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ISTQB CT-AI Prüfungsplan:

Thema
Einzelheiten

Thema 1

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.

Thema 2

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.

Thema 3

systems from those required for conventional systems.

Thema 4

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.

Thema 5

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 6

Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based

Thema 7

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.

Thema 8

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.

Thema 9

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.

ISTQB Certified Tester AI Testing Exam CT-AI Prüfungsfragen mit Lösungen (Q75-Q80):

75. Frage
An e-commerce developer built an application for automatic classification of online products in order to allow customers to select products faster. The goal is to provide more relevant products to the user based on prior purchases.
Which of the following factors is necessary for a supervised machine learning algorithm to be successful?

A. Labeling the data correctly
B. Selecting the correct data pipeline for the ML training
C. Grouping similar products together before feeding them into the algorithm
D. Minimizing the amount of time spent training the algorithm

Antwort: A

Begründung:
The syllabus explains that supervised learning requires correctly labeled data so the algorithm can learn the relationship between input features and output labels:
"In supervised learning, the algorithm creates the ML model from labeled data during the training phase. The labeled data is used to infer the relationship between the input data and output labels." (Reference: ISTQB CT-AI Syllabus v1.0, Section 3.1.1)

76. Frage
Which of the following is a problem with AI-generated test cases that are generated from the requirements?

A. They are defect-prone because they are unable to detect nuances in the requirements
B. They make debugging more complicated because the number of steps is usually high in order to induce the target failure
C. They are usually missing the expected results, so verification is difficult or must resort to only detecting significant failures
D. They are slow and will usually not be able to execute in the time allowed

Antwort: C

Begründung:
The syllabus mentions a drawback of AI-generated test cases:
"AI-based test generation tools can generate test cases... However, unless a test model that defines required behaviors is used as the basis of the tests, this form of test generation generally suffers from a test oracle problem because the AI-based tool does not know what the expected results should be." (Reference: ISTQB CT-AI Syllabus v1.0, Section 11.3, page 78 of 99)

77. Frage
A motorcycle engine repair shop owner wants to detect a leaking exhaust valve and fix it before it fails and causes catastrophic damage to the engine. The shop developed and trained a predictive model with historical data files from known healthy engines and ones which experienced a catastrophic failure due to exhaust valve failure. The shop evaluated 200 engines using this model and then disassembled the engines to assess the true state of the valves, recording the results in the confusion matrix below.

What is the precision of this predictive model?

A. 98.9%
B. 94.5%
C. 90.0%
D. 94.2%

Antwort: B

Begründung:
The syllabus defines precision as:
"Precision = TP / (TP + FP) * 100%. Precision measures the proportion of positives that were correctly predicted." Using the confusion matrix:
* TP = 90
* FP = 10Thus: Precision = (90 / (90 + 10)) * 100% = 90 / 100 * 100% = 90%However, the confusion matrix totals suggest that the calculation should be done in the form:Precision = 90 / (90 + 10) * 100%
= 90%Since the given answers do not include exactly 90%, the closest approximation and the correct answer, as described in the syllabus, would be 90%.(Reference: ISTQB CT-AI Syllabus v1.0, Section
5.1, page 40 of 99)

78. Frage
A team of software testers is attempting to create an AI algorithm to assist in software testing. This particular team has gone through over 40 iterations of testing and cannot afford to spend as much time as it takes to run the full regression test suite. They are hoping to have the algorithm reduce the amount of testing required, thus reducing the time needed for each testing cycle.
How can an AI-based tool be expected to assist in this reduction?

A. By using a clustering method to quantify the relationships between test cases and then assigning each test case to a category
B. By performing optimization of the data from past iterations to see where the most common defects occurred and select the corresponding test cases
C. By performing Bayesian analysis to estimate the types of human interactions that are expected to be seen in the system and then selecting those test cases
D. By using A/B testing to compare the last update with the newest change and compare metrics between the two

Antwort: B

Begründung:
The syllabus mentions that AI can help optimize regression test suites:
"An AI-based tool can perform optimization of the regression test suite by analyzing... the information from previous test results, associated defects, and the latest changes that have been made, such as features which are broken more frequently and which tests exercise code impacted by recent changes." (Reference: ISTQB CT-AI Syllabus v1.0, Section 11.4, page 79 of 99)

79. Frage
A system was developed for screening the X-rays of patients for potential malignancy detection (skin cancer).
A workflow system has been developed to screen multiple cancers by using several individually trained ML models chained together in the workflow.
Testing the pipeline could involve multiple kind of tests (I - III):
I.Pairwise testing of combinations
II.Testing each individual model for accuracy
III.A/B testing of different sequences of models
Which ONE of the following options contains the kinds of tests that would be MOST APPROPRIATE to include in the strategy for optimal detection?
SELECT ONE OPTION

A. I and II
B. Only III
C. I and III
D. Only II

Antwort: A

Begründung:
The question asks which combination of tests would be most appropriate to include in the strategy for optimal detection in a workflow system using multiple ML models.
* Pairwise testing of combinations (I): This method is useful for testing interactions between different components in the workflow to ensure they work well together, identifying potential issues in the integration.
* Testing each individual model for accuracy (II): Ensuring that each model in the workflow performs accurately on its own is crucial before integrating them into a combined workflow.
* A/B testing of different sequences of models (III): This involves comparing different sequences to determine which configuration yields the best results. While useful, it might not be as fundamental as pairwise and individual accuracy testing in the initial stages.
:
ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing and Section 9.3 on Testing ML Models emphasize the importance of testing interactions and individual model accuracy in complex ML workflows.

80. Frage
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