Free practice test

Free PMI-CPMAI (Certified Professional in Managing AI) practice test, with a free personalised score report.

Exam-aligned, 120 questions, 160 minutes, pass mark Proficiency bands (PMI publishes no fixed percentage). Mapped to the full official PMI syllabus. You download the question paper right away, then email your answers to info@learnersink.com and our certified trainers send back your personalised score report with the full answer key and rationales.

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  • Free personalised, topic-wise score report from a certified trainer
  • Topic-wise weightage and syllabus breakdown
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Questions
120
Duration
160 minutes
Pass mark
Proficiency bands (PMI publishes no fixed percentage)
Awarded by
PMI

PMI-CPMAI (Certified Professional in Managing AI) domain weightage

Topic-by-topic breakdown used by PMI on the live exam.

Foundations
8%

Why AI projects struggle, iterative delivery for AI, ethical and effective outcomes, tool agnostic approach, AI project risk profile.

Phase I Business Needs
15%

Aligning AI solutions to business need, feasibility assessment, ROI definition, scope, stakeholder alignment, build versus buy.

Phase II Data Needs
15%

Data source identification, compliance considerations, infrastructure requirements, data quality dimensions, governance roles.

Phase III Data Preparation
15%

Data cleaning, augmentation, compliance controls, labeling, missing data handling and preparation for model training.

Phase IV Development and Delivery
17%

Model selection, training approaches, iteration cadence, documentation and delivery of the AI solution.

Phase V Testing and Evaluation
15%

Evaluation metrics, bias and fairness testing, validation against business need, acceptance criteria.

Phase VI Operationalization
15%

Deployment, monitoring, model drift, retraining triggers, support models and responsible AI operations.

Syllabus covered in the practice test

CPMAI methodology overview

Six phases plus foundations, and why AI projects need a data-centric iterative method.

Business and data needs

Feasibility, ROI, build versus buy, data sourcing, compliance and governance.

Data preparation

Cleaning, labeling, augmentation and handling missing or biased data.

Development, delivery and evaluation

Model selection, iteration, evaluation metrics, bias testing and acceptance.

Operationalization

Deployment, monitoring, drift detection, retraining and responsible AI practice.