CertNexus AIP-210 Exam Topics
Certified Artificial Intelligence Practitioner Exam
Total Questions: 90What is Included in the CertNexus AIP-210 Exam?
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CertNexus AIP-210 Exam Overview :
| Exam Name | Certified Artificial Intelligence Practitioner Exam |
| Exam Code | AIP-210 |
| Actual Exam Duration | 120 minutes |
| Expected no. of Questions in Actual Exam | 80 |
| Official Information | https://certnexus.com/certified-artificial-intelligence-practitioner-caip/ |
| See Expected Questions | CertNexus AIP-210 Expected Questions in Actual Exam |
| Take Self-Assessment | Use CertNexus AIP-210 Practice Test to Assess your preparation - Save Time and Reduce Chances of Failure |
CertNexus AIP-210 Exam Topics :
| 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 |
Updates in the CertNexus AIP-210 Exam Topics:
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