Question 1

There is a growing backlog of unresolved defects for your project. You know the developers have an ML model that they have created which has learned which developers work on which type of software and the speed with which they resolve issues. How could you use this model to help reduce the backlog and implement more efficient defect resolution?

Options :

A : Use it to prioritize defects automatically based on the time expected for the fix to be made, the speed of the fix, and the likelihood of regressions.

B : Use it to assign defects to the best developer to resolve the problem and to load balance the defect assignments among the developers.

C : Use it to determine the root cause of each defect and develop a process improvement plan that can be implemented to remove the most common root causes.

D : Use it to review the code and determine where more defects are likely to occur so that testing can be targeted to those areas.

Answer: B

Question 2

An ML expert set to test AL based systems at a financial firm finds it hard to go through the piles of data and identify the specific characteristics or lines of comparison.

Which among the following is likely a challenge in dealing with test data being faced by the expert?

Options :

A : Errors in the test due the use of similar processes as data scientists

B : Legal restriction for data

C : Need to change input data over time

D : Big data

Answer: D

Question 3

Which of the following options BEST describes a deep neural net?

Options :

A : It is made up several layers with each layer (except input and output layers) connected to each other layer and errors are propagated backwards through the network

B : It is comprised of a hierarchical structure of neurons with the lowest (deepest) neurons making most of the decisions

C : It is made up of layers of neurons, each of which generates an activation value based on the other neurons in the same layer

D : It is comprised of connected neurons where each neuron has an associated bias and each connection has an associated weight

Answer: D

Question 4

“An ML model whose expected output for a given numeric input is a continuous variable"

Options :

A : Clustering Algorithm

B : Receiver Operating Characteristic (ROC) curve

C : Genetic Algorithm

D : Regression Model

Answer: D

Question 5

A local business has a mail pickup/delivery robot for their office. The robot currently uses a track to move between pickup/drop off locations. When it arrives at a destination, the robot stops to allow a human to remove or deposit mail. The office has decided to upgrade the robot to include AI capabilities that allow the robot to perform its duties without a track, without running into obstacles, and without human intervention. The test team is creating a list of new and previously established test objectives and acceptance criteria to be used in the testing of the robot upgrade. Which of the following test objectives will test an AI quality characteristic for this system?

Options :

A : The robot must evolve to optimize its routing

B : The robot must recharge for no more than six hours a day

C : The robot must record the time of each delivery which is compiled into a report

D : The robot must complete 99.99% of its deliveries each day

Answer: A

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