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PMI CPMAI Exam Syllabus Topics:

PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions:

1. A project team is using a generative AI assistant to draft stakeholder communications. The drafts are often generic and miss project constraints. What is the most likely cause?

A) The team is over-monitoring outputs
B) The model is too efficient
C) The prompts provide insufficient context and constraints
D) The tool requires more compute

2. A fraud detection system must minimize false positives to avoid incorrectly flagging legitimate transactions. The team is selecting evaluation metrics to optimize model performance accordingly. Which metric should be prioritized in this scenario?

A) Precision
B) Accuracy
C) Recall
D) F1-score

3. In an aerospace project focused on predictive maintenance using AI, the project team is facing challenges in coordinating the AI models' operationalization across various manufacturing sites.
Strong governance and corporate guardrails are established, but each site has different computational capabilities and network latencies. What is an effective method that helps to ensure consistent AI performance across these sites?

A) Utilizing cloud-based AI services uniformly
B) Implementing a centralized AI model repository
C) Using site-specific AI model tuning
D) Operationalizing a decentralized AI architecture

4. An inexperienced team is training a neural network model on a desktop computer and this is taking a significant amount of time. What would you recommend to them to speed up model training?

A) Train the model over multiple desktop computers
B) Break the dataset up into multiple smaller datasets and train the model on each of the smaller datasets over a desktop computer
C) Use a contractor to do the training portion
D) Train the model on GPUs

5. A project manager is overseeing the quality assurance and quality control of an AI/machine learning (ML) model. The model has been trained and initial tests have shown promising results.
However, the project manager is concerned about the long-term performance and reliability of the model in real-world scenarios. What should the project manager do?

A) Establish continuous monitoring and feedback loops.
B) Perform a comprehensive hyperparameter tuning.
C) Implement additional data augmentation techniques.
D) Set up cross-validation with a larger dataset.

Solutions: