The CAIP – CertNexus Certified Artificial Intelligence Practitioner training course prepares IT professionals and Information Technology Practitioners to enter the field of artificial intelligence. This course provides a vendor-neutral, cross-industry foundational knowledge of AI concepts, technologies, algorithms, and applications that will enable them to become a capable practitioner in a wide variety of AI-related job functions.
The CAIP training helps you prepare for theÂ AIP-110 exam available at Pearson Vue. This process 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. Once purchased, you will receive more information about how to register for and schedule your exam through Pearson Vue.
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 Intelligence and 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%|
This certification exam is designed for practitioners who are seeking to demonstrate a vendor-neutral, cross-industry skillset within AI and with a focus on ML that will enable them to design, implement, and handoff 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 the 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 toolsets (both proprietary and open-source).
- Demonstrate responsibility based on ethical implications when sharing data sources.
- Plan, manage, train and hand off an 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 of knowledge of development tools such as Python and R.