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

1. During the initial phase of an AI project, the team is assessing project success criteria. The project manager discovers that the project may be violating some compliance rules.
What problem describes the issue the project team is facing?

A) Failure to identify applicable data regulations early on
B) Absence of a clear AI go/no-go assessment
C) Inadequate separation of cognitive and noncognitive software
D) Lack of clarity on the project's business objective

2. Your organization wants to keep an eye on AI systems for Governance purposes. What are the most crucial things to consider? (Select all that apply.)

A) Algorithm selection
B) Human chain of accountability
C) AI System testing requirements
D) Continuous System monitoring
E) Key Performance Indicators (KPIs)
F) Data source identification
G) Vendor procurement methods
H) ROI determination

3. You're working with petabytes of data and need to make this dataset more manageable. To do this, you want to reduce the number of variables under consideration.
What is the name for this process?

A) Multivariate regression
B) Gradient Descent
C) Dimensionality Reduction
D) Data selection

4. As the project manager, you are leading a brainstorming session with key stakeholders around a new Hyperpersonalization project. What's a key feature for this project that should happen to ensure success?

A) Develop a unique profile of each individual, and manually update that profile over time for a wide variety of purposes
B) Develop a unique profile of each type of individual, and have that profile stay the same over the lifetime of that user
C) Develop a unique profile of each individual, and having that profile both learn and adapt over time as well as be programmed for a wide variety of purposes
D) Develop a unique profile of each individual, and have that profile learn and adapt over time for a wide variety of purposes

5. Your model has been working fine for the last three months, however recently you notice the model's performance has greatly declined. What seems to have been overlooked in your workflow pipeline?

A) Model reevaluation
B) Model retraining
C) Model Drift
D) Model Operationalization

Solutions: