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Amazon AIF-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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 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 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.

Amazon AWS Certified AI Practitioner Sample Questions (Q20-Q25):

NEW QUESTION # 20
Which functionality does Amazon SageMaker Clarify provide?

A. Identifies potential bias during data preparation
B. Documents critical details about ML models
C. Monitors the quality of ML models in production
D. Integrates a Retrieval Augmented Generation (RAG) workflow

Answer: A

NEW QUESTION # 21
Which term describes the numerical representations of real-world objects and concepts that AI and natural language processing (NLP) models use to improve understanding of textual information?

A. Binaries
B. Tokens
C. Embeddings
D. Models

Answer: C

NEW QUESTION # 22
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. Benchmark datasets
B. User-generated content
C. Content moderation guidelines
D. Moderation logs

Answer: A

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 References:
* Evaluating AI Models for Bias on AWS: AWS supports using benchmark datasets to assess model fairness and detect potential bias efficiently.

NEW QUESTION # 23
A company is implementing the Amazon Titan foundation model (FM) by using Amazon Bedrock. The company needs to supplement the model by using relevant data from the company's private data sources.
Which solution will meet this requirement?

A. Create an Amazon Bedrock knowledge base
B. Enable model invocation logging
C. Choose a lower temperature value
D. Use a different FM

Answer: A

Explanation:
Creating an Amazon Bedrock knowledge base allows the integration of external or private data sources with a foundation model (FM) like Amazon Titan. This integration helps supplement the model with relevant data from the company's private data sources to enhance its responses.
* Option C (Correct): "Create an Amazon Bedrock knowledge base": This is the correct answer as it enables the company to incorporate private data into the FM to improve its effectiveness.
* Option A: "Use a different FM" is incorrect because it does not address the need to supplement the current model with private data.
* Option B: "Choose a lower temperature value" is incorrect as it affects output randomness, not the integration of private data.
* Option D: "Enable model invocation logging" is incorrect because logging does not help in supplementing the model with additional data.
AWS AI Practitioner References:
* Amazon Bedrock and Knowledge Integration: AWS explains how creating a knowledge base allows Amazon Bedrock to use external data sources to improve the FM's relevance and accuracy.

NEW QUESTION # 24
A company has terabytes of data in a database that the company can use for business analysis. The company wants to build an AI-based application that can build a SQL query from input text that employees provide.
The employees have minimal experience with technology.
Which solution meets these requirements?

A. WaveNet
B. Residual neural network
C. Generative pre-trained transformers (GPT)
D. Support vector machine

Answer: C

Explanation:
Generative Pre-trained Transformers (GPT) are suitable for building an AI-based application that can generate SQL queries from natural language input provided by employees.
* GPT for Natural Language Processing:
* GPT models are designed for understanding and generating human-like text based on natural language input.
* They can be fine-tuned to interpret specific tasks, such as converting natural language queries into SQL queries.
* Why Option A is Correct:
* Natural Language Understanding: GPT is highly effective for tasks that require understanding of human language and generating structured outputs like SQL.
* User-Friendly: Requires minimal technology experience from employees, as they provide simple text input.
* Why Other Options are Incorrect:
* B. Residual neural network: Typically used in computer vision tasks, not for natural language- to-SQL conversion.
* C. Support vector machine: Used for classification tasks, not for generating structured queries from text.
* D. WaveNet: A deep generative model for audio data, unrelated to text-to-SQL tasks.

NEW QUESTION # 25
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