A startup company has implemented a new facial recognition system for a banking application for mobile devices. The application is intended to learn at run-time on the device to determine if the user should be granted access. It also sends feedback over the Internet to the application developers. The application deployment resulted in continuous restarts of the mobile devices.
Which of the following is the most likely cause of the failure?

• A. The size of the application is consuming too much of the phone's storage capacity
• B. The training, processing, and diagnostic generation are too computationally intensive for the mobile device hardware to handle
• C. The feedback requires a physical connection and cannot be sent over the Internet
• D. Mobile operating systems cannot process machine learning algorithms

A bank wants to use an algorithm to determine which applicants should be given a loan. The bank hires a data scientist to construct a logistic regression model to predict whether the applicant will repay the loan or not.
The bank has enough data on past customers to randomly split the data into a training dataset and a test
/validation dataset. A logistic regression model is constructed on the training dataset using the following independent variables:
Gender
Marital status
Number of dependents
Education
Income
Loan amount
Loan term
Credit score
The model reveals that those with higher credit scores and larger total incomes are more likely to repay their loans. The data scientist has suggested that there might be bias present in the model based on previous models created for other banks.
Given this information, what is the best test approach to check for potential bias in the model?

• A. Experience-based testing should be used to confirm that the training data set is operationally relevant.
  This can include applying exploratory data analysis (EDA) to check for bias within the training data set.
• B. Back-to-back testing should be used to compare the model created using the training data set to another model created using the test data set. If the two models significantly differ, it will indicate there is bias in the original model.
• C. A/B testing should be used to verify that the test data set does not detect any bias that might have been introduced by the original training data. If the two models significantly differ, it will indicate there is bias in the original model.
• D. Acceptance testing should be used to make sure the algorithm is suitable for the customer. The team can re-work the acceptance criteria such that the algorithm is sure to correctly predict the remaining applicants that have been set aside for the validation dataset ensuring no bias is present.

Which ONE of the following options describes a scenario of A/B testing the LEAST?
SELECT ONE OPTION

• A. A comparison of the performance of two different ML implementations on the same input data.
• B. A comparison of two different offers in a recommendation system to decide on the more effective offer for same users.
• C. A comparison of the performance of an ML system on two different input datasets.
• D. A comparison of two different websites for the same company to observe from a user acceptance perspective.

Which ONE of the following approaches to labelling requires the least time and effort?
SELECT ONE OPTION

• A. Pre-labeled dataset
• B. Internal
• C. Al-Assisted
• D. Outsourced