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ISTQB CT-AI 認定試験の出題範囲：

トピック	出題範囲
トピック 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.
トピック 2	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.
トピック 3	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
トピック 4	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.
トピック 5	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
トピック 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.
トピック 7	systems from those required for conventional systems.
トピック 8	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.
トピック 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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ISTQB Certified Tester AI Testing Exam 認定 CT-AI 試験問題 (Q133-Q138):

質問 # 133
An airline has created an ML model to project fuel requirements for future flights. The model imports weather data such as wind speeds and temperatures, calculates flight routes based on historical routings from air traffic control, and estimates loads from average passenger and baggage weights. The model performed within an acceptable standard for the airline throughout the summer but as winter set in, the load weights became less accurate. After some exploratory data analysis, it became apparent that luggage weights were higher in the winter than in summer.
Which of the following statements BEST describes the problem and how it could have been prevented?

A. The model suffers from a lack of transparency and therefore should be regularly tested to ensure that any progressive errors are detected soon enough for the problem to be mitigated
B. The model suffers from drift and therefore the performance standard should be eased until a new model with more transparency can be developed
C. The model suffers from drift and therefore should be regularly tested to ensure that any occurrences of drift are detected soon enough for the problem to be mitigated
D. The model suffers from corruption and therefore should be reloaded into the computer system being used, preferably with a method of version control to prevent further changes

正解：C

解説：
The syllabus states:
"Concept drift occurs when the operational environment changes without the trained model changing correspondingly. The outputs of the model become less accurate and less useful. Therefore, the operational model should be regularly evaluated against its acceptance criteria." (Reference: ISTQB CT-AI Syllabus v1.0, Section 7.6, Page 54 of 99)

質問 # 134
A ML engineer is trying to determine the correctness of the new open-source implementation *X", of a supervised regression algorithm implementation. R-Square is one of the functional performance metrics used to determine the quality of the model.
Which ONE of the following would be an APPROPRIATE strategy to achieve this goal?

A. Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.
B. Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.
C. Add 10% of the rows randomly and create another model and compare the R-Square scores of both the model.
D. Compare the R-Square score of the model obtained using two different implementations that utilize two different programming languages while using the same algorithm and the same training and testing data.

正解：D

解説：
This approach directly compares the performance of two implementations of the same algorithm.
If both implementations produce similar R-Square scores on the same training and testing data, it suggests that the new implementation "X" is correct.

質問 # 135
Which ONE of the following is a factor associated with the test data that can create challenges specific to testing AI-based systems?

A. Obtaining appropriate data from users
B. Manual creation of test data
C. Obtaining data from public benchmark datasets
D. Obtaining data that has not been pre-processed by the system under test.

正解：A

解説：
Obtaining appropriate data from users is a significant challenge when testing AI-based systems, as user-provided data may be noisy, unstructured, or highly variable. This can introduce complexities in testing the AI model's robustness and generalizability.

質問 # 136
Which of the following statements about bias in AI based systems is MOST correct?

A. Inappropriate bias is caused by overweighting of particular classes in algorithms
B. Inappropriate bias is caused by data used for training not being representative of the real world
C. Inappropriate bias can be caused by aspects of the algorithm or the data
D. Inappropriate bias only affects ML systems that process data about people

正解：C

解説：
The correct answer is D . The CT-AI syllabus explains that ML systems make decisions and predictions using algorithms and collected data , and that both can introduce bias into the results. Algorithmic bias can occur when the learning algorithm is incorrectly configured, for example where it overvalues some data compared with other data. Sample bias can occur when training data is not fully representative of the data space to which ML is applied. The syllabus further states that inappropriate bias is often caused by sample bias, but can also be caused by algorithmic bias.
Option A is too narrow because it describes only one algorithmic mechanism. Option B is also incomplete because it describes sample bias only. Option C is incorrect because the syllabus states that bias can be introduced into many types of AI-based systems, not only ML systems involving personal data. Therefore, the most complete and syllabus-aligned statement is that inappropriate bias can arise from either the algorithm or the data.
References/topics: CT-AI Syllabus Chapter 2, Section 2.4 "Bias."
=========

質問 # 137
Which of the following problems would best be solved using the supervised learning category of regression?

A. Recognizing a knife in carry-on luggage at a security checkpoint in an airport scanner
B. Determining the optimal age for a chicken's egg-laying production using input data of the chicken's age and average daily egg production for one million chickens
C. Predicting shopper purchasing behavior based on the category of shopper and the positioning of promotional displays within a store
D. Determining if an animal is a pig or a cow based on image recognition

正解：B

解説：
The syllabus states:
"Supervised learning... divides problems into two categories: classification and regression. Regression is used when the problem requires the ML model to predict a numeric output, for example predicting the age of a person based on their habits." (Reference: ISTQB CT-AI Syllabus v1.0, Section 3.1.1, Page 26 of 99)

質問 # 138
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