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Exam AIF-C01 Sample & Practice AIF-C01 Exam

After successful competition of the AIF-C01 certification, the certified candidates can put their career on the right track and achieve their professional career objectives in a short time period. However, to pass the AIF-C01 Exam you have to prepare well. For the quick AIF-C01 exam preparation the AIF-C01 Questions are the right choice.

Amazon AIF-C01 Exam Syllabus Topics:

Topic
Details

Topic 1

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 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

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

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 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.

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2025 Exam AIF-C01 Sample Pass Certify | Valid Practice AIF-C01 Exam: AWS Certified AI Practitioner

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Amazon AWS Certified AI Practitioner Sample Questions (Q38-Q43):

NEW QUESTION # 38
A company built an AI-powered resume screening system. The company used a large dataset to train the model. The dataset contained resumes that were not representative of all demographics. Which core dimension of responsible AI does this scenario present?

A. Transparency.
B. Explainability.
C. Fairness.
D. Privacy and security.

Answer: C

Explanation:
Fairness refers to the absence of bias in AI models. Using non-representative datasets leads to biased predictions, affecting specific demographics unfairly. Explainability, privacy, and transparency are important but not directly related to this scenario. References: AWS Responsible AI Framework.

NEW QUESTION # 39
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. Convert audio files to text documents by using ML models.
B. Run an application without provisioning or managing servers.
C. Create software snippets, reference tracking, and open-source license tracking.
D. Enable voice commands for coding and providing natural language search.

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 References:
* 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 # 40
An AI practitioner is using a large language model (LLM) to create content for marketing campaigns. The generated content sounds plausible and factual but is incorrect.
Which problem is the LLM having?

A. Underfitting
B. Hallucination
C. Data leakage
D. Overfitting

Answer: B

NEW QUESTION # 41
Which option is a characteristic of AI governance frameworks for building trust and deploying human-centered AI technologies?

A. Developing policies and guidelines for data, transparency, responsible AI, and compliance
B. Ensuring alignment with business standards, revenue goals, and stakeholder expectations
C. Expanding initiatives across business units to create long-term business value
D. Overcoming challenges to drive business transformation and growth

Answer: A

Explanation:
AI governance frameworks aim to build trust and deploy human-centered AI technologies by establishing guidelines and policies for data usage, transparency, responsible AI practices, and compliance with regulations. This ensures ethical and accountable AI development and deployment.
Exact Extract from AWS AI Documents:
From the AWS Documentation on Responsible AI:
"AI governance frameworks establish trust in AI technologies by developing policies and guidelines for data management, transparency, responsible AI practices, and compliance with regulatory requirements, ensuring human-centered and ethical AI deployment." (Source: AWS Documentation, Responsible AI Governance) Detailed Option A: Expanding initiatives across business units to create long-term business valueWhile expanding initiatives can drive value, it is not a core characteristic of AI governance frameworks focused on trust and human-centered AI.
Option B: Ensuring alignment with business standards, revenue goals, and stakeholder expectationsAlignment with business goals is important but not specific to AI governance frameworks for building trust and ethical AI deployment.
Option C: Overcoming challenges to drive business transformation and growthOvercoming challenges is a general business goal, not a defining characteristic of AI governance frameworks.
Option D: Developing policies and guidelines for data, transparency, responsible AI, and complianceThis is the correct answer. This option directly describes the core components of AI governance frameworks that ensure trust and ethical AI deployment.
Reference:
AWS Documentation: Responsible AI Governance (https://aws.amazon.com/machine-learning/responsible-ai/) AWS AI Practitioner Learning Path: Module on AI Governance AWS Well-Architected Framework: Machine Learning Lens (https://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/)

NEW QUESTION # 42
A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company wants to know how much information can fit into one prompt.
Which consideration will inform the company's decision?

A. Batch size
B. Temperature
C. Context window
D. Model size

Answer: C

Explanation:
The context window determines how much information can fit into a single prompt when using a large language model (LLM) like those on Amazon Bedrock.
* Context Window:
* The context window is the maximum amount of text (measured in tokens) that a language model can process in a single pass.
* For LLM applications, the size of the context window limits how much input data, such as text for sentiment analysis, can be fed into the model at once.
* Why Option B is Correct:
* Determines Prompt Size: The context window size directly informs how much information (e.
g., words or sentences) can fit in one prompt.
* Model Capacity: The larger the context window, the more information the model can consider for generating outputs.
* Why Other Options are Incorrect:
* A. Temperature: Controls randomness in model outputs but does not affect the prompt size.
* C. Batch size: Refers to the number of training samples processed in one iteration, not the amount of information in a prompt.
* D. Model size: Refers to the number of parameters in the model, not the input size for a single prompt.

NEW QUESTION # 43
......

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