Question 1

During CPMAI Phase II of your project, your team is going through their data collection needs. One team member wants to make use of pre-trained models while another member is adamantly against it. As the project lead, what should you do?

Options :

A : Evaluate your data and use only what you have and build all models in house.

B : Evaluate your data and see if using pre-trained models make sense. If so, have the team see what pre-trained models your company already owns and use those.

C : Evaluate your data and see if using pre-trained models make sense. If so, have the team do research to find the ones that best suit your project.

D : Have one team build all models in-house and the other team use pre-trained models and see which team's models perform better.

Answer: C

Question 2

During CPMAI Phase II, it's important to not only understand the sources of your data but also what data is required for training as well as identifying the features that are required. When looking to gather data, what approach is best when determining how much data you need?

Options :

A : The "Goldilocks" approach

B : The "less is better" approach

C : The "more is better" approach

D : There is no correct approach

Answer: A

Question 3

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?

Options :

A : Use noise reduction techniques to reduce all forms of data noise

B : Implement a new data privacy policy

C : Store the data in a data warehouse

D : If this data is not needed, use Data anonymization techniques to remove it before feeding to models

Answer: D

Question 4

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?

Options :

A : Data selection and attribute pruning to reduce overall size and data complexity.

B : Data Deduping to reduce overall size and data complexity.

C : Data integration focused on reducing the number of data sources.

D : Careful algorithm selection that reduces the need for data.

Answer: A

Question 5

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?

Options :

A : Unsupervised Learning

B : Reinforcement Learning

C : Supervised Learning

D : All would work equally well

Answer: B

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