Question 1

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 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

You have been tasked at your organization to manage a large language model (LLM) project. Identify what LLMs are useful for. (Select all that apply.)

Options :

A : Process automation

B : Text summarization

C : Machine Translation

D : Classify and categorize content

E : Code generation

F : Improve search quality

Answer: A,B,C,D,E,F

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 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

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