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Free PMI CPMAI_v7 Exam Questions (page: 5)

Page 4 of 21
PDF with 100 Questions

Clean, well-labeled datasets used for machine learning are partitioned into three subsets: Training sets, Validation sets, and Test sets. As your team is doing this, what's the best way to split up this data?

  1. Split by patterned subsampling
  2. Split by random subsampling
  3. Use the same data for all sets
  4. Split by alphabetical order

Answer(s): B



You need to hire a data scientist to join your team.
What skill sets should you be looking for when hiring and interviewing this person? (Select all that apply.)

  1. Prompt engineering skills
  2. Understanding of tools and technologies for manipulating, collecting, and preparing large data sets
  3. Critical thinking skills
  4. Understanding of algorithms
  5. Automation skills, especially around creating RPA bots
  6. Strong math skills, especially in calculus and statistics

Answer(s): B,C,D,F



Creating machine learning models can be complicated. Your team wants to use tools called Automated Machine Learning (AutoML) to simplify the process. You know of another team that has used AutoML tools and it's saved the team a lot of time. However, what's the one area you should not have the AutoML tool help with?

  1. Automatic model assessment
  2. Iterative modeling and evaluation
  3. Automatic hyperparameter tuning
  4. Automatic model selection
  5. Automatic algorithm selection

Answer(s): D



One of the key elements of a data-centric methodology is the data requirements phase. During CPMAI Phase II, several unexpected issues have developed and are now threatening the data collection efforts.
What course of action might make the issue worse?

  1. See if you can expand the scope to continue with the project
  2. See if you already have access to enough data to continue with the project
  3. See if you can adjust the scope of this interaction to continue with the project
  4. See if you can purchase the data needed to continue with the project

Answer(s): C



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?

  1. You are underfitting the data
  2. You are overfitting the data
  3. You have selected the wrong algorithm
  4. You have selected the wrong data

Answer(s): B



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