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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.

Amazon AWS Certified AI Practitioner Sample Questions (Q14-Q19):

NEW QUESTION # 14
A hospital is developing an AI system to assist doctors in diagnosing diseases based on patient records and medical images. To comply with regulations, the sensitive patient data must not leave the country the data is located in. Which data governance strategy will ensure compliance and protect patient privacy?

A. Data residency
B. Data enrichment
C. Data discoverability
D. Data quality

Answer: A

Explanation:
Comprehensive and Detailed
Data residency ensures data is stored and processed within specific geographic or jurisdictional boundaries, meeting compliance requirements like HIPAA or GDPR.
Data quality refers to accuracy and consistency of data.
Data discoverability is about cataloging and searching datasets.
Data enrichment enhances datasets with additional external data.
Reference:
AWS Data Residency Guide

NEW QUESTION # 15
Which technique can a company use to lower bias and toxicity in generative AI applications during the post- processing ML lifecycle?

A. Human-in-the-loop
B. Data augmentation
C. Feature engineering
D. Adversarial training

Answer: A

Explanation:
The correct answer is A because Human-in-the-loop (HITL) is a post-processing strategy used to monitor, review, and filter outputs from generative AI models for toxicity, bias, or inappropriate content. It allows human reviewers to approve or reject model responses before they are delivered to end-users, ensuring alignment with ethical guidelines and company policies.
From the AWS documentation:
"Human-in-the-loop (HITL) workflows in generative AI are used to validate and approve outputs of models, especially in applications where content quality, compliance, or harm reduction is critical. HITL is a key step in responsible AI implementations to mitigate hallucinations, bias, and unsafe content." Explanation of other options:
B). Data augmentation is a pre-processing technique to increase data diversity, not typically used in post- processing stages.
C). Feature engineering is relevant in traditional ML, especially structured data tasks, not typically used in generative AI post-processing.
D). Adversarial training is a model training strategy, not a post-processing mitigation approach.
Referenced AWS AI/ML Documents and Study Guides:
* AWS Responsible AI Practices Whitepaper
* AWS Generative AI Developer Guide - Human-in-the-loop and Post-processing
* Amazon A2I Documentation - Integrating Human Review in ML Workflows

NEW QUESTION # 16
A company wants to build and deploy ML models on AWS without writing any code.
Which AWS service or feature meets these requirements?

A. Amazon SageMaker Canvas
B. Amazon Comprehend
C. AWS DeepRacer
D. Amazon Rekognition

Answer: A

NEW QUESTION # 17
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. Tokens
B. Models
C. Embeddings
D. Binaries

Answer: C

NEW QUESTION # 18
A company uses Amazon SageMaker AI to generate article summaries in multiple languages. The company needs a metric to evaluate the quality of the summary translations in multiple languages. Which evaluation metric will meet these requirements?

A. Bilingual evaluation understudy (BLEU)
B. Precision
C. Area Under the ROC Curve (AUC)
D. Recall-Oriented Understudy for Gisting Evaluation (ROUGE)

Answer: A

Explanation:
BLEU (Bilingual Evaluation Understudy) is the standard metric for evaluating machine translation quality across multiple languages.
ROUGE is for summarization quality (not translation).
AUC is for classification model performance.
Precision is a general metric but not specific for evaluating translations.
Reference:
AWS Documentation - Evaluation Metrics for NLP

NEW QUESTION # 19
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