Exam Name: CertNexus Certified Artificial Intelligence Practitioner (CAIP)
Certification Provider: CertNexus
Corresponding Certification: Certified AI Practitioner
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Updated: Sep 10, 2026
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CertNexus AIP-210 Exam Overview:
| Certification Vendor: | CertNexus |
|---|---|
| Exam Name: | CertNexus Certified Artificial Intelligence Practitioner (CAIP) |
| Exam Number: | AIP-210 |
| Exam Format: | Multiple Choice, Multiple Response |
| Available Languages: | English |
| Related Certifications: | Certified Ethical Emerging Technologist (CEET) |
| Exam Duration: | 120 minutes |
| Certificate Validity Period: | 3 years |
| Real Exam Qty: | 80 scored + 10 trial (90 total) |
| Exam Price: | $367.50 USD |
| Passing Score: | 60% (or 59% depending on exam form; forms are statistically equated) |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | In person at Pearson VUE test centers or online with Pearson OnVUE online proctoring |
| Pre Condition: | No mandatory prerequisites. Recommended: foundational knowledge of AI/ML concepts and programming experience (especially Python). |
| Official Syllabus URL: | https://certnexus.com/wp-content/uploads/2025/03/Certified-Artificial-Intelligence-Practitioner-blueprint-v-1.10_final.pdf |
CertNexus AIP-210 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Understanding the Artificial Intelligence Problem | 26% | - Analyze machine learning system use cases - Research Learning Systems
- Communicate with stakeholders - Identify potential ethical concerns - Describe how artificial intelligence and machine learning are used to solve business problems (including commercial, government, public interest, and research) |
| Topic 2: Engineering Features for Machine Learning | 20% | - Recognize relative impact of data quality and size to algorithms - Explain data collection/transformation process in ML workflow
- Work with textual, numerical, audio, or video data formats - Address business risks, ethical concerns, and related concepts in data exploration and feature engineering |
| Topic 3: Training and Tuning ML Systems and Models | 24% | - Evaluate the model - Design machine and deep learning models
- Train, validate, and test data subsets - Optimize the algorithm (e.g., structure, run time, tuning hyperparameters) |
| Topic 4: Operationalizing ML Models | 30% | - Deploy a model - Secure a pipeline (includes maintenance) - Maintain the model postproduction - Address business risks, ethical concerns, and related concepts in operationalizing the model |
CertNexus AIP-210 Exam Questions Answered
The AIP-210 exam leads to the Certified AI Practitioner certification from CertNexus. It is positioned at the Intermediate level. The exam validates the knowledge and skills expected of candidates preparing for CertNexus Certified Artificial Intelligence Practitioner (CAIP).
Related certifications include:
- Certified Ethical Emerging Technologist (CEET)
The AIP-210 exam includes 80 scored + 10 trial (90 total) questions and allows 120 minutes. Before test day, divide the available time by the number of questions in a timed practice set to establish a steady per-question pace. If a difficult item starts consuming too much time, mark it for review and move forward instead of losing momentum. A full timed practice session can help you decide when to move on, when to return, and how much buffer to reserve for a final check.
The passing score for AIP-210 is 60% (or 59% depending on exam form; forms are statistically equated), and the official exam fee is $367.50 USD. Because a retake requires paying the exam fee again, use timed practice results as a readiness check before scheduling your next attempt. If your performance remains inconsistent, continue reviewing weak domains and repeat a timed session before booking the exam.
No mandatory prerequisites. Recommended: foundational knowledge of AI/ML concepts and programming experience (especially Python).
Eligibility rules can change, so confirm the current requirements on the official exam page before registering.
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The current AIP-210 outline is organized into 4 top-level domains. The first listed areas include:
- Understanding the Artificial Intelligence Problem (26%)
- Operationalizing ML Models (30%)
- Training and Tuning ML Systems and Models (24%)
Review the Exam Topics section above for the complete outline and any nested subtopics.
CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions:
Your dependent variable data is a proportion. The observed range of your data is 0.01 to 0.99. The instrument used to generate the dependent variable data is known to generate low quality data for values close to 0 and close to 1. A colleague suggests performing a logit-transformation on the data prior to performing a linear regression. Which of the following is a concern with this approach?
Definition of logit-transformation
If p is the proportion: logit(p)=log(p/(l-p))
- A. Values near 0.5 before logit-transformation will be near 0 after.
- B. The model will be more likely to violate the assumption of normality.
- C. Noisy data could become more influential in your model.
- D. After logit-transformation, the data may violate the assumption of independence.
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Which of the following can take a question in natural language and return a precise answer to the question?
- A. Spark ML
- B. IBM Watson
- C. Databricks
- D. Pandas
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In which of the following scenarios is lasso regression preferable over ridge regression?
- A. The number of features is much larger than the sample size.
- B. There is high collinearity among some of the features associated with the dependent variable.
- C. The sample size is much larger than the number of features.
- D. There are many features with no association with the dependent variable.
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A classifier has been implemented to predict whether or not someone has a specific type of disease.
Considering that only 1% of the population in the dataset has this disease, which measures will work the BEST to evaluate this model?
- A. Recall and explained variance
- B. Precision and accuracy
- C. Mean squared error
- D. Precision and recall
A market research team has ratings from patients who have a chronic disease, on several functional, physical, emotional, and professional needs that stay unmet with the current therapy. The dataset also captures ratings on how the disease affects their day-to-day activities.
A pharmaceutical company is introducing a new therapy to cure the disease and would like to design their marketing campaign such that different groups of patients are targeted with different ads. These groups should ideally consist of patients with similar unmet needs.
Which of the following algorithms should the market research team use to obtain these groups of patients?
- A. k-nearest neighbors
- B. k-means clustering
- C. Logistic regression
- D. Naive-Bayes
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