CERTNEXUS

Certified Artificial Intelligence Practitioner (CAIP)

AIP-210

The Certified Artificial Intelligence Practitioner (CAIP) certification validates your ability to design, implement, and hand off AI solutions and environments. It is for professionals who want to demonstrate practical skills in AI and machine learning concepts, technologies, and tools. Earning CAIP shows you can apply AI in a wide variety of job functions and drive measurable business outcomes.

Practice Free Questions

4Domains

20Objectives

120Practice pages

558Free questions

AIP-210 free practice questions

Choose any objective and open any question page — no sign-in required.

Objective 1.1 Describe how artificial intelligence and machine learning are used to solve business (including commercial, government, public interest, and research) problems

27 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–27

Objective 1.2 Analyze the use cases of ML algorithms to rank them by their success probability

26 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–26

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]

28 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–28

Objective 1.4 Analyze machine learning system use cases

30 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–30

Objective 1.5 Communicate with stakeholders

26 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–26

Objective 1.6 Identify potential ethical concerns

12 questions · 3 pages

Questions 1–5 6–10 11–12

Objective 2.1 Recognize relative impact of data quality and size to algorithms

25 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–25

Objective 2.2 Explain data collection/transformation process in ML workflow (transformations include standardization; normalization; log, square-root, and logit transformations)

27 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–27

Objective 2.3 Work with textual, numerical, audio, or video data formats

24 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–24

Objective 2.4 Transform numerical and categorical data

24 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–24

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]

17 questions · 4 pages

Questions 1–5 6–10 11–15 16–17

Objective 3.2 Optimize the algorithm (e.g., structure, run time, tuning hyperparameters)

30 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–30

Objective 3.3 Train, validate, and test data subsets

28 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–28

Objective 3.4 Evaluate the model

37 questions · 8 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–30 31–35 36–37

Objective 4.1 Deploy a model

39 questions · 8 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–30 31–35 36–39

Objective 4.2 Secure a pipeline (includes maintenance)

32 questions · 7 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–30 31–32

Objective 4.3 Maintain the model postproduction

32 questions · 7 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–30 31–32

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Percentages reflect share of the current practice bank, not official exam weightings. Every page above is a live, crawlable practice page (20 objectives · 120 pages).

Certified Artificial Intelligence Practitioner (CAIP)

AIP-210 free practice questions

Domain 1 · 1.0 Understanding the Artificial Intelligence Problem27% of practice bank · 6 objectives · 149 questions27%

Domain 2 · 2.0 Engineering Features for Machine Learning24% of practice bank · 5 objectives · 132 questions24%

Domain 3 · 3.0 Training and Tuning ML Systems and Models26% of practice bank · 5 objectives · 145 questions26%

Domain 4 · 4.0 Operationalizing ML Models24% of practice bank · 4 objectives · 132 questions24%