CT-AI Practice Questions

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

A.  

If only limited data is available, validation and test data sets can be combined in multiple ways during training.

B.  

If limited data is available, it may be better to work without a separate test data set.

C.  

Optimally, the data should be distributed equally between the training, validation, and test data sets.

D.  

The data in the test data set must be equivalent to the data in the training data sets and to the data in the validation data sets.

A.  

Run the test several times to ensure that the AI always returns the same correct test result

B.  

Decompose the system test into multiple data ingestion tests to determine if the AI system is getting a sufficient volume of input data

C.  

Decompose the system test into multiple data ingestion tests to determine if the AI system is getting precise and accurate input data

D.  

Run the test several times to generate a statistically valid test result to ensure that an appropriate number of answers are accurate

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.  

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.

D.  

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.

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.  

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.  

Adaptability and flexibility are important when the system needs to change its behavior and determine the change on its own.

B.  

Adaptability is considered to be the ability of the system to be used in unspecified situations.

C.  

Self-learning AI-based systems are classified according to whether they are adaptable only or flexible only.

D.  

Flexibility is considered to be the ease with which the system can be reprogrammed to a changed operating condition.

A.  

Individual bias at the neuron level, activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.

B.  

Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.

C.  

Individual bias at the neuron level, and weights assigned to the connections between the neurons.

D.  

Individual bias at the neuron level, and activation values of neurons in the previous layer.

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.  

Evaluating the model

B.  

Deploying the model

C.  

Tuning the model

D.  

Data testing