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Amazon AIF-C01 Exam Syllabus Topics:

TopicDetails
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
  • 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 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
  • 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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Amazon AWS Certified AI Practitioner Sample Questions (Q86-Q91):

NEW QUESTION # 86
A company has implemented a generative AI solution to create personalized exercise routines for premium subscription users. The company offers free basic subscriptions and paid premium subscriptions. The company wants to evaluate the AI solution's return on investment over time.

Answer: C

Explanation:
The correct answer is C, as conversion rate (how many users upgrade to premium) and retention rate (how many stay subscribed) are primary indicators of a generative AI system's business impact and ROI.
According to AWS documentation, evaluating AI performance should go beyond technical accuracy to include business metrics that show real-world value. For a premium service, higher conversion implies successful personalization that attracts free users, while strong retention reflects sustained engagement and user satisfaction. Metrics like ARPU or query reduction are secondary indicators. AWS emphasizes outcome- based measurement to ensure AI initiatives contribute measurable ROI tied to customer adoption, loyalty, and profitability. Therefore, tracking conversion and retention gives the clearest long-term measure of financial success from generative AI.
Referenced AWS AI/ML Documents and Study Guides:
AWS Machine Learning Specialty Guide - ML Business Metrics
AWS AI Adoption Framework - Value Realization and ROI Tracking


NEW QUESTION # 87
A security company is using Amazon Bedrock to run foundation models (FMs). The company wants to ensure that only authorized users invoke the models. The company needs to identify any unauthorized access attempts to set appropriate AWS Identity and Access Management (IAM) policies and roles for future iterations of the FMs.
Which AWS service should the company use to identify unauthorized users that are trying to access Amazon Bedrock?

Answer: D


NEW QUESTION # 88
A retail store wants to predict the demand for a specific product for the next few weeks by using the Amazon SageMaker DeepAR forecasting algorithm.
Which type of data will meet this requirement?

Answer: A

Explanation:
Amazon SageMaker's DeepAR is a supervised learning algorithm designed for forecasting scalar (one- dimensional) time series data. Time series data consists of sequences of data points indexed in time order, typically with consistent intervals between them. In the context of a retail store aiming to predict product demand, relevant time series data might include historical sales figures, inventory levels, or related metrics recorded over regular time intervals (e.g., daily or weekly). By training the DeepAR model on this historical time series data, the store can generate forecasts for future product demand. This capability is particularly useful for inventory management, staffing, and supply chain optimization. Other data types, such as text, image, or binary data, are not suitable for time series forecasting tasks and would not be appropriate inputs for the DeepAR algorithm.
Reference: Amazon SageMaker DeepAR Algorithm


NEW QUESTION # 89
An AI practitioner is determining the appropriate data type for various use cases.
Select the correct data type from the following list for each use case. Select each data type one time.

Answer:

Explanation:

Explanation:
Sentiment analysis # Text data
Traffic sign recognition # Image data
Customer demographics & purchase history # Tabular data
Stock price forecasting # Time series data
AWS classifies NLP tasks like sentiment analysis under text data
Computer vision tasks such as object and sign recognition use image data Structured rows and columns (demographics, transactions) are tabular data Sequential data indexed by time (prices, metrics) is time series data


NEW QUESTION # 90
An AI practitioner is writing software code. The AI practitioner wants to quickly develop a test case and create documentation for the code.

Answer: A

Explanation:
Comprehensive and Detailed
Amazon Q Developer is an AI-powered coding assistant integrated into IDEs (e.g., VS Code, JetBrains). It can:
Generate unit tests.
Create documentation.
Suggest code completions.
This is the fastest and most effective solution for this scenario.
Reference:
Amazon Q Developer - AWS Documentation


NEW QUESTION # 91
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