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CAIP – CertNexus Certified Artificial Intelligence Practitioner (Exam AIP-110)


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Course Schedule

1 Days Course
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Classroom + Online

Course Details


The Certified AI Practitioner (CAIP) exam requires no application fee, supporting documentation, or other eligibility verification measures for you to be eligible to take the exam. An exam voucher will come bundled with your training program or can be purchased separately here. Once purchased, you will receive more information about how to register for and schedule your exam through Pearson Vue. You can also purchase a voucher directly through Pearson Vue. Once you have obtained your voucher information, you can register for an exam time here. By registering, you agree to our Candidate Agreement included here.


This exam will certify that the candidate has the knowledge and skill set of AI concepts, technologies, and tools that will enable them to become a capable AI practitioner in a wide variety of AI-related job functions. To ensure exam candidates possess the aforementioned skills, the Certified AI Practitioner (CAIP) exam will test them on the following domains with the following weightings:

Domain % of Examination
1.0 Applied Artificial Intelligenceand Machine Learning in Business 5%
2.0 Problem Formulation 25%
3.0 Data Collection, Comprehension, Cleaning, and Engineering 20%
4.0 Algorithm Selection and Model Training 35%
5.0 Model Handoff 10%
6.0 Ethics and Oversight 5%
Total 100%

Target Audience

This certification exam is designed for practitioners who are seeking to demonstrate a vendor-neutral, cross-industry skill set within AI and with a focus on ML that will enable them to design, implement, and hand off an AI solution or environment.


While there are no formal prerequisites to register for and schedule an exam, we strongly recommend you first possess the following knowledge and skills:

● Explain how artificial intelligence (AI) and machine learning (ML) can solve business problems.

● Execute an applied ML workflow.

● Summarize outcomes of accepted learning algorithms.

● Formulate mathematical representations of business problems using domain insight.

● Develop and test hypothesis using experimental design.

● Distinguish benefits and drawbacks of various machine learning models and given a scenario select appropriate model and define tradeoffs.

● Given a scenario, select appropriate tool sets (both proprietary and open source).

● Demonstrate responsibility based upon ethical implications when sharing data sources.

● Plan, manage, train and hand off a ML model as part of a (software) solution.

● Communicate the findings of an AI and ML workflow and solution back to the organization.

● Identify the impact that propagating biases has within AI.

● Select and implement an appropriate algorithm for a given business problem.

● Select and implement the appropriate techniques for a given ML problem.

● Demonstrate a working level knowledge of development tools such as Python and R.