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Amazon AIF-C01 Exam Questions
- Topic 1: 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 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: 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: 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 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.
Free Amazon AIF-C01 Exam Actual Questions
Note: Premium Questions for AIF-C01 were last updated On Apr. 23, 2025 (see below)
An AI company periodically evaluates its systems and processes with the help of independent software vendors (ISVs). The company needs to receive email message notifications when an ISV's compliance reports become available.
Which AWS service can the company use to meet this requirement?
AWS Data Exchange is a service that allows companies to securely exchange data with third parties, such as independent software vendors (ISVs). AWS Data Exchange can be configured to provide notifications, including email notifications, when new datasets or compliance reports become available.
Option D (Correct): 'AWS Data Exchange': This is the correct answer because it enables the company to receive notifications, including email messages, when ISVs' compliance reports are available.
Option A: 'AWS Audit Manager' is incorrect because it focuses on assessing an organization's own compliance, not receiving third-party compliance reports.
Option B: 'AWS Artifact' is incorrect as it provides access to AWS's compliance reports, not ISVs'.
Option C: 'AWS Trusted Advisor' is incorrect as it offers optimization and best practices guidance, not compliance report notifications.
AWS AI Practitioner Reference:
AWS Data Exchange Documentation: AWS explains how Data Exchange allows organizations to subscribe to third-party data and receive notifications when updates are available.
A company deployed an AI/ML solution to help customer service agents respond to frequently asked questions. The questions can change over time. The company wants to give customer service agents the ability to ask questions and receive automatically generated answers to common customer questions. Which strategy will meet these requirements MOST cost-effectively?
RAG combines large pre-trained models with retrieval mechanisms to fetch relevant context from a knowledge base. This approach is cost-effective as it eliminates the need for frequent model retraining while ensuring responses are contextually accurate and up to date. Reference: AWS RAG Techniques.
A company is building a large language model (LLM) question answering chatbot. The company wants to decrease the number of actions call center employees need to take to respond to customer questions.
Which business objective should the company use to evaluate the effect of the LLM chatbot?
The business objective to evaluate the effect of an LLM chatbot aimed at reducing the actions required by call center employees should be average call duration.
Average Call Duration:
This metric measures the time taken to handle a customer call or query. A successful LLM chatbot should reduce the call duration by efficiently providing answers, minimizing the need for human intervention.
By decreasing the average call duration, the company can improve call center efficiency, reduce costs, and enhance the user experience.
Why Option B is Correct:
Direct Impact: The objective aligns directly with the goal of reducing the number of actions call center employees must take.
Operational Efficiency: Reducing call duration is a clear indicator of the chatbot's effectiveness in assisting customers without human help.
Why Other Options are Incorrect:
A . Website engagement rate: Is unrelated to call center operations.
C . Corporate social responsibility: Does not relate to call center efficiency.
D . Regulatory compliance: Is important but does not measure the effectiveness of a chatbot in reducing employee actions.
Which AWS feature records details about ML instance data for governance and reporting?
Amazon SageMaker Model Cards provide a centralized and standardized repository for documenting machine learning models. They capture key details such as the model's intended use, training and evaluation datasets, performance metrics, ethical considerations, and other relevant information. This documentation facilitates governance and reporting by ensuring that all stakeholders have access to consistent and comprehensive information about each model. While Amazon SageMaker Debugger is used for real-time debugging and monitoring during training, and Amazon SageMaker Model Monitor tracks deployed models for data and prediction quality, neither offers the comprehensive documentation capabilities of Model Cards. Amazon SageMaker JumpStart provides pre-built models and solutions but does not focus on governance documentation.
A student at a university is copying content from generative AI to write essays.
Which challenge of responsible generative AI does this scenario represent?
- Select Question Types you want
- Set your Desired Pass Percentage
- Allocate Time (Hours : Minutes)
- Create Multiple Practice tests with Limited Questions
- Customer Support
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