CertNexus AIP-210 Exam Syllabus
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Before starting your AIP-210 exam preparation, it is recommended to review the complete CertNexus Certified Artificial Intelligence Practitioner Exam syllabus and carefully go through the exam objectives listed below. Once you understand the exam structure and objectives, you should practice using our free AIP-210 questions. We also provide premium AIP-210 practice test, fully updated according to the latest exam objectives, to help you accurately assess your preparedness for the actual exam.
CertNexus AIP-210 Exam Objectives
| Section | Weight | Objectives |
|---|---|---|
| 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 Objective 1.6 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 |
| Common Service Tasks and Tools | 2.1 Explain the Cisco device boot-up process 2.2 Identify common Cisco IOS commands 2.3 Identify tools for device file management 2.4 Confirm physical layer connectivity 2.5 Access devices remotely over a network 2.5.a Common Windows tools 2.6 Explain how to connect to the console port 2.7 Describe how to capture device status 2.8 Describe techniques for password recovery 2.9 Identify common tools for device replacement 2.10 Locate serial numbers on Cisco devices |
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| Official Information | https://certnexus.com/certified-artificial-intelligence-practitioner-caip/ |
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