Use this quick start guide to collect all the information about CertNexus CAIP (AIP-210) Certification exam. This study guide provides a list of objectives and resources that will help you prepare for items on the AIP-210 CertNexus Artificial Intelligence Practitioner 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 CertNexus CAIP certification exam.
The CertNexus CAIP certification is mainly targeted to those candidates who want to build their career in Artificial Intelligence domain. The CertNexus Certified Artificial Intelligence Practitioner (CAIP) exam verifies that the candidate possesses the fundamental knowledge and proven skills in the area of CertNexus CAIP.
CertNexus CAIP Exam Summary:
| Exam Name | CertNexus Certified Artificial Intelligence Practitioner (CAIP) |
| Exam Code | AIP-210 |
| Exam Price | $368 (USD) |
| Duration | 120 mins |
| Number of Questions | 80 |
| Passing Score | 60% |
| Books / Training | CAIP Training |
| Schedule Exam | Pearson VUE |
| Sample Questions | CertNexus CAIP Sample Questions |
| Practice Exam | CertNexus AIP-210 Certification Practice Exam |
CertNexus AIP-210 Exam Syllabus Topics:
| Topic | Details | Weights |
|---|---|---|
| Understanding the Artificial Intelligence Problem |
- Describe how artificial intelligence and machine learning are used to solve business (including commercial, government, public interest, and research) problems - Analyze the use cases of ML algorithms to rank them by their success probability - Research Learning Systems [Identify business case for image recognition; NLP; Speech recognition; Predictive & recommendation systems; Discovery & diagnostic systems; Robotics and autonomous systems] - Analyze machine learning system use cases - Communicate with stakeholders - Identify potential ethical concerns |
26% |
| Engineering Features for Machine Learning |
- Recognize relative impact of data quality and size to algorithms - Explain data collection/transformation process in ML workflow (transformations include standardization; normalization; log, square-root, and logit transformations) - Work with textual, numerical, audio, or video data formats - Transform numerical and categorical data - Address business risks, ethical concerns, and related concepts in data exploration/ feature engineering |
20% |
| Training and Tuning ML Systems and Models |
- Design machine and deep learning models [Differentiate types of ML algorithms; differentiate types of DL algorithms; design for pattern recognition in predictive models] - Optimize the algorithm (e.g., structure, run time, tuning hyperparameters) - Train, validate, and test data subsets - Evaluate the model - Address business risks, ethical concerns, and related concepts in training and tuning |
24% |
| Operationalizing ML Models |
- Deploy a model - Secure a pipeline (includes maintenance) - Maintain the model postproduction - Address business risks, ethical concerns, and related concepts in operationalizing the model |
30% |
To ensure success in CertNexus CAIP certification exam, we recommend authorized training course, practice test and hands-on experience to prepare for CertNexus Artificial Intelligence Practitioner (AIP-210) exam.