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CERTIFIED AI ENGINEER

This exam covers model development. Candidates will demonstrate their ability to build and deploy AI models.

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Course

Self-paced Course

Hands-on labs, projects and quizzes. Learn on your schedule.

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Assessment

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Timed, real-format questions with instant feedback & review.

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Eligibility
Eligibility
Exam Structure
Exam Structure
Sample Test
Sample Test
Question Bank
Question Bank
Exam Platform
Exam Platform
Transcript
Transcript
Career path
Career path
Certification Pathway
Certification Pathway

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Eligibility

The Certified AI Engineer exam is intended for professionals and students who wish to validate their ability to build, deploy, and manage AI solutions in real-world environments.

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Educational Background
A Bachelor's degree in computer science, engineering, mathematics, statistics, or a related field.
Alternatively, equivalent professional experience.
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Professional Experience
1 - 2 years of experience in AI, machine learning, or data engineering projects.
Familiarity with at least one programming language such as Python, R, or JavaScript.
Prerequisites Icon
Technical Prerequisites

Basic Understanding of :

Machine learning concepts (supervised, unsupervised learning)
Cloud platforms (Azure, AWS, or GCP)
Version control (Git)
Data Visualization tools (Power BI, Tableau, or equivalent)
Courses Icon
Recommended Preparatory Courses
AI - 900: AI Fundamentals
Python for Data Engineering
GloAI Self - Paced AI Engineer Course

Exam Icon
Exam Structure

The Certified AI Engineer exam assesses your ability to design, train, evaluate, and deploy AI models in real-world environments. The test combines knowledge-based and scenario-based questions to validate both theoretical understanding and practical application.

Exam Format

Exam Type Online, Proctored
Duration 120 Minutes
Questions Types Multiple Choice, Case Studies, Scenario-Based
Number of Questions 50–60
Difficulty Level Intermediate to Advanced
Passing Score 70%
Languages English
Mode Web-based (GloAI Exam Portal)

Domains Covered & Weightage

Domain Weightage Description
AI Fundamentals 15% Concepts of AI, ML, and DL
Model Development 25% Data preparation, training, tuning
Model Deployment 25% CI/CD, monitoring, retraining
Ethical & Responsible AI 15% Fairness, transparency, compliance
Real-World Case Studies 20% Applied scenarios using AI tools

Scoring & Results

  • Each questions carries equal weight
  • No negative marking.
  • Passing criteria: $\ge 70\%$ (35 out of 50 questions).
  • Candidates receive instant provisional results on completion.
  • Official certificate and transcript available within 48 hours.

Retake Policy

  • You can retake the exam up to 2 times within 6 months.
  • Each retake requires a separate exam purchase.
  • GloAI recommends completing the Practice Exam before retaking.

Sample Test Icon
Sample Test

Get a glimpse of the question style and difficulty level before you take the full exam.

Example Question 1 – Multiple Choice

Question:

An AI engineer is developing a model that classifies images of animals. The dataset is large and labeled. Which type of learning is most appropriate?

A. Unsupervised Learning
B. Supervised Learning
C. Reinforcement Learning
D. Transfer Learning

Correct Answer: B

Explanation: Supervised learning is used when **labeled data** is available for training a classification model.

Example Question 2 – Scenario Based

Scenario:

You've deployed a sentiment analysis model to a production API. Users report that the model gives inconsistent results during peak traffic hours.

Question:

What's the most likely cause?

A. Data drift in the model training set
B. Latency in model monitoring system
C. Insufficient compute resources on deployment server
D. Model version mismatch

Correct Answer: C

Explanation: Performance issues during load spikes are usually caused by **inadequate compute allocation or autoscaling limits.**

Example Question 3 – Case Study

Case Summary:

A healthcare organization wants to automate patient triage using AI. The model must comply with data privacy laws and provide explainable results.

Question:

Which approach should the AI Engineer prioritize?

A. Use deep learning with no interpretation layers
B. Implement a black-box model for higher accuracy
C. Use explainable AI (XAI) techniques
D. Collect more data from social media sources

Correct Answer: C

Explanation: In healthcare, **explainability and compliance** are essential; XAI methods ensure model transparency.

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Certified AI Engineer

Certified AI Engineer

This exam will assess your ability to build and deploy AI models

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Sample Question Panel
Question:

What is the primary goal of supervised learning?

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Question Review Panel

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Secure Proctoring

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Real World Scenarios

Interactive case-based questions for practical skill validation

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Transcript

Official Transcript
Certified AI Engineer Certificate
Career Path icon

Career Path

A progression from entry to leadership, aligned with certifications.

Entry-Level Roles
After CAIP / CAIE
  • AI Practitioner
  • Junior AI Engineer
  • Data Analyst / AI Technician
Intermediate Roles
After CRAIS / CGAIS / Technical Specializations
  • Machine Learning Engineer
  • Data Scientist
  • Generative AI Developer
  • Computer Vision Engineer
  • Cloud AI Engineer
  • Responsible AI Specialist
Advanced Roles
After Industry or Specialized Certifications
  • AI Consultant (Finance, Healthcare, Manufacturing, etc.)
  • AI Solutions Architect
  • AI Research Engineer
  • AI Security & Privacy Specialist
Leadership Roles
After CAIL / CAITO / CAIGR
  • AI Product Manager
  • Head of AI / Director of Data Science
  • AI Transformation Officer
  • Chief AI Officer (CAIO)
  • AI Governance & Risk Director
Entry-Level Roles
After CAIP / CAIE
  • AI Practitioner
  • Junior AI Engineer
  • Data Analyst / AI Technician
Advanced Roles
After Industry or Specialized Certifications
  • AI Consultant (Finance, Healthcare, Manufacturing, etc.)
  • AI Solutions Architect
  • AI Research Engineer
  • AI Security & Privacy Specialist
Career path timeline
Intermediate Roles
After CRAIS / CGAIS / Technical Specializations
  • Machine Learning Engineer
  • Data Scientist
  • Generative AI Developer
  • Computer Vision Engineer
  • Cloud AI Engineer
  • Responsible AI Specialist
Leadership Roles
After CAIL / CAITO / CAIGR
  • AI Product Manager
  • Head of AI / Director of Data Science
  • AI Transformation Officer
  • Chief AI Officer (CAIO)
  • AI Governance & Risk Director

Career Growth Ladder

Level Typical Roles Relevant Certifications
Entry-LevelAI Technician, Data AnalystCAIP
Mid-LevelAI Engineer, ML Engineer, AI DeveloperCAIE, CRAIS, CGAIS
AdvancedAI Architect, AI Consultant, Security SpecialistCLLMS, CCVE, CAICE, CAISPS, Industry Certs
LeadershipHead of AI, AI Product Manager, CAIOCAIL, CAITO, CAIGR, CEAIR

Certification Pathway

GlofAI Learning Path (Certification Progression)

01
Foundation
02
Core
03
Specialized
04
Industry
05
Leadership
01
Stage 1 — Foundation
Goal: Build baseline AI knowledge and confidence.
Certified AI Practitioner (CAIP) – Foundational skills in AI, data, and ML.
02
Stage 2 — Core AI Competency
Goal: Master model development and deployment.
  • Certified AI Engineer (CAIE) – Core technical certification.
  • Certified Responsible AI Specialist (CRAIS) – Ethics, bias mitigation, compliance.
  • Certified Generative AI Specialist (CGAIS) – Prompt engineering, generative AI tools.
03
Stage 3 — Specialized Technical Expertise
Goal: Deep dive into specific domains of AI technology.
  • Certified LLM Specialist (CLLMS) – NLP & Large Language Models.
  • Certified Computer Vision Expert (CCVE) – Imaging, video analytics, AR/VR.
  • Certified AI Cloud Engineer (CAICE) – AI on AWS, Azure, GCP, Databricks.
  • Certified AI Security & Privacy Specialist (CAISPS) – AI risks, privacy, cybersecurity.
04
Stage 4 — Industry-Specific Applications
Goal: Apply AI to real-world sectors.
  • Finance & Banking: Certified AI in Financial Services (CAIFS)
  • Healthcare: Certified AI in Healthcare (CAIH)
  • Retail & Supply Chain: Certified AI in Retail & Supply Chain (CAIRSC)
  • Manufacturing: Certified AI in Manufacturing (CAIM)
  • Public Sector & Policy: Certified AI in Policy & Governance (CAIPG)
05
Stage 5 — Executive & Enterprise Leadership
Goal: Lead AI strategy and enterprise adoption.
  • Certified AI Leader (CAIL) – Strategic, leadership-level AI management.
  • Certified AI Transformation Officer (CAITO) – C-suite strategy & transformation.
  • Certified Enterprise AI Ready (CEAIR) – AI governance framework for organizations.
  • Certified AI Governance & Risk Expert (CAIGR) – Compliance & regulatory strategy.
Certification Pathway Diagram

Stage 1 — Foundation

Goal: Build baseline AI knowledge and confidence.

Certified AI Practitioner (CAIP) – Foundational skills in AI, data, and ML.

Stage 2 — Core AI Competency

Goal: Master model development and deployment.

  • Certified AI Engineer (CAIE) – Core technical certification.
  • Certified Responsible AI Specialist (CRAIS) – Ethics, bias mitigation, compliance.
  • Certified Generative AI Specialist (CGAIS) – Prompt engineering, generative AI tools.

Stage 3 — Specialized Technical Expertise

Goal: Deep dive into specific domains of AI technology.

  • Certified LLM Specialist (CLLMS) – NLP & Large Language Models.
  • Certified Computer Vision Expert (CCVE) – Imaging, video analytics, AR/VR.
  • Certified AI Cloud Engineer (CAICE) – AI on AWS, Azure, GCP, Databricks.
  • Certified AI Security & Privacy Specialist (CAISPS) – AI risks, privacy, cybersecurity.

Stage 4 — Industry-Specific Applications

Goal: Apply AI to real-world sectors.

  • Finance & Banking: Certified AI in Financial Services (CAIFS)
  • Healthcare: Certified AI in Healthcare (CAIH)
  • Retail & Supply Chain: Certified AI in Retail & Supply Chain (CAIRSC)
  • Manufacturing: Certified AI in Manufacturing (CAIM)
  • Public Sector & Policy: Certified AI in Policy & Governance (CAIPG)

Stage 5 — Executive & Enterprise Leadership

Goal: Lead AI strategy and enterprise adoption.

  • Certified AI Leader (CAIL) – Strategic, leadership-level AI management.
  • Certified AI Transformation Officer (CAITO) – C-suite strategy & transformation.
  • Certified Enterprise AI Ready (CEAIR) – AI governance framework for organizations.
  • Certified AI Governance & Risk Expert (CAIGR) – Compliance & regulatory strategy.

Progression Summary

Level Focus Example Certifications
BeginnerFoundationsCAIP
IntermediateCore AI SkillsCAIE, CRAIS, CGAIS
AdvancedSpecialized TechnicalCLLMS, CCVE, CAICE, CAISPS
ExpertIndustry ApplicationsCAIFS, CAIH, CAIRSC, CAIM, CAIPG
LeaderStrategic & EnterpriseCAIL, CAITO, CEAIR, CAIGR