ISTQB Certified Tester AI Testing - CT-AI무료 덤프문제 풀어보기

A. Previously failing tests

B. Test environment downtime

C. Source control data

D. Defect reports

A. ML models trained with critical defect tickets can identify defects that cause serious consequences.

B. ML models identify developers who should handle a defect based on ticket content.

C. ML models can identify categories for a reported defect during assignment.

D. ML models can support duplicate defect identification when checking defect criticality.

A. They are usually missing the expected results, so verification is difficult or must resort to only detecting significant failures

B. They are defect-prone because they are unable to detect nuances in the requirements

C. They make debugging more complicated because the number of steps is usually high in order to induce the target failure

D. They are slow and will usually not be able to execute in the time allowed

A. It has been trained to analyze mathematical models and is given a set of landscape pictures to classify

B. It has been trained to recognize human faces at a particular resolution and it is given a human face image captured with a higher resolution

C. It has been trained to analyze customer buying trend data and is given information on supplier cost data

D. It has been trained to recognize cats and is given an image of a dog

A. The model suffers from drift and therefore should be regularly tested to ensure that any occurrences of drift are detected soon enough for the problem to be mitigated

B. The model suffers from corruption and therefore should be reloaded into the computer system being used, preferably with a method of version control to prevent further changes

C. The model suffers from a lack of transparency and therefore should be regularly tested to ensure that any progressive errors are detected soon enough for the problem to be mitigated

D. The model suffers from drift and therefore the performance standard should be eased until a new model with more transparency can be developed

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

B. Check the input test data for potential sample bias.

C. Test the model during model evaluation for data bias.

D. Testing the data pipeline for any sources for algorithmic bias.

A. A self driving car that automatically stops the car if the driver is not responding to voice prompts, in order to ensure they are awake.

B. A chatbot that learns the most effective responses to humans (in order to ensure the humans continue to converse) based on prior experiences.

C. A fraud detection system that alerts operators when it sees a specific fraud risk associated with a transaction, in order to obtain human input to make a final decision.

D. A loan approval system that can continue to process loan applications indefinitely, within a defined amount of aggregate credit, until an operator reduces the amount of aggregate credit available.

A. Predicting that diesel prices will increase by ~10% is a classification problem

B. Recognizing a dog from many different images is a regression problem

C. In classification, objects are always assigned to exactly two classes

D. Deciding whether an object is a bicycle or a motorcycle is a classification problem

A. Lack of standards

B. Lack of transparency

C. Lack of robustness

D. Complexity

A. All high priority defects will be identified using this method

B. The number of parameters to test can be reduced to less than a dozen

C. Pairwise cannot be applied to this problem because there is AI involved and the evolving values may result in unexpected results that cannot be verified

D. While the number of tests needed can be reduced, there may still be a large enough set of tests that automation will be required to execute all of them

A. Reinforcement learning

B. Unsupervised learning

C. Supervised learning (classification)

D. Supervised learning (regression)

A. 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.

B. 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.

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. 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.

A. R-Squared

B. MSE

C. Accuracy

D. Precision

A. Cleaning, transforming, augmenting

B. Identifying, gathering, labelling

C. Building, approving, deploying

D. Feature selecting, feature growing, feature augmenting