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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-Specific Quality Characteristics: In this section, the topics covered are about the challenges in testing created by the self-learning of AI-based systems.
トピック 2	systems from those required for conventional systems.
トピック 3	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.
トピック 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.
トピック 5	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 6	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.

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ISTQB CT-AI資格専門知識 & CT-AIテスト参考書

CT-AI試験の質問は、当社の製品を使用して試験を準備し、夢の証明書を取得できると信じています。より良い求人を希望する場合は、適切なプロ品質を備えなければならないことを私たちは皆知っています。私たちのCT-AI学習教材はあなたのそばにいて気配りのあるサービスを提供する用意があります、そして私たちのCT-AI学習教材はすべてのお客様に心からお勧めします。想像できる。 CT-AIトレーニングガイドには多くの利点があります。

ISTQB Certified Tester AI Testing Exam 認定 CT-AI 試験問題 (Q144-Q149):

質問 # 144
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. Only II
D. I and III

正解：A

解説：
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.
References:
* 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.

質問 # 145
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?

A. Black-box testing eliminates the need for the tester to understand the internal structure of the AI-system
B. Test automation can be built quickly and easily from the test cases developed during black-box testing
C. The black-box testing method will allow the tester to check the transparency of the algorithm used to create the internal structure
D. The tester wishes to better understand the logic of the software used to create the internal structure

正解：A

解説：
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)

質問 # 146
Which ONE of the following options describes a scenario of A/B testing the LEAST?
SELECT ONE OPTION

A. A comparison of the performance of an ML system on two different input datasets.
B. A comparison of the performance of two different ML implementations on the same input data.
C. A comparison of two different offers in a recommendation system to decide on the more effective offer for same users.
D. A comparison of two different websites for the same company to observe from a user acceptance perspective.

正解：A

解説：
A/B testing, also known as split testing, is a method used to compare two versions of a product or system to determine which one performs better. It is widely used in web development, marketing, and machine learning to optimize user experiences and model performance. Here's why option C is the least descriptive of an A/B testing scenario:
Understanding A/B Testing:
In A/B testing, two versions (A and B) of a system or feature are tested against each other. The objective is to measure which version performs better based on predefined metrics such as user engagement, conversion rates, or other performance indicators.
Application in Machine Learning:
In ML systems, A/B testing might involve comparing two different models, algorithms, or system configurations on the same set of data to observe which yields better results.
Why Option C is the Least Descriptive:
Option C describes comparing the performance of an ML system on two different input datasets. This scenario focuses on the input data variation rather than the comparison of system versions or features, which is the essence of A/B testing. A/B testing typically involves a controlled experiment with two versions being tested under the same conditions, not different datasets.
Clarifying the Other Options:
A). A comparison of two different websites for the same company to observe from a user acceptance perspective: This is a classic example of A/B testing where two versions of a website are compared.
B). A comparison of two different offers in a recommendation system to decide on the more effective offer for the same users: This is another example of A/B testing in a recommendation system.
D). A comparison of the performance of two different ML implementations on the same input data: This fits the A/B testing model where two implementations are compared under the same conditions.
References:
ISTQB CT-AI Syllabus, Section 9.4, A/B Testing, explains the methodology and application of A/B testing in various contexts.
"Understanding A/B Testing" (ISTQB CT-AI Syllabus).

質問 # 147
A software component uses machine learning to recognize the digits from a scan of handwritten numbers. In the scenario above, which type of Machine Learning (ML) is this an example of?
SELECT ONE OPTION

A. Clustering
B. Reinforcement learning
C. Classification
D. Regression

正解：C

解説：
Recognizing digits from a scan of handwritten numbers using machine learning is an example of classification. Here's a breakdown:
Classification: This type of machine learning involves categorizing input data into predefined classes. In this scenario, the input data (handwritten digits) are classified into one of the 10 digit classes (0-9).
Why Not Other Options:
Reinforcement Learning: This involves learning by interacting with an environment to achieve a goal, which does not fit the problem of recognizing digits.
Regression: This is used for predicting continuous values, not discrete categories like digit recognition.
Clustering: This involves grouping similar data points together without predefined classes, which is not the case here.

質問 # 148
Which of the following is a dataset issue that can be resolved using pre-processing?

A. Invalid data
B. Numbers stored as strings
C. Wanted outliers
D. Insufficient data

正解：B

解説：
The syllabus describes that data pre-processing includes cleaning (e.g., fixing or removing invalid data) and transforming data (e.g., changing data types such as numbers stored as strings).
"Transformation: The format of the given data is changed... converting categorical data into numerical data, changing image formats..."

質問 # 149
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