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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 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.

Topic 3

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 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

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.

Amazon AWS Certified AI Practitioner Sample Questions (Q28-Q33):

NEW QUESTION # 28
Why does overfilting occur in ML models?

A. The model contains a regularization method.
B. The training dataset does not reptesent all possible input values.
C. The training dataset contains too many features.
D. The model training stops early because of an early stopping criterion.

Answer: B

Explanation:
Overfitting occurs when an ML model learns the training data too well, including noise and patterns that do not generalize to new data. A key cause of overfitting is when the training dataset does not represent all possible input values, leading the model to over-specialize on the limited data it was trained on, failing to generalize to unseen data.
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"Overfitting often occurs when the training dataset is not representative of the broader population of possible inputs, causing the model to memorize specific patterns, including noise, rather than learning generalizable features." (Source: Amazon SageMaker Developer Guide, Model Evaluation and Overfitting) Detailed Explanation:
* Option A: The training dataset does not represent all possible input values.This is the correct answer. If the training dataset lacks diversity and does not cover the range of possible inputs, the model overfits by learning patterns specific to the training data, failing to generalize.
* Option B: The model contains a regularization method.Regularization methods (e.g., L2 regularization) are used to prevent overfitting, not cause it. This option is incorrect.
* Option C: The model training stops early because of an early stopping criterion.Early stopping is a technique to prevent overfitting by halting training when performance on a validation set degrades. It does not cause overfitting.
* Option D: The training dataset contains too many features.While too many features can contribute to overfitting (e.g., by increasing model complexity), this is less directly tied to overfitting than a non- representative dataset. The dataset's representativeness is the primary cause.
References:
Amazon SageMaker Developer Guide: Model Evaluation and Overfitting (https://docs.aws.amazon.com
/sagemaker/latest/dg/model-evaluation.html)
AWS AI Practitioner Learning Path: Module on Model Performance and Evaluation AWS Documentation: Understanding Overfitting (https://aws.amazon.com/machine-learning/)

NEW QUESTION # 29
A company wants to display the total sales for its top-selling products across various retail locations in the past 12 months.
Which AWS solution should the company use to automate the generation of graphs?

A. Amazon Q in AWS Chatbot
B. Amazon Q Developer
C. Amazon Q in Amazon EC2
D. Amazon Q in Amazon QuickSight

Answer: D

NEW QUESTION # 30
A digital devices company wants to predict customer demand for memory hardware. The company does not have coding experience or knowledge of ML algorithms and needs to develop a data-driven predictive model. The company needs to perform analysis on internal data and external data.
Which solution will meet these requirements?

A. Store the data in Amazon S3. Create ML models and demand forecast predictions by using Amazon SageMaker built-in algorithms that use the data from Amazon S3.
B. Import the data into Amazon SageMaker Data Wrangler. Create ML models and demand forecast predictions by using SageMaker built-in algorithms.
C. Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas.
D. Import the data into Amazon SageMaker Data Wrangler. Build ML models and demand forecast predictions by using an Amazon Personalize Trending-Now recipe.

Answer: C

Explanation:
I'll continue to format the rest. Let me know if you would like me to provide them all in one go or in parts.

NEW QUESTION # 31
A manufacturing company wants to create product descriptions in multiple languages.
Which AWS service will automate this task?

A. Amazon Transcribe
B. Amazon Kendra
C. Amazon Polly
D. Amazon Translate

Answer: D

Explanation:
The manufacturing company needs to create product descriptions in multiple languages, which requires automated language translation. Amazon Translate is a fully managed service that uses machine learning to provide high-quality translation between languages, making it the ideal solution for this task.
Exact Extract from AWS AI Documents:
From the Amazon Translate Developer Guide:
"Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. It can be used to automatically translate text, such as product descriptions, into multiple languages to reach a global audience." (Source: Amazon Translate Developer Guide, Introduction to Amazon Translate) Detailed Option A: Amazon TranslateThis is the correct answer. Amazon Translate automates the translation of text into multiple languages, directly addressing the company's need to create product descriptions in different languages.
Option B: Amazon TranscribeAmazon Transcribe converts speech to text, which is unrelated to translating text into multiple languages. This option is incorrect.
Option C: Amazon KendraAmazon Kendra is an intelligent search service that uses machine learning to provide answers from documents, not for translating text. This option is irrelevant.
Option D: Amazon PollyAmazon Polly is a text-to-speech service that generates spoken audio from text, not for translating text into other languages. This option does not meet the requirements.
Reference:
Amazon Translate Developer Guide: Introduction to Amazon Translate (https://docs.aws.amazon.com/translate/latest/dg/what-is.html) AWS AI Practitioner Learning Path: Module on Natural Language Processing Services AWS Documentation: Language Translation with Amazon Translate (https://aws.amazon.com/translate/)

NEW QUESTION # 32
A company wants to develop an educational game where users answer questions such as the following: "A jar contains six red, four green, and three yellow marbles. What is the probability of choosing a green marble from the jar?" Which solution meets these requirements with the LEAST operational overhead?

A. Use code that will calculate probability by using simple rules and computations.
B. Use unsupervised learning to create a model that will estimate probability density.
C. Use supervised learning to create a regression model that will predict probability.
D. Use reinforcement learning to train a model to return the probability.

Answer: A

Explanation:
The problem involves a simple probability calculation that can be handled efficiently by straightforward mathematical rules and computations. Using machine learning techniques would introduce unnecessary complexity and operational overhead.
Option C (Correct): "Use code that will calculate probability by using simple rules and computations": This is the correct answer because it directly solves the problem with minimal overhead, using basic probability rules.
Option A: "Use supervised learning to create a regression model" is incorrect as it overcomplicates the solution for a simple probability problem.
Option B: "Use reinforcement learning to train a model" is incorrect because reinforcement learning is not needed for a simple probability calculation.
Option D: "Use unsupervised learning to create a model" is incorrect as unsupervised learning is not applicable to this task.
AWS AI Practitioner Reference:
Choosing the Right Solution for AI Tasks: AWS recommends using the simplest and most efficient approach to solve a given problem, avoiding unnecessary machine learning techniques for straightforward tasks.

NEW QUESTION # 33
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