developer Overview)

Detailed

Option A: Amazon Q Developer

This is the correct answer. Amazon Q Developer can assist the company by generating AWS Glue scripts, explaining Glue concepts, and providing guidance on setting up ETL jobs, which is particularly helpful for users with limited programming experience.

Option B: AWS Config

AWS Config is used for tracking and managing resource configurations and compliance, not for assisting with coding or using services like AWS Glue. This option is incorrect.

Option C: Amazon Personalize

Amazon Personalize is a machine learning service for building recommendation systems, not for assisting with data integration or AWS Glue. This option is irrelevant.

Option D: Amazon Comprehend

Amazon Comprehend is an NLP service for analyzing text, not for helping users write code or use AWS Glue. This option does not meet the requirements.

Reference: AWS Documentation: Amazon Q Developer Overview (https://aws.amazon.com/q/developer/)

AWS Glue Developer Guide: Introduction to AWS Glue (https://docs.aws.amazon.com/glue/latest/dg/what-is-glue.html)

AWS AI Practitioner Learning Path: Module on AWS Developer Tools and Services

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