Question 1

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 2

An inexperienced team is training a neural network model on a desktop computer and this is taking a significant amount of time. What would you recommend to them to speed up model training?

Options :

A : Train the model over multiple desktop computers

B : Train the model on GPUs

C : Use a contractor to do the training portion

D :

Break the dataset up into multiple smaller datasets and train the model on each of the smaller datasets over a desktop computer

Answer: B

Question 3

Your team has built a new robot that roams the halls at your organization and helps with various things such as small deliveries. However, you notice that many employees are opting not to use the robot. When you ask them why they tell you that the robot looks "creepy" and they would rather not interact with it. What's going on here?

Options :

A : Lack of understanding the robot's usefulness

B : The bot is falling into The "Uncanny Valley"

C : Bias towards the robot

D : Safety and reliability issues that impact bot usefulness

Answer: B

Question 4

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 5

Your team has built a new robot that roams the halls at your organization and helps with various things such as small deliveries. However, you notice that many employees are opting not to use the robot. When you ask them why they tell you that the robot looks "creepy" and they would rather not interact with it. What's going on here?

Options :

A : Lack of understanding the robot's usefulness

B : The bot is falling into The "Uncanny Valley"

C : Bias towards the robot

D : Safety and reliability issues that impact bot usefulness

Answer: B

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