CT-AI_(v1.0)_World Practice Questions

A.  

Challenges resulting from low accuracy of the models.

B.  

The challenge of mimicking undefined scenarios generated due to self-learning

C.  

The challenge of providing explainability to the decisions made by the system.

D.  

Challenges in the creation of scenarios of human handover for autonomous systems.

A.  

Testing the accuracy of the classification model.

B.  

Testing the API of the service powered by the ML model.

C.  

Testing the speed of the training of the model.

D.  

Testing the speed of the prediction by the model.

A.  

Testing the distribution shift in the training data for inappropriate bias.

B.  

Test the model during model evaluation for data bias.

C.  

Testing the data pipeline for any sources for algorithmic bias.

D.  

Check the input test data for potential sample bias.

A.  

A comparison of two different websites for the same company to observe from a user acceptance perspective.

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 the performance of two different ML implementations on the same input data.

A.  

Identifying the relationship between developers and the modules developed by them.

B.  

Search of similar code based on natural language processing.

C.  

Clustering of similar code modules to predict based on similarity.

D.  

Using a classification model to predict the presence of a defect by using code quality metrics as the input data.

A.  

Using a pixel comparison of the GUI before and after the change to check the differences.

B.  

Using a computer vision to compare the GUI before and after the test object changes.

C.  

Using a vision-based detection of the GUI layout changes before and after test object changes.

D.  

Using a ML-based classifier to flag if changes in GUI are to be flagged for humans.

A.  

Only III

B.  

I and II

C.  

I and III

D.  

Only II

A.  

Compliance needs require proper care to be taken of input personal data.

B.  

Nature of data constantly changes with lime.

C.  

Data for the use case is being generated at a fast pace.

D.  

Test data being sourced from public sources.

A.  

Test for human handover to give rest to the system.

B.  

Test for human handover when it should actually not be relinquishing control.

C.  

Test for human handover requiring mandatory relinquishing control.

D.  

Test for human handover after a given time interval.

A.  

Comparison of the results of a current neural network model ML model implemented in platform A (for example Pytorch) with a similar neural network model ML model implemented in platform B (for example Tensorflow), for the same data.

B.  

Comparison of the results of a home-grown neural network model ML model with results in a neural network model implemented in a standard implementation (for example Pytorch) for same data

C.  

Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.

D.  

Comparison of the results of the current neural network ML model on the current data set with a slightly modified data set.