You can see it againUnder pressure people bring back shapes and positions long after the wording has gone.
Picture superiority · Shepard 1967, Standing 1973
CERTNEXUSCertified Artificial Intelligence Practitioner (CAIP)
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.
Exam formatMultiple choice and performance-based
DeliveryPearson VUE
Free questions558
Content last reviewed 16 February 2026 · Up to date
The certification
What AIP-210 proves, and what it asks of you
What this certification covers, who it is written for, and what the exam itself looks like on the day.
4domains
20objectives
141concepts
US $304exam fee
What it is
What this certification is
What it validates, who it is written for, and the experience it assumes.
About this certification
The Certified Artificial Intelligence Practitioner (CAIP) certification validates the knowledge, skills, and abilities required to design, implement, and hand off an AI solution or environment. It is designed for professionals who want to demonstrate they can apply AI and machine learning concepts, technologies, and tools in real-world scenarios, from problem definition to deployment.
Earning CAIP proves you have the practical skills to become a capable AI practitioner in a wide variety of AI-related job functions. The certification covers the full lifecycle of AI projects, including data preparation, model building, and deployment, ensuring you can deliver AI solutions that meet business needs and drive value.
Who it’s for
This certification is for professionals who want to validate their skills in designing, implementing, and handing off AI solutions. It is ideal for data scientists, machine learning engineers, AI developers, and IT professionals who work with AI technologies and want to demonstrate their ability to apply AI in a business context. You should have a foundational understanding of AI and machine learning concepts, as well as experience with data analysis and programming. The certification is designed for individuals who are ready to take on AI projects and want to prove their capability to stakeholders and employers.
Recommended experience
While there are no formal prerequisites, CertNexus recommends that candidates have a basic understanding of AI and machine learning concepts, as well as some experience with data analysis and programming. Familiarity with AI and machine learning concepts and terminology; Experience with data preparation and analysis; Basic programming skills, preferably in Python; Understanding of how AI solutions are deployed and integrated into business processes
The syllabus
What you’ll learn
Every domain and objective CertNexus measures, with the weight they carry on the exam.
The official CertNexus exam outline · checked 16 February 2026 · See the source
1.0 Understanding the Artificial Intelligence Problem
- 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
2.0 Engineering Features for Machine Learning
- 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
3.0 Training and Tuning ML Systems and Models
- 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
4.0 Operationalizing ML Models
- 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
On the day
The exam itself
Everything CertNexus publishes about sitting it, and nothing we inferred.
Prerequisites
No mandatory prerequisites — this certification has no required predecessor exam or credential.
Exam codeAIP-210
CertificationCertified Artificial Intelligence Practitioner (CAIP)
Exam formatMultiple choice and performance-based
DeliveryPearson VUE
LanguagesEnglish
PricingUS $304
Certification levelPractitioner
After you pass
Where this credential goes next
The path CertNexus lays out, how the credential is kept, and where to book.
Step-by-step path to Certified Artificial Intelligence Practitioner (CAIP)
Credential earnedCertified Artificial Intelligence Practitioner (CAIP) Practitioner level certification Lifecycle status
This certification is currently active and available. CertNexus maintains this certification to validate current skills and industry relevance.
Exam status: ActiveMaintained by CertNexus
Exam registration
Register for the exam through Pearson VUE, CertNexus’s authorized testing partner.
Schedule your examVisit the official CertNexus certification page for exam policies and requirements.
View the official pageYour coach
And when you are serious, your coach Pip takes over
Your coach in the app reads what you have answered with the book closed and tells you one thing to do tonight. It will not count an answer you gave with the page open, and it will tell you when you are not ready.
See how the coach worksBefore you book
Questions people ask
How does the CAIP certification relate to other CertNexus certifications?
CAIP is a standalone certification focused on AI and machine learning. It is part of CertNexus's portfolio that also includes certifications for data science (CDSP), cybersecurity (CFR), and IoT (CIoTP). Each certification has its own exam code and can be earned independently.
Is there a lower-level certification required before taking AIP-210?
No. CertNexus does not require any prerequisite certification for AIP-210. You can take the exam directly, regardless of your prior certifications.
Can I take the AIP-210 exam online?
Yes, the AIP-210 exam is available online via Pearson VUE's OnVUE platform, as well as at Pearson VUE test centers. You must meet the technology and environment requirements for online testing.
What are the retake policies for the AIP-210 exam?
CertNexus vouchers come with a free retake. You can use the same voucher to schedule a retake if you do not pass on the first attempt. Standard waiting periods may apply between attempts.
Are there any hands-on or lab components in the AIP-210 exam?
The AIP-210 exam includes performance-based questions that assess your ability to apply AI and ML concepts in practical scenarios. These may involve interpreting data, evaluating models, or making decisions about deployment.
What job roles does the CAIP certification map to?
The CAIP certification is designed for professionals in AI-related roles such as AI practitioner, machine learning engineer, data scientist, and AI developer. It validates skills applicable across a wide variety of job functions.
Can I recertify by passing a different CertNexus exam?
CertNexus does not publish a recertification policy for CAIP. If you are interested in maintaining your credential, check the CertNexus website for the latest information.
Are there any regional restrictions for taking the AIP-210 exam?
CertNexus policy prohibits individuals residing in the embargoed territories of Iran, North Korea, Syria, and Sudan from taking a CertNexus test or becoming certified.
Information freshness · Content last reviewed on 2026-02-16 Up to date
Practice free questions 558 questions, free, no account needed.
The curriculum
The whole exam, mapped and written
Every domain, every objective, and every concept CertNexus measures — each one written out.
4domains
20objectives
141concepts
120free pages
The domains, and what sits inside them
Every objective below is a page you can open and practise now, without an account.
The official CertNexus exam outline · checked 16 February 2026 · See the source
11.0 Understanding the Artificial Intelligence Problem149 questions
Objective 1.1 Describe how artificial intelligence and machine learning are used to solve business (including commercial, government, public interest, and research) problems7 concepts · 6 pagesOpen →Objective 1.2 Analyze the use cases of ML algorithms to rank them by their success probability7 concepts · 6 pagesOpen →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]8 concepts · 6 pagesOpen →Objective 1.4 Analyze machine learning system use cases6 concepts · 6 pagesOpen →Objective 1.5 Communicate with stakeholders6 concepts · 6 pagesOpen →Objective 1.6 Identify potential ethical concerns1 concepts · 3 pagesOpen →
22.0 Engineering Features for Machine Learning132 questions
Objective 2.1 Recognize relative impact of data quality and size to algorithms6 concepts · 5 pagesOpen →Objective 2.2 Explain data collection/transformation process in ML workflow (transformations include standardization; normalization; log, square-root, and logit transformations)8 concepts · 6 pagesOpen →Objective 2.3 Work with textual, numerical, audio, or video data formats6 concepts · 5 pagesOpen →Objective 2.4 Transform numerical and categorical data9 concepts · 5 pagesOpen →Objective 2.5 Address business risks, ethical concerns, and related concepts in data exploration/feature engineering7 concepts · 7 pagesOpen →
33.0 Training and Tuning ML Systems and Models145 questions
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]3 concepts · 4 pagesOpen →Objective 3.2 Optimize the algorithm (e.g., structure, run time, tuning hyperparameters)9 concepts · 6 pagesOpen →Objective 3.3 Train, validate, and test data subsets10 concepts · 6 pagesOpen →Objective 3.4 Evaluate the model10 concepts · 8 pagesOpen →Objective 3.5 Address business risks, ethical concerns, and related concepts in training and tuning8 concepts · 7 pagesOpen →
44.0 Operationalizing ML Models132 questions
Objective 4.1 Deploy a model10 concepts · 8 pagesOpen →Objective 4.2 Secure a pipeline (includes maintenance)8 concepts · 7 pagesOpen →Objective 4.3 Maintain the model postproduction6 concepts · 7 pagesOpen →Objective 4.4 Address business risks, ethical concerns, and related concepts in operationalizing the model6 concepts · 6 pagesOpen →
Not a table of contents
Every line on this map already opens
In front of every objective the practice pages are already there, free and without an account. This is one objective, opened.
Objective 1.1 Describe how artificial intelligence and machine learning are used to solve business (including commercial, government, public interest, and research) problems6 pagesOpen →
Free in front of it
27 questions on this objective, five to a page. Every range above is a real page, open now, with no account.
Reading the map is not the same as knowing the ground
The curriculum tells you what is on the exam. Proving you know it is a different job — and it is the one the closed-book run does.
Free questions
558 questions. No account, no card.
The whole bank is open. 5 questions to a page, every answer explained, and a discussion thread on each one.
558questions
120pages
20objectives
Freealways
Browse by domain
Every objective, and every page range, is a link — so you can pick up exactly where you left off.
11.0 Understanding the Artificial Intelligence Problem149 questions
22.0 Engineering Features for Machine Learning132 questions
Objective 2.3 Work with textual, numerical, audio, or video data formats24 questions · 5 pagesOpen →
33.0 Training and Tuning ML Systems and Models145 questions
44.0 Operationalizing ML Models132 questions
What a practice page gives you
1Five to a page
Short enough to finish, long enough to matter.
2Every answer explained
Not only which one is right — why the others are wrong.
3A discussion on each
Ask, answer, and vote. Every question has its own thread.
A question, and the idea behind it
Every question comes from a concept
These are not trivia. Each one is written against a concept in the book, so when you get one wrong there is somewhere to go and find out why.
The visual book
See it once. Carry it into the exam.
The pages shown here come from our AI-900 book — an example of how each concept is written in plain language and, where the idea needs one, drawn as a full page you can take in at a glance.
141concepts
558free questions
Why a picture, and not another paragraph
Three reasons, and each one is a real finding rather than a slogan.
You can see it againThe whole idea at onceWhere it starts, what happens in the middle, what comes out, and the mistake to avoid.
Multimedia principle · MayerThe look-alikes sit togetherThe pairs the exam tests are drawn side by side, so the difference is seen, not told.
Dual coding · PaivioWhat one page is made of
You are never asked to read a poster here — only to see how one is built. After that, every other page is legible at a glance.
1A numbered strip on top
The idea as a sequence, followed with a finger before a word is read.
2Panels for the parts
What it is, how the machine learns it, when it is the right tool.
3The trap, framed
The distinction the exam tests, given its own box instead of buried in prose.
4One line at the bottom
The sentence to carry into the exam room.
Open book, on purpose
Answer it with the picture beside you
This is the part that teaches. The illustration and the written explanation stay where they are while you work, so a scenario stops being a memory test and becomes something you can simply look at.
It teaches · it does not count
A smartphone uses AI to unlock when the owner looks at the camera. Which AI capability is being used?
AImage classification
BFacial recognition
COptical character recognition
DObject detection
Look at the five steps. Detect finds a face, align straightens it, encode turns it into numbers, search compares it with a stored face, and decide answers one question: is this the same person? Classification would only say “a face”. Detection would only draw a box. Neither decides whose.
Then the page shuts, and it starts counting
The same questions come back with the book closed — that run is the one that counts. After it, your coach picks one thing for tonight, sized to the time you have, and brings pages back before you lose them.
Testing effect · Roediger & Karpicke 2006 · spacing effect · Cepeda et al. 2006
Official resources
Straight from CertNexus
Where the exam is defined, scheduled and scored.
3official links
141concepts mapped
The source
CertNexus’s own pages
We link to them rather than repeat them, so nothing here goes stale behind them.
Checked against the source
Every objective on the official CertNexus outline already has questions here
We build from the official skills outline, not from a summary of it — 20 objectives, 141 concepts written under them, and free questions against every one. When CertNexus changes the outline, this page changes with it.
20objectives on the outline→141concepts written→558free questions
An official page tells you what will be asked. It cannot tell you what you will get right.
That is the only question worth answering the night before, and no link answers it. You answer it by taking the questions with the book closed, and seeing what comes back.