PMI CPMAI Value Pack (Frequently Bought Together)

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PMI CPMAI Exam Syllabus Topics:

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

1. Your team is using a neural network algorithm to generate a Machine Learning Model. What specific artifacts need to be included? (Choose all that apply.)

A) Bias-variance tradeoff
B) The algorithm code
C) Supporting training data
D) Hyperparameter settings

2. During CPMAI Phase II of your project, your team is going through their data collection needs.
One team member wants to make use of pre-trained models while another member is adamantly against it. As the project lead, what should you do?

A) Evaluate your data and see if using pre-trained models make sense. If so, have the team see what pre-trained models your company already owns and use those
B) Have one team build all models in-house and the other team use pre-trained models and see which team's models perform better.
C) Evaluate your data and see if using pre-trained models make sense. If so, have the team do research to find the ones that best suit your project.
D) Evaluate your data and use only what you have and build all models in house.

3. A project team is preparing to move to the next phase of their AI project. The team needs to ensure that all transparency and explainability requirements are met. Which activity should the project team perform?

A) Document the decision-making process of the AI model.
B) Conduct a thorough data quality assessment.
C) Define the ethical guidelines for the AI project.
D) Establish a feedback mechanism for ongoing evaluation.

4. During the evaluation of an AI solution, the project team notices an unexpected decline in model performance. The model was previously achieving high accuracy but has recently shown increased error rates. Which action will identify the cause of the performance decline?

A) Analyzing the distribution of real world data for potential shifts
B) Checking for issues in the data preprocessing pipeline that may have introduced noise
C) Reviewing recent changes made to the model's architecture and parameters
D) Increasing the amount of regularization to prevent overfitting

5. A machine learning model shows excellent performance on training data but significantly worse results on validation datasets. The team suspects the model memorizes patterns rather than generalizing effectively. Which technique should be applied to address this issue?

A) Increase model complexity
B) Use cross-validation techniques
C) Remove validation data
D) Increase training epochs indefinitely

Solutions: