CertNexus Certification — AI Practitioner

Certified AI
Practitioner™

The industry-standard certification for AI and ML practitioners — validating your ability to design, implement, and deploy AI solutions across real-world business functions.

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Exam Code: AIP-210

80 Questions · 120 Minutes

Pearson VUE

Exam Items

Core Domains

60%

Pass Score

AI & ML Career Paths

Roles You Can Pursue
After CAIP Certification

The AI job market is the fastest-growing sector in global tech. CAIP holders qualify for practitioner, engineering, and leadership roles across industries.

Machine Learning Scientist

Data Scientist

Research Scientist

Applied Scientist

AI Developer

ML Engineer

UI/UX Designer

Robotics Process Analyst

Digital Knowledge Manager

Cognitive Copywriter

Intelligence Designer

BI Data Analyst

Director of Business Intelligence

Data Engineer

Robotics Scientist

AI Research Scientist

BI Developer

BI Analyst

Statistician

AI Researcher

Data Evangelist

Avatar Animator

Conversation Interface Writer

Content Interface Writer

Exam Specification

CAIP Exam Details

This exam certifies that the candidate has the knowledge and skill set of AI concepts, technologies, and tools to become a capable AI practitioner across a wide variety of AI-related job functions.

Target Candidate

Practitioners seeking to demonstrate a vendor-neutral, cross-industry skill set within AI and ML that enables them to design, implement, and hand off an AI solution or environment.

Exam Code

AIP-210

Launch Date

March 2023

Duration

120 Minutes

Includes candidate agreement + tutorial time

Passing Score

60%

59% depending on exam form

Number of Items

80 Questions

Multiple choice / multiple response

Exam Options

Pearson VUE

Test center or Pearson OnVUE online proctoring

Why Get Certified

Why the CAIP™ is the
AI Industry Standard

The Certified AI Practitioner™ has emerged as the benchmark credential for those seeking to confirm real-world AI and ML skills.

Prove Your Skills

Validate foundational knowledge of AI concepts, technologies, algorithms, and real-world applications.

Lead AI Teams

Verify that applicants and team members have the requisite skills to perform AI tasks effectively.

Vendor-Neutral

Cross-industry recognition valid across AWS, Azure, GCP, and on-premise AI stacks.

Higher Earning

Certified AI Practitioners earn up to 40% more than non-certified peers in equivalent ML and data roles.

Zetlan AI Training

Hands-On AI & Machine
Learning Training

Zetlan's CAIP training covers AI concepts while providing ample opportunities to practise the required skills of an ML professional — from model design through deployment.

Live Instructor-Led Training

Expert-guided sessions with real-world AI projects and lab exercises

Recorded Sessions

Access class recordings to revise at your own pace, anytime

Technical Support

Dedicated support via Call and WhatsApp on all working days

Exam Preparation

Mock tests, practice questions, and exam strategy included

Course Curriculum

CAIP Exam Domains

Three core domains covering the complete AI practitioner workflow — from problem framing through feature engineering to production deployment.

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

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

Research learning systems — identify business case for image recognition, NLP, speech recognition, predictive & recommendation systems, discovery & diagnostic systems, and robotics / autonomous systems

Analyze machine learning system use cases

Communicate effectively with stakeholders regarding AI project scope and expectations

Identify potential ethical concerns and regulatory considerations in AI implementations

Design machine and deep learning models — differentiate types of ML algorithms, differentiate types of DL algorithms, design for pattern recognition in predictive models

Optimize the algorithm — structure, run time, tuning hyperparameters for peak model performance

Train, validate, and test data subsets using rigorous evaluation methodologies

Evaluate the model using appropriate performance metrics and benchmarks

Address business risks, ethical concerns, and related concepts in training and tuning phases

Deploy a model into a production environment, including API integration and infrastructure configuration

Secure a pipeline — apply security best practices throughout the model development and maintenance lifecycle

Maintain the model post-production — monitor drift, retrain on new data, version management

Address business risks, ethical concerns, and related concepts in operationalizing and scaling the model

Certified AI Practitioner™

Roles You Can PursueAfter CAIP Certification

CAIP Exam Details

Why the CAIP™ is theAI Industry Standard

Hands-On AI & MachineLearning Training

CAIP Exam Domains

Ready to Become a Certified AI Practitioner?

Certified AI
Practitioner™

Roles You Can Pursue
After CAIP Certification

Why the CAIP™ is the
AI Industry Standard

Hands-On AI & Machine
Learning Training