Free ISTQB CT-AI Practice Exams - Page 3 of 5

Question #21

ln the near future, technology will have evolved, and Al will be able to learn multiple tasks by itself without needing to be retrained, allowing it to operate even in new environments. The cognitive abilities of Al are similar to a child of 1-2 years.’

In the above quote, which ONE of the following options is the correct name of this type of Al? SELECT ONE OPTION

A . Technological singularity
B . Narrow Al
C . Super Al
D . General Al

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Question #22

Which ONE of the following characteristics is the least likely to cause safety related issues for an Al system? SELECT ONE OPTION

A . Non-determinism
B . Robustness
C . High complexity
D . Self-learning

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Question #23

Which ONE of the following tests is LEAST likely to be performed during the ML model testing phase? SELECT ONE OPTION

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.

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Question #24

Pairwise testing can be used in the context of self-driving cars for controlling an explosion in the number of combinations of parameters.

Which ONE of the following options is LEAST likely to be a reason for this incredible growth of parameters? SELECT ONE OPTION

A . Different Road Types
B . Different weather conditions
C . ML model metrics to evaluate the functional performance
D . Different features like ADAS, Lane Change Assistance etc.

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Correct Answer: C
C

Explanation:

Pairwise testing is used to handle the large number of combinations of parameters that can arise in complex systems like self-driving cars. The question asks which of the given options is least likely to be a reason for the explosion in the number of parameters.

Different Road Types (A): Self-driving cars must operate on various road types, such as highways, city streets, rural roads, etc. Each road type can have different characteristics, requiring the car’s system to adapt and handle different scenarios. Thus, this is a significant factor contributing to the growth of parameters.

Different Weather Conditions (B): Weather conditions such as rain, snow, fog, and bright sunlight significantly affect the performance of self-driving cars. The car’s sensors and algorithms must adapt to these varying conditions, which adds to the number of parameters that need to be considered.

ML Model Metrics to Evaluate Functional Performance (C): While evaluating machine learning (ML) model performance is crucial, it does not directly contribute to the explosion of parameter combinations in the same way that road types, weather conditions, and car features do. Metrics are used to measure and assess performance but are not themselves variable conditions that the system must handle.

Different Features like ADAS, Lane Change Assistance, etc. (D): Advanced Driver Assistance Systems (ADAS) and other features add complexity to self-driving cars. Each feature can have multiple settings and operational modes, contributing to the overall number of parameters.

Hence, the least likely reason for the incredible growth in the number of parameters is

C. ML model metrics to evaluate the functional performance.

Reference: ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing discusses the application of this technique to manage the combinations of different variables in AI-based systems, including those used in self-driving cars.

Sample Exam Questions document, Question #29 provides context for the explosion in parameter combinations in self-driving cars and highlights the use of pairwise testing as a method to manage this complexity.

Question #25

Which ONE of the following describes a situation of back-to-back testing the LEAST? SELECT ONE OPTION

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.

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Question #26

What is the primary challenge associated with using unsupervised learning for AIbased systems in healthcare diagnostics?

A . The need for large volumes of labeled data
B . The difficulty in interpreting the model outputs
C . The complexity of integration with existing systems
D . The necessity for realtime data processing

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Question #27

Which ONE of the following options BEST DESCRIBES clustering? SELECT ONE OPTION

A . Clustering is classification of a continuous quantity.
B . Clustering is supervised learning.
C . Clustering is done without prior knowledge of output classes.
D . Clustering requires you to know the classes.

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Question #28

For an AI system that adapts its behavior based on historical traffic data, which type of machine learning is being applied?

A . Supervised learning
B . Unsupervised learning
C . Reinforcement learning
D . Semisupervised learning

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Question #29

Which ONE of the following models BEST describes a way to model defect prediction by looking at the history of bugs in modules by using code quality metrics of modules of historical versions as input? SELECT ONE OPTION

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.

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Question #30

Which ONE of the following is the BEST option to optimize the regression test selection and prevent the regression suite from growing large? SELECT ONE OPTION

A . Identifying suitable tests by looking at the complexity of the test cases.
B . Using of a random subset of tests.
C . Automating test scripts using Al-based test automation tools.
D . Using an Al-based tool to optimize the regression test suite by analyzing past test results

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