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

1. Your team is working on an image recognition system to help identify plants. They have collected a large amount of data but need to get this data labeled.
Which phase of CPMAI is this done?

A) Phase II
B) Phase I
C) Phase IV
D) Phase VI
E) Phase V
F) Phase III

2. You're testing your model and it is overly sensitive to the fluctuations of data and having trouble generalizing. What type of problem is this?

A) You have selected the wrong algorithm
B) You are underfitting the data
C) You are overfitting the data
D) You have selected the wrong data

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

4. Use cognitive technologies/AI when you can't code the rules or you can't scale easily with people or automation. As a good rule of thumb when deciding if AI is right for the project you should:

A) See if simple rules work. If yes, then pick the right AI solution to solve the problem.
B) Decide if it's probabilistic or deterministic patterns. If it's deterministic then go with the AI project.
C) Decide if it's a statistics pattern. If it's statistical then go with the AI project.
D) Decide if it's probabilistic or deterministic patterns. If it's probabilistic then go with the AI project.

5. Your team is working on an NLP model and has just operationalized the first model. Your team makes updates to the model, overwrites the original model, and puts this new model into operation. However, one of the teams using the model has seen a decrease in performance and is asking to use the original model.
What critical error did your team make?

A) They did not practice model versioning and keep all versions of the model
B) They did not have data governance in place
C) They did not practice model iteration and properly iterate on the model
D) They did not have a model retraining pipeline that took into account models

Solutions: