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ISTQB CT-AI Prüfungsplan:

Thema	Einzelheiten
Thema 1	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Thema 2	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.
Thema 3	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.
Thema 4	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.
Thema 5	systems from those required for conventional systems.
Thema 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.

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ISTQB Certified Tester AI Testing Exam CT-AI Prüfungsfragen mit Lösungen (Q84-Q89):

84. Frage
Which supervised-learning classification/regression statement is correct?
Choose ONE option (1 out of 4)

A. Recognizing a dog from many different images is a regression problem
B. Deciding whether an object is a bicycle or a motorcycle is a classification problem
C. In classification, objects are always assigned to exactly two classes
D. Predicting that diesel prices will increase by ~10% is a classification problem

Antwort: B

Begründung:
The ISTQB CT-AI syllabus explains supervised learning under Section1.6 - Machine Learning Approaches.
It definesclassificationas predictingcategorical labels, whereasregressionpredictscontinuous numerical values.
OptionB-deciding whether an object is a bicycle or a motorcycle-fits the definition of classification precisely because the model chooses between discrete categories. The syllabus also uses similar examples to illustrate classification tasks, reinforcing that this is the correct interpretation .
Option A is incorrect because image recognition of a dog is aclassificationtask, not regression. Option C is incorrect because predicting a 10% price rise involves forecasting anumerical value, which is aregressionproblem. Option D is incorrect because classification can involveany number of classes, not only two. Multiclass classification is explicitly mentioned in the syllabus.
Therefore, OptionBis the only answer aligned with the syllabus' definitions.

85. Frage
Which ONE of the following statements BEST describes a testing challenge that specifically applied to a self-learning system?

A. In can be difficult to explain the link between test inputs and outputs
B. When systems change themselves the results of previously passing tests may change
C. External data sources might be required to ensure that the system is unbiased
D. It is necessary to test whether the system will relinquish control to a human at the right time

Antwort: B

Begründung:
A key challenge in testing self-learning systems is that, as the system learns and adapts over time, the results of previously passing tests may change. This is because the system's behavior evolves as it learns from new data, potentially altering how it responds to test inputs. This dynamic nature of self-learning systems makes it challenging to maintain consistent and reliable test results.

86. Frage
A data scientist is performing unsupervised learning on a set of financial records relating to previous loan applications, and trying to predict defaults on future loans. They are reporting poor functional performance because of data issues.
Which ONE of the below is LEAST likely to be a contributory factor?

A. Inconsistent pre-processing of some records
B. Missing records for some accounts
C. Irrelevant data included in the account records
D. Missing data relating to whether loans were previously granted and repaid

Antwort: C

Begründung:
Irrelevant data included in the account records is less likely to contribute to poor functional performance in unsupervised learning, especially compared to missing records, missing key data (such as whether loans were granted or repaid), or inconsistent pre-processing. While irrelevant data can affect the quality of the model, missing or inconsistent data typically has a more direct negative impact on unsupervised learning models.

87. Frage
Which of the following is one of the reasons for data mislabelling?

A. Lack of domain knowledge
B. Interoperability error
C. Small datasets
D. Expert knowledge

Antwort: A

Begründung:
The syllabus lists multiple reasons for mislabelled data, including the lack of domain knowledge:
"Lack of required domain knowledge may lead to incorrect labelling."

88. Frage
A transportation company operates three types of delivery vehicles in its fleet. The vehicles operate at different speeds (slow, medium, and fast). The transportation company is attempting to optimize scheduling and has created an AI-based program to plan routes for its vehicles using records from the medium-speed vehicle traveling to selected destinations. The test team uses this data in metamorphic testing to test the accuracy of the estimated travel times created by the AI route planner with the actual routes and times.
Which of the following describes the next phase of metamorphic testing?

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 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.
C. 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.
D. 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.

Antwort: A

Begründung:
Metamorphic Testing (MT)is a testing technique that verifies AI-based systems by generatingfollow-up test casesbased on existing test cases. These follow-up test cases adhere to aMetamorphic Relation (MR), ensuring that if the system is functioning correctly, changes in input should result in predictable changes in output.
* Metamorphic testing works by transforming source test cases into follow-up test cases
* Here, thesource test caseinvolves testing themedium-speed vehicle'stravel time.
* Thefollow-up test casesare derived byextrapolating travel times for fast and slow vehiclesusing predictable relationships based on speed differences.
* MR states that modifying input should result in a predictable change in output
* Since the speed of the vehicle is a known factor, it is possible to predict the new arrival times and verify whether they follow expected trends.
* This is a direct application of metamorphic testing principles
* Inroute optimization systems, metamorphic testing often applies transformations tospeed, distance, or conditionsto verify expected outcomes.
* (B) Decomposing each route into traffic density and vehicle power#
* While useful for statistical analysis, this approach does not generate follow-up test cases based on a definedmetamorphic relation (MR).
* (C) Selecting dissimilar routes and transforming them into a fast or slow route#
* Thisdoes not follow metamorphic testing principles, which require predictable transformations.
* (D) Running fast vehicles on long routes and slow vehicles on short routes#
* This methoddoes not maintain a controlled MRand introduces too manyuncontrolled variables.
* Metamorphic testing generates follow-up test cases based on a source test case."MT is a technique aimed at generating test cases which are based on a source test case that has passed.One or more follow- up test cases are generated by changing (metamorphizing) the source test case based on a metamorphic relation (MR)."
* MT has been used for testing route optimization AI systems."In the area of AI, MT has been used for testing image recognition, search engines, route optimization and voice recognition, among others." Why Option A is Correct?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, as it aligns with the principles ofmetamorphic testing by modifying input speeds and verifying expected results.

89. Frage
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