Download Cognitive project management in AI.CPMAI.ExamTopics.2026-01-26.87q.vcex

Vendor: PMI
Exam Code: CPMAI
Exam Name: Cognitive project management in AI
Date: Jan 26, 2026
File Size: 53 KB
Downloads: 1
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Demo Questions

Question 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.)
  1. The algorithm code
  2. Supporting training data
  3. Bias-variance tradeoff
  4. Hyperparameter settings
Correct answer: ABD
Question 2
You’re working on a project and are working with personally identifiable information (PII). What’s the best approach to take when it comes to collecting and using this data?
  1. Use noise reduction techniques to reduce all forms of data noise
  2. Implement a new data privacy policy
  3. Store the data in a data warehouse
  4. If this data is not needed, use Data anonymization techniques to remove it before feeding to models
Correct answer: D
Question 3
In the case that an algorithm you want to use isn’t algorithmically explainable, AI systems should try to do the following:
  1. Provide a means to have contestability of the algorithm selected
  2. Provide a means to interpret AI results so that cause and effect can be represented
  3. Provide a means to have a different team on the project
  4. Provide a means to reverse-engineer the algorithm to inspect its performance
Correct answer: B
Question 4
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?
  1. Dimensionality Reduction
  2. Gradient Descent
  3. Multivariate regression
  4. Data selection
Correct answer: A
Question 5
You have just joined a team and they are working on a new project. The project lead isn’t sure what type of technology should be used on this project - AI or a traditional software development approach. What is the best way to determine if you have the criteria for a good AI / ML Project?
  1. Evaluate whether the solution can be done with automation.
  2. Determine if the project fits within the scope, budget, and timeline set out.
  3. Determine whether the project has a cognitive technology component and meets a short-term need.
  4. Determine the long-term need for the organization and build the project to that long-term goal.
Correct answer: C
Question 6
You want to create a model to figure out if a customer would be likely to repurchase a certain item. The project owner doesn’t want you to create anything too complicated, and you have a limited data set to work with.
Which algorithm is the best choice given these constraints?
  1. Ensemble models
  2. Naive Bayes
  3. Neural Networks
  4. Generative AI
Correct answer: B
Question 7
Your team has created a model that is going to be used for monitoring systems and it needs to provide analysis on a weekly basis. What’s the most appropriate Model Operationalization approach?
  1. Batch prediction
  2. Real-time prediction
  3. Web service / Microservice
  4. Stream learning
Correct answer: A
Question 8
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?
  1. Outsource the project ASAP
  2. Find a citizen data scientist to help
  3. Use an AutoML solution
  4. Put the project on hold until the resources needed become available
Correct answer: C
Question 9
An organization is to undertake a multi-pattern AI project. They want to build a robot that is able to roam the halls as well as converse with employees and answer basic questions.
What is the best approach for handling this project?
  1. Run each pattern as its own project, with their own CPMAI phase iterations, data requirements, and project needs
  2. Run each pattern in isolation, with separate teams
  3. Run it as a hybrid approach and some phases are run separately while other phases are combined together
  4. Run it as one project, combining teams, data requirements, and project needs
Correct answer: C
Question 10
Your team is running a simulation-based optimization exercise to increase routing efficiency. Learning for this exercise is done through “trial and error”
Which type of machine learning approach is being leveraged for this exercise?
  1. Unsupervised Learning
  2. Reinforcement Learning
  3. Supervised Learning
  4. All would work equally well
Correct answer: B
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