Certified Artificial Intelligence Practitioner (AIP-210) Exam Voucher

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 capable AI practitioners in a wide variety of AI-related job functions.
 

This certification exam is designed for practitioners who are seeking to demonstrate a vendorneutral, 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. Exposure in a professional environment: 1 to 3 years.
 

There should be no prerequisites for the examination. However, the following background knowledge is recommended:
• Applied mathematics
• Math theory
• Statistical modeling procedures (linear algebra, probability, statistics, multivariate calculus, distributions like Poisson, normal, binomial, etc.)
• Programming abilities for ML and statistics (e.g., Python® and R)
• Ensemble learning
• Using algorithmic methods and frameworks (e.g., random forest or XGBoost)
• Proficiency with a querying language
• Strong communication skills
• Demonstrate responsibility based upon ethical implications when sharing data sources
• Familiarity with data visualization
You can obtain this level of skill and knowledge by taking the following courseware, which is available through training providers located around the world, or by attending an equivalent third-party training program:
• Introduction to Programming with Python®
• Python® Programming: Advanced
• Using Data Science Tools in Python®
• Data Wrangling with Python®
• Applied Data Science with Python® and Jupyter®
• Big Data Analysis with Python®
• Certified Ethical Emerging Technologist™ (CEET)
• CertNexus Certified Artificial Intelligence (AI) Practitioner™ (Exam CAIP-210)

Domain 1.0 Understanding the Artificial Intelligence Problem (26%) Objective 1.1 - Describe how artificial intelligence and machine learning are used to solve business (including commercial, government, public interest, and research) problems Objective 1.2 - Analyze the use cases of ML algorithms to rank them by their success probability Objective 1.3 - Research Learning Systems [Identify business case for image recognition; NLP; Speech recognition; Predictive & recommendation systems; Discovery & diagnostic systems; Robotics and autonomous systems] Objective 1.4 - Analyze machine learning system use cases Objective 1.5 - Communicate with stakeholders Identify potential ethical concerns Domain 2.0 Engineering Features for Machine Learning (20%) Objective 2.1 - Recognize relative impact of data quality and size to algorithms Objective 2.2 - Explain data collection/transformation process in ML workflow (transformations include standardization; normalization; log, square-root, and logit transformations) Objective 2.3 - Work with textual, numerical, audio, or video data formats Objective 2.4 - Transform numerical and categorical data Objective 2.5 - Address business risks, ethical concerns, and related concepts in data exploration/ feature engineering Domain 3.0 Training and Tuning ML Systems and Models (24%) Objective 3.1 - Design machine and deep learning models [Differentiate types of ML algorithms; differentiate types of DL algorithms; design for pattern recognition in predictive models] Objective 3.2 - Optimize the algorithm (e.g., structure, run time, tuning hyperparameters) Objective 3.3 - Train, validate, and test data subsets Objective 3.4 - Evaluate the model Objective 3.5 - Address business risks, ethical concerns, and related concepts in training and tuning Domain 4.0 Operationalizing ML Models (30%) Objective 4.1 - Deploy a model Objective 4.2 - Secure a pipeline (includes maintenance) Objective 4.3 - Maintain the model postproduction Objective 4.4 - Address business risks, ethical concerns, and related concepts in operationalizing the model