Question 1

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?

Options :

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 get additional information from project lead to see how many examples per dimension are needed

D : Try to improve the quality of your data through more preparation

Answer: B

Question 2

A project manager meets with a customer for initial discussions about an upcoming project. At the end of the meeting, the customer asks the project manager for a rough estimate of the project duration. Based on her experience with three similar projects, the project manager provides an estimate of 8-10 months. What's wrong with this timeframe?

Options :

A : It's underestimating the project timeline by 3 months

B : It fits into a waterfall timeframe, but not an agile project timeframe

C : It's not accounting for data preparation timelines

D : It's not accounting for potential project delays

Answer: D

Question 3

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

Question 4

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?

Options :

A : Biased data sets leading to algorithmic discrimination

B : Generative AI models hallucinating data results

C : Nothing is wrong, algorithmic decisions will never be 100%

D : Data privacy issues leading to data sharing concerns

Answer: A

Question 5

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

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