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ISTQB CT-AI Exam Syllabus Topics:

Topic
Details

Topic 1

Testing AI-Based Systems Overview: In this section, focus is given to how system specifications for AI-based systems can create challenges in testing and explain automation bias and how this affects testing.

Topic 2

Quality Characteristics for AI-Based Systems: This section covers topics covered how to explain the importance of flexibility and adaptability as characteristics of AI-based systems and describes the vitality of managing evolution for AI-based systems. It also covers how to recall the characteristics that make it difficult to use AI-based systems in safety-related applications.

Topic 3

ML: Data: This section of the exam covers explaining the activities and challenges related to data preparation. It also covers how to test datasets create an ML model and recognize how poor data quality can cause problems with the resultant ML model.

Topic 4

Methods and Techniques for the Testing of AI-Based Systems: In this section, the focus is on explaining how the testing of ML systems can help prevent adversarial attacks and data poisoning.

Topic 5

ML Functional Performance Metrics: In this section, the topics covered include how to calculate the ML functional performance metrics from a given set of confusion matrices.

Topic 6

Machine Learning ML: This section includes the classification and regression as part of supervised learning, explaining the factors involved in the selection of ML algorithms, and demonstrating underfitting and overfitting.

Topic 7

Neural Networks and Testing: This section of the exam covers defining the structure and function of a neural network including a DNN and the different coverage measures for neural networks.

Topic 8

systems from those required for conventional systems.

Topic 9

Introduction to AI: This exam section covers topics such as the AI effect and how it influences the definition of AI. It covers how to distinguish between narrow AI, general AI, and super AI; moreover, the topics covered include describing how standards apply to AI-based systems.

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CT-AI Exam Questions, CT-AI study materials. Certified Tester AI Testing Exam

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q114-Q119):

NEW QUESTION # 114
A test engineer is planning testing for a wearable medical device using AI. The medical device will detect possible heart issues in patients and dispatch emergency services automatically. It is not expected that many patients will have heart issues, and this is reflected in the available data.
In this case, it is decided it is more important that emergency services are not sent un- necessarily, than it is to detect actual heart problems.
Which ONE of the following metrics should the test engineer choose to ensure that the emergency services are sent only when needed?

A. R-Squared
B. Precision
C. Accuracy
D. MSE

Answer: B

Explanation:
Precision is the most appropriate metric in this case, as it focuses on the proportion of true positive results (correctly identifying heart issues) out of all the positive predictions made by the model. Since it is more important that emergency services are not sent unnecessarily (false positives), optimizing for precision ensures that the model is more cautious about dispatching emergency services, minimizing false alarms.

NEW QUESTION # 115
Which ONE of the following would be the LEAST effective input to an AI-based test optimization process?

A. Previously failing tests
B. Source control data
C. Defect reports
D. Test environment downtime

Answer: D

Explanation:
Test environment downtime would be the least effective input to an AI-based test optimization process. While downtime may affect the ability to run tests, it does not provide direct insight into the quality of the tests or the effectiveness of the AI model being tested. In contrast, previously failing tests, defect reports, and source control data are much more relevant as they provide information about areas where the system may have issues or require further optimization.

NEW QUESTION # 116
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?

A. Using a classification model to predict the presence of a defect by using code quality metrics as the input data.
B. Identifying the relationship between developers and the modules developed by them.
C. Search of similar code based on natural language processing.
D. Clustering of similar code modules to predict based on similarity.

Answer: A

Explanation:
Option D specifies using a classification model to predict the presence of defects by using code quality metrics as input data. This accurately describes the process of defect prediction using historical bug data and quality metrics.

NEW QUESTION # 117
Which statement about AI-based test case generation is correct?
Choose ONE option (1 out of 4)

A. A different test oracle is usually required for each AI-generated functional test case.
B. Expected results may not be available for AI-generated functional test cases.
C. AI-generated functional test cases typically result in poor requirements coverage.
D. An AI-based system under test must not be used as a functional test oracle.

Answer: B

Explanation:
The ISTQB CT-AI syllabus indicates inSection 5.2 - AI for Testingthat AI-generated test cases may not come withpredefined expected results. This is because test-case generation methods-such as evolutionary algorithms, reinforcement learning, or clustering-based sampling-produceinputs, but the tester must still determine the correct outputs. Therefore, OptionCis correct: expected results may not be available, especially when AI produces novel or previously unseen input combinations.
Option A is incorrect: AI-based test generation can improve coverage by exploring large input spaces. Option B is incorrect because the need for oracles does not depend on whether the test case is AI-generated. Option D is incorrect because the syllabus allows using a model as an oracle inback-to-back testing, provided limitations are understood.
Therefore,Option Cis the correct syllabus-aligned statement.

NEW QUESTION # 118
Which ONE of the following options is the MOST APPROPRIATE stage of the ML workflow to set model and algorithm hyperparameters?

A. Deploying the model
B. Evaluating the model
C. Data testing
D. Tuning the model

Answer: D

Explanation:
Setting model and algorithm hyperparameters is an essential step in the machine learning workflow, primarily occurring during the tuning phase.
Evaluating the model (A): This stage involves assessing the model's performance using metrics and does not typically include the setting of hyperparameters.
Deploying the model (B): Deployment is the stage where the model is put into production and used in real-world applications. Hyperparameters should already be set before this stage.
Tuning the model (C): This is the correct stage where hyperparameters are set. Tuning involves adjusting the hyperparameters to optimize the model's performance.
Data testing (D): Data testing involves ensuring the quality and integrity of the data used for training and testing the model. It does not include setting hyperparameters.
Hence, the most appropriate stage of the ML workflow to set model and algorithm hyperparameters is C. Tuning the model.

NEW QUESTION # 119
......

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