ISTQB Certified Tester AI Testing - CT-AI Free Exam Questions

A. Two variants of the system can be used and compared

B. Combinatorial testing cannot yet be applied to AI

C. Each neuron can be treated as a parameter for pairwise tests

D. Inputs to the system and environment factors can be considered parameters for pairwise tests

A. AI-based defect prediction is based on formal principles and requires only a few factors.

B. AI-based defect prediction can detect whether defects exist but not where.

C. AI-based defect prediction is most effective when based on previous similar constellations.

D. AI-based defect prediction is most effective when based on source-code metrics such as branches or McCabe complexity.

A. Testing for concept drift

B. Testing for explainability

C. Adversarial testing

D. Testing for transparency

A. Value-change coverage

B. None of the coverage measures

C. Neuron coverage

D. Sign-change coverage

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. Threshold coverage

B. Value coverage

C. Sign change coverage

D. Neuron coverage

A. Classification for the optimization of regression test cases

B. Probabilistic methods for the prediction of system failures

C. Computational optimization techniques for defect prediction

D. Fuzzy logic for the generation of test cases

A. Using AIB testing to identify data poisoning can better identify outliers than exploratory data analysis.

B. The adversarial examples identified during adversarial testing must not be added to the training data so that they do not poison the model.

C. Adversarial testing consists of using adversarial attacks to identify vulnerabilities so that they can be eliminated.

D. Regression testing can be used to verify data sourcing policies to ensure the source of training data.

A. Complacency/Disregard

B. Ignorance/Cognitive

C. Geographical/Locality

D. Automation/Complacency

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

B. Challenges resulting from low accuracy of the models.

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

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

A. The team tests the time required for the fast and slow vehicles to travel the same route as the medium vehicle. Then, by calculating the speed difference, they then predict how much faster or slower the vehicles will travel. That information is then used to verify that the arrival time of the vehicles meets the expected result.

B. The team decomposes each route into the relevant components that affect the travel time such as traffic density and vehicle power. The team then uses statistical analysis to characterize the influence of each component to calculate the fast and slow vehicle route times.

C. The team uses an AI system to select the most dissimilar routes. With this information, any of the AI routes can be metaphorically transformed into a fast or slow route.

D. The team uses the same AI route planner to create routes that are longer and shorter but follow the same track. Finally, by driving the fast vehicles on the long routes and slow vehicles on the short routes and vice versa, the AI system will have enough information to infer travel times for all vehicles on all routes.