Amazon AIF-C01 Exam Questions - Failure Will Result In A Refund

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Amazon AIF-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	Guidelines for Responsible AI: This domain highlights the ethical considerations and best practices for deploying AI solutions responsibly, including ensuring fairness and transparency. It is aimed at AI practitioners, including data scientists and compliance officers, who are involved in the development and deployment of AI systems and need to adhere to ethical standards.
Topic 2	Fundamentals of AI and ML: This domain covers the fundamental concepts of artificial intelligence (AI) and machine learning (ML), including core algorithms and principles. It is aimed at individuals new to AI and ML, such as entry-level data scientists and IT professionals.
Topic 3	Security, Compliance, and Governance for AI Solutions: This domain covers the security measures, compliance requirements, and governance practices essential for managing AI solutions. It targets security professionals, compliance officers, and IT managers responsible for safeguarding AI systems, ensuring regulatory compliance, and implementing effective governance frameworks.
Topic 4	Applications of Foundation Models: This domain examines how foundation models, like large language models, are used in practical applications. It is designed for those who need to understand the real-world implementation of these models, including solution architects and data engineers who work with AI technologies to solve complex problems.
Topic 5	Fundamentals of Generative AI: This domain explores the basics of generative AI, focusing on techniques for creating new content from learned patterns, including text and image generation. It targets professionals interested in understanding generative models, such as developers and researchers in AI.

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Amazon AWS Certified AI Practitioner Sample Questions (Q54-Q59):

NEW QUESTION # 54
A company wants to create an application to summarize meetings by using meeting audio recordings.
Select and order the correct steps from the following list to create the application. Each step should be selected one time or not at all. (Select and order THREE.)
* Convert meeting audio recordings to meeting text files by using Amazon Polly.
* Convert meeting audio recordings to meeting text files by using Amazon Transcribe.
* Store meeting audio recordings in an Amazon S3 bucket.
* Store meeting audio recordings in an Amazon Elastic Block Store (Amazon EBS) volume.
* Summarize meeting text files by using Amazon Bedrock.
* Summarize meeting text files by using Amazon Lex.

Answer:

Explanation:

Reference:
Amazon Transcribe Developer Guide: Introduction to Amazon Transcribe (https://docs.aws.amazon.com/transcribe/latest/dg/what-is.html) AWS Bedrock User Guide: Text Generation and Summarization (https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html) AWS AI Practitioner Learning Path: Module on Speech-to-Text and Generative AI Amazon S3 User Guide: Storing Data for Processing (https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html)

NEW QUESTION # 55
Which AW5 service makes foundation models (FMs) available to help users build and scale generative AI applications?

A. Amazon Kendra
B. Amazon Bedrock
C. Amazon Q Developer
D. Amazon Comprehend

Answer: B

Explanation:
Amazon Bedrock is a fully managed service that provides access to foundation models (FMs) from various providers, enabling users to build and scale generative AI applications. It simplifies the process of integrating FMs into applications for tasks like text generation, chatbots, and more.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Amazon Bedrock is a fully managed service that makes foundation models (FMs) from leading AI providers available through a single API, enabling developers to build and scale generative AI applications with ease." (Source: AWS Bedrock User Guide, Introduction to Amazon Bedrock) Detailed Option A: Amazon Q DeveloperAmazon Q Developer is an AI-powered assistant for coding and AWS service guidance, not a service for hosting or providing foundation models.
Option B: Amazon BedrockThis is the correct answer. Amazon Bedrock provides access to foundation models, making it the primary service for building and scaling generative AI applications.
Option C: Amazon KendraAmazon Kendra is an intelligent search service powered by machine learning, not a service for providing foundation models or building generative AI applications.
Option D: Amazon ComprehendAmazon Comprehend is an NLP service for text analysis tasks like sentiment analysis, not for providing foundation models or supporting generative AI.
Reference:
AWS Bedrock User Guide: Introduction to Amazon Bedrock (https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html) AWS AI Practitioner Learning Path: Module on Generative AI Services AWS Documentation: Generative AI on AWS (https://aws.amazon.com/generative-ai/)

NEW QUESTION # 56
A company wants to use generative AI to increase developer productivity and software development. The company wants to use Amazon Q Developer.
What can Amazon Q Developer do to help the company meet these requirements?

A. Enable voice commands for coding and providing natural language search.
B. Run an application without provisioning or managing servers.
C. Create software snippets, reference tracking, and open-source license tracking.
D. Convert audio files to text documents by using ML models.

Answer: C

Explanation:
Amazon Q Developer is a tool designed to assist developers in increasing productivity by generating code snippets, managing reference tracking, and handling open-source license tracking. These features help developers by automating parts of the software development process.
Option A (Correct): "Create software snippets, reference tracking, and open-source license tracking": This is the correct answer because these are key features that help developers streamline and automate tasks, thus improving productivity.
Option B: "Run an application without provisioning or managing servers" is incorrect as it refers to AWS Lambda or AWS Fargate, not Amazon Q Developer.
Option C: "Enable voice commands for coding and providing natural language search" is incorrect because this is not a function of Amazon Q Developer.
Option D: "Convert audio files to text documents by using ML models" is incorrect as this refers to Amazon Transcribe, not Amazon Q Developer.
AWS AI Practitioner Reference:
Amazon Q Developer Features: AWS documentation outlines how Amazon Q Developer supports developers by offering features that reduce manual effort and improve efficiency.

NEW QUESTION # 57
A company wants to build a lead prioritization application for its employees to contact potential customers.
The application must give employees the ability to view and adjust the weights assigned to different variables in the model based on domain knowledge and expertise.
Which ML model type meets these requirements?

A. K-nearest neighbors (k-NN) model
B. Neural network
C. Logistic regression model
D. Deep learning model built on principal components

Answer: C

Explanation:
The company needs an ML model for a lead prioritization application where employees can view and adjust the weights assigned to different variables based on domain knowledge. Logistic regression is a linear model that assigns interpretable weights to input features, making it easy for users to understand and modify these weights. This interpretability and adjustability make it suitable for the requirements.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"Logistic regression is a supervised learning algorithm used for classification tasks. It is highly interpretable, as it assigns weights to each feature, allowing users to understand and adjust the importance of different variables based on domain expertise." (Source: AWS AI Practitioner Learning Path, Module on Machine Learning Algorithms) Detailed Explanation:
* Option A: Logistic regression modelThis is the correct answer. Logistic regression provides interpretable coefficients (weights) for each feature, enabling employees to view and adjust them based on domain knowledge, meeting the application's requirements.
* Option B: Deep learning model built on principal componentsDeep learning models, even when using principal components, are complex and lack interpretability. The weights in such models are not easily adjustable by users, making this option unsuitable.
* Option C: K-nearest neighbors (k-NN) modelk-NN is a non-parametric model that does not assign explicit weights to features. It relieson distance metrics, which are not easily adjustable based on domain knowledge, so it does not meet the requirements.
* Option D: Neural networkNeural networks are highly complex and lack interpretability, as their weights are not directly tied to input features in a human-understandable way. Adjusting weights based on domain knowledge is impractical, making this option incorrect.
References:
AWS AI Practitioner Learning Path: Module on Machine Learning Algorithms Amazon SageMaker Developer Guide: Logistic Regression (https://docs.aws.amazon.com/sagemaker/latest/dg
/algos.html)
AWS Documentation: Interpretable Machine Learning Models (https://aws.amazon.com/machine-learning/)

NEW QUESTION # 58
An AI practitioner wants to use a foundation model (FM) to design a search application. The search application must handle queries that have text and images.
Which type of FM should the AI practitioner use to power the search application?

A. Multi-modal generation model
B. Image generation model
C. Text embedding model
D. Multi-modal embedding model

Answer: D

NEW QUESTION # 59
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AMAZON AIF-C01 EXAM QUESTIONS - FAILURE WILL RESULT IN A REFUND

Amazon AIF-C01 Exam Questions - Failure Will Result In A Refund