100% Pass ISTQB - Reliable CT-AI - Exam Certified Tester AI Testing Exam Exercise

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ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
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
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 6
  • 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 7
  • Testing AI-Specific Quality Characteristics: In this section, the topics covered are about the challenges in testing created by the self-learning of AI-based systems.
Topic 8
  • 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.
Topic 9
  • 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 10
  • systems from those required for conventional systems.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q109-Q114):

NEW QUESTION # 109
Which option describes a reasonable application of AIB testing for a self-learning system after it has changed its behavior due to user input?
Choose ONE option (1 out of 4)

Answer: C

Explanation:
According to Section4.6 - AI Behaviour Testing (AIB Testing)of the ISTQB CT-AI syllabus, AIB testing is used to evaluate changes in the functional behavior of self-learning systems. The core principle iscomparing pre-change and post-change model behavior using the same test inputs, so that any difference in outputs can be attributed to the model's learning and not to differences in input data. This directly corresponds to OptionC.
Option A is incorrect because the absence of a test oracle does not justify generating new test cases; AIB relies onreusing identical inputsto detect behavioral drift. Option B is invalid because using different inputs prevents meaningful comparison. Option D is incorrect because comparing with an unrelated non-self- learning system does not allow evaluation of the same model's behavioral evolution.
Thus, OptionCaccurately represents the correct application of AIB testing: assessing model behavior changes by running identical test inputs before and after learning updates.


NEW QUESTION # 110
Which of the following statements about bias in AI-based systems is MOST correct?

Answer: D

Explanation:
Inappropriate bias in AI systems is typically caused by the data used for training not being representative of the real world. This can lead to the model making biased or incorrect predictions when applied to real-world scenarios. While algorithmic factors can also contribute to bias, the primary issue arises from biased or unrepresentative training data.


NEW QUESTION # 111
Which machine learning approach is most suitable for predicting customer purchase probability?

Answer: D

Explanation:
The ISTQB CT-AI syllabus explains in Section1.6 - Machine Learning Approachesthatsupervised learningis appropriate when labeled data exists and the goal is to predict an output based on known historical examples. Predicting a customer'spurchase probabilityis aclassificationtask when the output corresponds to discrete categories such as"likely to purchase"vs."not likely to purchase."The syllabus gives similar examples in describing classification as the process of assigning instances to predefined classes based on learned patterns in labeled data. Because the retail company wants to determine whether a customer will make a purchase based on marketing actions, classification is the most appropriate choice .


NEW QUESTION # 112
Which ONE of the following characteristics is the least likely to cause safety related issues for an Al system?
SELECT ONE OPTION

Answer: B

Explanation:
The question asks which characteristic is least likely to cause safety-related issues for an AI system. Let's evaluate each option:
* Non-determinism (A): Non-deterministic systems can produce different outcomes even with the same inputs, which can lead to unpredictable behavior and potential safety issues.
* Robustness (B): Robustness refers to the ability of the system to handle errors, anomalies, and unexpected inputs gracefully. A robust system is less likely to cause safety issues because it can maintain functionality under varied conditions.
* High complexity (C): High complexity in AI systems can lead to difficulties in understanding, predicting, and managing the system's behavior, which can cause safety-related issues.
* Self-learning (D): Self-learning systems adapt based on new data, which can lead to unexpected changes in behavior. If not properly monitored and controlled, this can result in safety issues.
References:
* ISTQB CT-AI Syllabus Section 2.8 on Safety and AI discusses various factors affecting the safety of AI systems, emphasizing the importance of robustness in maintaining safe operation.


NEW QUESTION # 113
Consider an AI-system in which the complex internal structure has been generated by another software system. Why would the tester choose to do black-box testing on this particular system?

Answer: D

Explanation:
The syllabus explains:
"Where the internal structure of an AI-based system is too complex for humans to understand, the system can only be tested as a black box. Even when the internal structure is visible, this provides no additional useful information to help with testing." This confirms that black-box testing is chosen because the tester does not need to understand the system's internal structure.
(Reference: ISTQB CT-AI Syllabus v1.0, Section 8.5, page 61 of 99)


NEW QUESTION # 114
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