Certified Responsible AI Specialist

Focus on ethics, bias mitigation and compliance in AI.

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Assessment

Practice Exam

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

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Certification Pathway

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.

Educational Background

Bachelor's in CS/Engineering/Math/Stats or related

Alternatively, equivalent professional experience

Professional Experience

1–2 years in AI/ML/Data engineering projects

Familiarity with Python, R, or JavaScript

Technical Prerequisites

ML concepts (supervised/unsupervised)

Cloud (Azure/AWS/GCP)

Version control (Git)

Data Visualization (Power BI/Tableau or equivalent)

Recommended Preparatory Courses

AI-900: AI Fundamentals

Python for Data Engineering

GloAI Self-Paced AI Engineer Course

Exam Structure

The exam assesses your ability to design, train, evaluate, and deploy AI models in real-world environments.

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: 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

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 classification.

Example Question 2 – Scenario Based

Question:

You've deployed a sentiment analysis model to a production API. Users report inconsistent results during peak hours. What's the most likely cause?

A. Data drift in the training set

B. Latency in the monitoring system

C. Insufficient compute on deployment server

D. Model version mismatch

Correct Answer: C

Explanation: Load spikes usually require more compute or autoscaling.

Example Question 3 – Case Study

Question:

A healthcare organization needs explainable results and compliance. Which approach should be prioritized?

A. Deep learning with no interpretation layers

B. Black-box model for higher accuracy

C. Explainable AI (XAI) techniques

D. Collect more data from social media

Correct Answer: C

Explanation: Explainability and compliance are essential in healthcare.

Exam Platform

Modern, secure, and user-friendly interface designed for smooth and fair assessments.

Certified Responsible AI Specialist

Focus on ethics, bias mitigation and compliance in AI.

Enroll Now

Sample Question Panel

Question:

What is the primary goal of supervised learning?

Options:

A. To find patterns in unlabeled data

B. To predict labels for new data

C. To reduce the dimensionality of data

D. To discover hidden structures in data

Review

25 Unanswered

20 Answered

Exam Completed

You will receive your score soon.

Timed Exams

Automatic timer and progress bar help track completion time.

Question Review Panel

Mark questions for review and revisit before submission

Secure Proctoring

Built-in AI proctoring ensures integrity

Real World Scenarios

Interactive case-based questions for practical skill validation

Instant Results

See your provisional score immediately after submission

Have Technical Questions ?

Our support team is available during exam hours

Transcript

Certified Responsible AI Specialist Certificate

Question Bank

Curated set of practice questions available to help you prepare effectively.

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

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-Level	AI Technician, Data Analyst	CAIP
Mid-Level	AI Engineer, ML Engineer, AI Developer	CAIE, CRAIS, CGAIS
Advanced	AI Architect, AI Consultant, Security Specialist	CLLMS, CCVE, CAICE, CAISPS, Industry Certs
Leadership	Head of AI, AI Product Manager, CAIO	CAIL, CAITO, CAIGR, CEAIR

Certification Pathway

GlofAI Learning Path (Certification Progression)

Foundation

Core

Specialized

Industry

Leadership

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.

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
Beginner	Foundations	CAIP
Intermediate	Core AI Skills	CAIE, CRAIS, CGAIS
Advanced	Specialized Technical	CLLMS, CCVE, CAICE, CAISPS
Expert	Industry Applications	CAIFS, CAIH, CAIRSC, CAIM, CAIPG
Leader	Strategic & Enterprise	CAIL, CAITO, CEAIR, CAIGR