PMI CPMAI Cognitive Project Management in AI Practice Exam
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Official Name: PMI CPMAI Cognitive Project Management in AI
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Start NowAbout the PMI CPMAI Cognitive Project Management in AI Certification
The PMI CPMAI Cognitive Project Management in AI certification is designed for professionals who manage AI, machine learning, and data-driven initiatives using a structured, iterative methodology that aligns technical execution with business goals and ethical standards. As technology evolves and industry demands grow more complex; this credential validates your ability to apply real-world skills and knowledge using PMI tools and frameworks. Earning the certification positions you as a trusted expert, capable of solving high-impact challenges and contributing to secure, scalable, and efficient systems.
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Test Your Knowledge of PMI CPMAI Cognitive Project Management in AI
Question #1
An AI project team is struggling to define ethical boundaries for model deployment.
Which phase should address ethical considerations in CPMAI?
A) every phase
B) data preparation
C) model training
D) business understanding
Solution
Correct answers: A – Explanation:
Ethical concerns should be addressed throughout the AI project lifecycle.
Question #2
A team is selecting an algorithm for anomaly detection.
Which method is most appropriate for clustering anomalies?
A) K-Means
B) Decision Tree
C) Neural Network
D) Hidden Markov Model
Solution
Correct answers: A – Explanation:
K-Means is a common clustering algorithm. Decision Trees and Neural Networks are classification tools.
Question #3
A project manager is evaluating data readiness for an AI initiative.
Which factor is most critical for supervised learning?
A) labeled data
B) large volume
C) real-time access
D) encrypted storage
Solution
Correct answers: A – Explanation:
Supervised learning requires labeled data. Volume and access are secondary.
Question #4
An organization wants to use AutoML tools.
Which task should NOT be automated by AutoML?
A) algorithm selection
B) hyperparameter tuning
C) model evaluation
D) ethical review
Solution
Correct answers: D – Explanation:
Ethical review requires human judgment. AutoML can handle technical tasks.
Question #5
A team is developing an RPA bot for data entry.
Which type of bot is most appropriate?
A) unattended bot
B) attended bot
C) BPO bot
D) AI chatbot
Solution
Correct answers: A – Explanation:
Unattended bots automate back-office tasks without human intervention.
Question #6
A stakeholder asks when to consider trustworthy AI principles.
When should these be applied?
A) every phase
B) only during deployment
C) only during data collection
D) only during model training
Solution
Correct answers: A – Explanation:
Trustworthy AI should be considered throughout the project lifecycle.
Question #7
A team is short on labeled images for training.
Which technique helps augment the dataset?
A) data transformation
B) data anonymization
C) data sampling
D) business understanding
Solution
Correct answers: A – Explanation:
Data transformation (e.g., rotation, scaling) increases dataset diversity.
Question #8
A manager wants to reuse a model trained on cars for animal recognition.
Which technique supports this?
A) transfer learning
B) reinforcement learning
C) generative modeling
D) pre-trained embeddings
Solution
Correct answers: A – Explanation:
Ethical concerns should be addressed throughout the AI project lifecycle.
Question #9
A company is expanding globally and wants to ensure AI compliance.
What should they do?
A) follow laws in current and future markets
B) focus only on local laws
C) ignore regulations due to small size
D) follow US laws only
Solution
Correct answers: A – Explanation:
Global compliance requires awareness of laws in all target markets.
Question #10
A stakeholder asks how to monitor AI systems post-deployment.
Which practice supports governance?
A) continuous monitoring
B) vendor selection
C) algorithm tuning
D) data encryption
Solution
Correct answers: A – Explanation:
Continuous monitoring ensures AI systems remain compliant and effective.
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