CPMAI Practice Test

CPMAI Practice Test practice questions, page 9 of 12, with answer review and explanations.

Question 41

Leadership wants a new HR system built that will better handle potential candidate matching. The project manager assigned to this project believes that the project is well-suited for AI, however they are unsure which pattern of AI this would be. What should the project manager do?

  1. Move forward without determining which patter of AI this falls under.
  2. Pick an algorithm that seems best suited for the problem and then determine which pattern of AI it is based on the algorithm selected.
  3. Conduct a straw poll with stakeholders to determine which pattern of AI this project falls under so they can best collect the data needed and skill sets for the team.
  4. Determine which pattern of AI this project falls under so they can best collect the data needed and skill sets for the team.

Question 42

Your team is working on a new loan decision model that takes a number of factors and data points into consideration and then automatically approves or denies a loan. After a month in operation someone does a review and notices that the system is denying a large number of loans from a certain demographic when all other factors from people in other regions (such as age, salary, and credit score) are the same. What is most likely happening here?

  1. Data privacy issues leading to data sharing concerns
  2. Biased data sets leading to algorithmic discrimination
  3. Generative AI models hallucinating data results
  4. Nothing is wrong, algorithmic decisions will never be 100%

Question 43

Your team has been asked to summarize and highlight patterns in historical purchasing data, identifying prior performance metrics and patterns. What type of analytics is most appropriate to apply for this need?

  1. Descriptive Analytics
  2. Predictive Analytics
  3. Diagnostic Analytics
  4. Projective Analytics

Question 44

You are working on the data engineering pipeline for the AI project and you want to make sure to address the creation of pipelines to deal with model iteration. What part of the pipeline best deals with this step?

  1. Data Acquisition / Ingest / Capture
  2. Retraining Pipelines
  3. Feature Engineering
  4. ELT Pipeline

Question 45

In order for Supervised Learning approaches to work, they must be fed clean, well-labeled data that the system can use to learn from examples. But how do you get Labeled Data? As a team leader at a small startup, what approach would not be beneficial when trying to gather labeled data?

  1. Contract with Third Party Data Labeling Firms
  2. Hire a Contractor Workforce
  3. Get your Users to Do it
  4. Find a source of already labeled data