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Amazon AIF-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 4
  • 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 5
  • 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.

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Amazon AWS Certified AI Practitioner Sample Questions (Q83-Q88):

NEW QUESTION # 83
Which statement presents an advantage of using Retrieval Augmented Generation (RAG) for natural language processing (NLP) tasks?

Answer: C

Explanation:
Comprehensive and Detailed
Retrieval-Augmented Generation (RAG) integrates external knowledge sources (databases, vector stores, document repositories) with LLMs, enabling them to generate contextually accurate and up-to-date responses without retraining.
B is incorrect: RAG does not speed up training; it improves inference results.
C is incorrect: speech recognition is not an RAG use case.
D is incorrect: computer vision augmentation is unrelated to RAG.
Reference:
AWS Documentation - Knowledge Bases for RAG in Amazon Bedrock


NEW QUESTION # 84
An AI practitioner has trained a model on a training dataset. The model performs well on the training data.
However, the model does not perform well on evaluation data. What is the MOST likely cause of this issue?

Answer: A

Explanation:
When a model performs well on training data but poorly on evaluation/test data, it indicates overfitting.
Overfitting: The model memorizes the training data patterns instead of generalizing.
Underfitting (A) means the model performs poorly on both training and test data.
Bias (C) refers to systemic errors in predictions, not this training/test mismatch.
Prompt engineering (B) applies to generative AI, not general ML training models.
# Reference:
AWS ML Glossary - Overfitting and Underfitting


NEW QUESTION # 85
What Is the benefit of fine-tuning a foundation model (FM)?

Answer: D


NEW QUESTION # 86
In which stage of the generative AI model lifecycle are tests performed to examine the model's accuracy?

Answer: D

Explanation:
The evaluation stage of the generative AI model lifecycle involves testing the model to assess its performance, including accuracy, coherence, and other metrics. This stage ensures the model meets the desired quality standards before deployment.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"The evaluation phase in the machine learning lifecycle involves testing the model against validation or test datasets to measure its performance metrics, such as accuracy, precision, recall, or task-specific metrics for generative AI models." (Source: AWS AI Practitioner Learning Path, Module on Machine Learning Lifecycle) Detailed Option A: DeploymentDeployment involves making the model available for use in production. While monitoring occurs post-deployment, accuracy testing is performed earlier in the evaluation stage.
Option B: Data selectionData selection involves choosing and preparing data for training, not testing the model's accuracy.
Option C: Fine-tuningFine-tuning adjusts a pre-trained model to improve performance for a specific task, but it is not the stage where accuracy is formally tested.
Option D: EvaluationThis is the correct answer. The evaluation stage is where tests are conducted to examine the model's accuracy and other performance metrics, ensuring it meets requirements.
Reference:
AWS AI Practitioner Learning Path: Module on Machine Learning Lifecycle Amazon SageMaker Developer Guide: Model Evaluation (https://docs.aws.amazon.com/sagemaker/latest/dg/model-evaluation.html) AWS Documentation: Generative AI Lifecycle (https://aws.amazon.com/machine-learning/)


NEW QUESTION # 87
A company wants to improve multiple ML models.
Select the correct technique from the following list of use cases. Each technique should be selected one time or not at all. (Select THREE.)
Few-shot learning Fine-tuning Retrieval Augmented Generation (RAG) Zero-shot learning

Answer:

Explanation:

Explanation:
AWS References:
Amazon Bedrock - Retrieval Augmented Generation (RAG)
AWS Generative AI Guide - Zero-shot & Few-shot learning


NEW QUESTION # 88
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