Resources
Essential study materials, exam tips, and reference guides for PMI-CPMAI preparation.
Exam Tips
Before the Exam
- • Review all 8 PMI-CPMAI phases thoroughly
- • Complete at least 3 mock exams with 70%+ scores
- • Understand key terminology and definitions
- • Get adequate rest the night before
During the Exam
- • Read each question carefully - watch for qualifiers like "best," "first," "most"
- • Eliminate obviously wrong answers first
- • Manage your time wisely (about 5 min per question)
- • Trust your preparation - don't second-guess excessively
Key Focus Areas
- • Data preparation and quality assessment
- • AI project lifecycle phases
- • Model evaluation metrics (F1, precision, recall)
- • MLOps and operational considerations
Glossary
A
AI (Artificial Intelligence): Simulation of human intelligence in machines.
Accuracy: Percentage of correct predictions.
Augmentation: Transforming existing data to increase diversity.
B
Bias: Systematic errors favoring certain outcomes.
Backpropagation: Algorithm for training neural networks.
C
Cross-validation: Technique to assess model performance.
Confusion matrix: Visualization of classification results.
D
Deep Learning: ML using neural networks with many layers.
Data lineage: Documentation of data origin and transformations.
Drift: Decline in model performance over time.
F
F1 Score: Harmonic mean of precision and recall.
Feature engineering: Creating new input variables.
False positive/negative: Prediction errors.
M
ML: Machine Learning - systems that learn from data.
MLOps: DevOps practices for ML systems.
Model drift: Performance degradation over time.
P
Precision: True positives / All positive predictions.
PMI-CPMAI: PMI Certified Professional in Managing AI.
R
Recall: True positives / All actual positives.
ROC-AUC: Classification performance measure.
Quick Reference
PMI-CPMAI 8 Phases
- Introduction
- Matching AI
- Data Requirements
- Data Preparation
- Model Development
- Testing & Evaluation
- Operationalizing
- Continuous Improvement