IBM Artificial Intelligence Fundamental Associate Exam Syllabus

Use this quick start guide to collect all the information about IBM Artificial Intelligence Fundamental Associate (C1000-206) Certification exam. This study guide provides a list of objectives and resources that will help you prepare for items on the C1000-206 IBM Artificial Intelligence Fundamentals v1 - Associate exam. The Sample Questions will help you identify the type and difficulty level of the questions and the Practice Exams will make you familiar with the format and environment of an exam. You should refer this guide carefully before attempting your actual IBM Artificial Intelligence Fundamental Associate certification exam.

The IBM Artificial Intelligence Fundamental Associate certification is mainly targeted to those candidates who want to build their career in Data, Analytics, and AI domain. The IBM Certified Artificial Intelligence v1 - Associate exam verifies that the candidate possesses the fundamental knowledge and proven skills in the area of IBM Artificial Intelligence Fundamental Associate.

IBM Artificial Intelligence Fundamental Associate Exam Summary:

Exam Name	IBM Certified Artificial Intelligence v1 - Associate
Exam Code	C1000-206
Exam Price	$200 (USD)
Duration	60 mins
Number of Questions	44
Passing Score	70%
Books / Training	Introduction to Artificial Intelligence
Schedule Exam	Pearson VUE
Sample Questions	IBM Artificial Intelligence Fundamental Associate Sample Questions
Practice Exam	IBM C1000-206 Certification Practice Exam

IBM C1000-206 Exam Syllabus Topics:

Topic	Details	Weights
AI Foundations	- Define artificial intelligence (AI) - Differentiate AI, machine learning, and deep learning - Summarize the evolution of AI from symbolic systems to foundation models and generative AI - Differentiate structured, unstructured, and semi-structured data and explain their role in AI systems - Describe common AI application domains - Define generative AI and distinguish it from traditional predictive AI systems - Define foundation models, large language models, and AI agents - Explain how transformer-based models enable modern generative AI - Identify current capabilities and limitations of AI systems	25%
Machine Learning and Deep Learning Fundamentals	- Describe how machine learning systems are trained and used for prediction - Differentiate supervised, unsupervised, and reinforcement learning - Explain common machine learning problem types - Describe core machine learning algorithms - Explain the role of neural networks in deep learning - Compare classical machine learning and deep learning approaches - Describe basic model evaluation concepts - Explain overfitting and underfitting - Describe the high-level machine learning lifecycle	23%
Generative AI and Applied AI Concepts	- Identify common generative AI use cases across text, image, and video - Explain how large language models generate text using tokens - Describe foundational prompt engineering and tuning techniques - Define embeddings and explain their role in semantic similarity search - Describe the purpose and high-level function of vector databases in AI systems - Explain the concept of retrieval-augmented generation and how it reduces hallucinations - Identify common limitations of generative AI systems - Describe AI agents and explain how they can use tools or memory to complete tasks - Identify LLM categorizations	23%
Responsible AI, Ethics, Security, and Governance	- Explain the concept of AI Ethics - Explain the concept and core tenets of Responsible AI - Explain the concept and importance of AI Governance - Explain the concept and importance of AI Security	16%
AI Use Case Implementation and Business Integration	- Determine the most appropriate AI approach for a given use case scenario - Differentiate implementation strategies for generative AI and traditional machine learning systems - Compare retrieval-augmented generation and fine-tuning approaches for generative AI solutions - Identify integration patterns for AI systems - Identify AI related regulations - Identify monitoring and performance considerations for deployed AI systems	13%

To succeed in the IBM Artificial Intelligence Fundamental Associate certification exam, we recommend an authorized training course, practice tests and hands-on experience while you prepare for the IBM Artificial Intelligence Fundamentals v1 - Associate (C1000-206) exam. Have questions about the exam format, difficulty or passing score? Our IBM C1000-206 exam FAQs answer them.