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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	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 4	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 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.

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Amazon AWS Certified AI Practitioner Sample Questions (Q276-Q281):

NEW QUESTION # 276
A company is using Amazon SageMaker to develop AI models.
Select the correct SageMaker feature or resource from the following list for each step in the AI model lifecycle workflow. Each SageMaker feature or resource should be selected one time or not at all. (Select TWO.) SageMaker Clarify SageMaker Model Registry SageMaker Serverless Inference

Answer:

Explanation:

NEW QUESTION # 277
A company is building an ML model. The company collected new data and analyzed the data by creating a correlation matrix, calculating statistics, and visualizing the data.
Which stage of the ML pipeline is the company currently in?

A. Exploratory data analysis
B. Hyperparameter tuning
C. Feature engineering
D. Data pre-processing

Answer: A

NEW QUESTION # 278
An e-commerce company wants to build a solution to determine customer sentiments based on written customer reviews of products.
Which AWS services meet these requirements? (Select TWO.)

A. Amazon Rekognition
B. Amazon Polly
C. Amazon Lex
D. Amazon Comprehend
E. Amazon Bedrock

Answer: D,E

Explanation:
To determine customer sentiments based on written customer reviews, the company can use Amazon Comprehend and Amazon Bedrock.
* Amazon Comprehend:
* A natural language processing (NLP) service that uses machine learning to uncover insights and relationships in text.
* Can analyze customer reviews to detect sentiments (positive, negative, neutral, or mixed) automatically.
* Amazon Bedrock:
* Provides access to foundational models (FMs) from multiple AI companies for tasks such as text generation, summarization, and sentiment analysis.
* The company can use a pre-trained sentiment analysis model available on Amazon Bedrock for processing customer reviews.
* Why Other Options are Incorrect:
* A. Amazon Lex: Used for building conversational interfaces like chatbots, not for sentiment analysis.
* C. Amazon Polly: Converts text to speech; it doesn't analyze sentiment.
* E. Amazon Rekognition: Analyzes images and videos, not text.

NEW QUESTION # 279
A company is using the Generative AI Security Scoping Matrix to assess security responsibilities for its solutions. The company has identified four different solution scopes based on the matrix.
Which solution scope gives the company the MOST ownership of security responsibilities?

A. Refining an existing third-party generative AI foundation model (FM) by fine-tuning the model by using data specific to the business.
B. Building an application by using an existing third-party generative AI foundation model (FM).
C. Building and training a generative AI model from scratch by using specific data that a customer owns.
D. Using a third-party enterprise application that has embedded generative AI features.

Answer: C

Explanation:
Building and training a generative AI model from scratch provides the company with the most ownership and control over security responsibilities. In this scenario, the company is responsible for all aspects of the security of the data, the model, and the infrastructure.
* Option D (Correct): "Building and training a generative AI model from scratch by using specific data that a customer owns": This is the correct answer because it involves complete ownership of the model, data, and infrastructure, giving the company the highest level of responsibility for security.
* Option A: "Using a third-party enterprise application that has embedded generative AI features" is incorrect as the company has minimal control over the security of the AI features embedded within a third-party application.
* Option B: "Building an application using an existing third-party generative AI foundation model (FM)" is incorrect because security responsibilities are shared with the third-party model provider.
* Option C: "Refining an existing third-party generative AI FM by fine-tuning the model with business- specific data" is incorrect as the foundation model and part of the security responsibilities are still managed by the third party.
AWS AI Practitioner References:
* Generative AI Security Scoping Matrix on AWS: AWS provides a security responsibility matrix that outlines varying levels of control and responsibility depending on the approach to developing and using AI models.

NEW QUESTION # 280
An AI practitioner wants to perform a contextual grounding check in Amazon Bedrock.
Which three components must the AI practitioner use?

A. Amazon Bedrock console, SDK, and source
B. Confidence score, user query, and filtering threshold
C. User query, model response, and grounding source
D. API credentials, grounding source, and model response

Answer: C

Explanation:
Amazon Bedrock Guardrails provides contextual grounding checks to determine whether foundation model responses are supported by reference information and relevant to user queries.
According to AWS documentation, contextual grounding checks require three components:
1. Grounding source: Reference information used to establish factual context.
2. User query: The question or request submitted by the user.
3. Model response: The generated content evaluated against the reference source and query.
These checks evaluate two important dimensions: grounding, which determines whether claims are supported by source information, and relevance, which determines whether responses address the user ' s question.
Option B includes API credentials, which are not a contextual grounding component. Option C describes development interfaces. Option D includes confidence scores and thresholds, which support evaluation but cannot replace the required source and response.
Therefore, option A is correct.
AWS Reference: https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails-contextual-grounding- check.html

NEW QUESTION # 281
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