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

1. When looking to implement AI to help break the Digital Transformation logjam, it's important to:

A) Invest in technology solutions to solve the problem
B) Have the right culture for AI
C) Start with basic automation and introduce AI only as needed
D) Figure out which pattern(s) of AI are needed to incorporate intelligent systems into non-digital processes

2. You are working with a dataset that has a high number of dimensions. You're running into issues because some dimensions don't have enough real examples to properly train the systems for predictable results.
What's your best course of action?

A) Keep going as planned and the problem will eventually correct itself
B) Try to get additional data - at least 5 training examples for each dimension in the representation
C) Try to improve the quality of your data through more preparation
D) Try to get additional information from project lead to see how many examples per dimension are needed

3. Your team has collected petabytes of data for your AI project. As the project lead, you understand this is too much data to use for this iteration of the project.
What is the best course of action to take with this data?

A) Data integration focused on reducing the number of data sources.
B) Data Deduping to reduce overall size and data complexity.
C) Careful algorithm selection that reduces the need for data.
D) Data selection and attribute pruning to reduce overall size and data complexity.

4. Your organization has just rolled out a new image recognition system and is asking all employees to use it. It was trained using images from the ImageNet test set. After a few weeks, users are finding the results are not as expected and are asking for visibility into all the aspects of what went into building an AI system.
What area of Trustworthy AI is being addressed here?

A) AI Systemic Transparency
B) Responsible AI
C) Transparent AI
D) Explainable AI
E) Governed AI

5. You're working with a small inexperienced team on a new ML project. Choosing the best algorithm with the best settings given the training and test data is proving to be very hard for them. You lack the critical data science resources available on your team, and can't wait weeks until a data science resource becomes available to join your team.
What's your best course of action?

A) Put the project on hold until the resources needed become available
B) Outsource the project ASAP
C) Find a citizen data scientist to help
D) Use an AutoML solution

Solutions: