[2026年09月]更新のISTQB CT-AI試験基本問題には解答が付きます [Q37-Q55]

[2026年09月]更新のISTQB CT-AI試験基本問題には解答が付きます

2026年最新の実際に出るISTQB CT-AI試験問題集と解答

質問 # 37
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."

質問 # 38
Which ONE of the following characteristics is the least likely to cause safety related issues for an Al system?
SELECT ONE OPTION

• A. Non-determinism
• B. Self-learning
• C. Robustness
• D. High complexity

正解：C

解説：
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.
:
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.

質問 # 39
An airline has created a 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 drift and therefore should be regularly tested to ensure that any occurrences of drift 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 corruption and therefore should be reloaded into the computer system being used, preferably with a method of version control to prevent further changes.
• D. 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.

正解：A

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."

質問 # 40
Which ONE of the following options does NOT describe a challenge for acquiring test data in ML systems?

• A. Compliance needs require proper care to be taken of input personal data.
• B. Nature of data constantly changes with lime.
• C. Test data being sourced from public sources.
• D. Data for the use case is being generated at a fast pace.

正解：D

解説：
Challenges for Acquiring Test Data in ML Systems: Compliance needs, the changing nature of data over time, and sourcing data from public sources are significant challenges. Data being generated quickly is generally not a challenge; it can actually be beneficial as it provides more data for training and testing.

質問 # 41
Which machine learning approach is most suitable for predicting customer purchase probability?
Choose ONE option (1 out of 4)

• A. Unsupervised learning
• B. Supervised learning (classification)
• C. Supervised learning (regression)
• D. Reinforcement learning

正解：B

解説：
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 .
Option A (regression) predicts continuous numeric values and is less suitable because the task centers oncategorical likelihood, not estimating exact monetary values. Option C (unsupervised learning) is used when labels are not available-here, the company has labeled purchase histories. Option D (reinforcement learning) requires an interactive environment with reward-driven behavior, which is not applicable to this scenario.
Thus,supervised learning (classification)is the most suitable approach according to the syllabus.

質問 # 42
Which ONE of the following options is an example that BEST describes a system with Al-based autonomous functions?

• A. A system that utilizes human beings for all important decisions.
• B. A fully automated manufacturing plant that uses no software.
• C. A system that utilizes a tool like Selenium.
• D. A system that is fully able to respond to its environment.

正解：D

解説：
AI-Based Autonomous Functions: An AI-based autonomous system is one that can respond to its environment without human intervention. The other options either involve human decisions or do not use AI at all.

質問 # 43
Which ONE of the following tests is LEAST likely to be performed during the ML model testing phase?
SELECT ONE OPTION

• A. Testing the accuracy of the classification model.
• B. Testing the speed of the prediction by the model.
• C. Testing the API of the service powered by the ML model.
• D. Testing the speed of the training of the model.

正解：D

解説：
The question asks which test is least likely to be performed during the ML model testing phase. Let's consider each option:
Testing the accuracy of the classification model (A): Accuracy testing is a fundamental part of the ML model testing phase. It ensures that the model correctly classifies the data as intended and meets the required performance metrics.
Testing the API of the service powered by the ML model (B): Testing the API is crucial, especially if the ML model is deployed as part of a service. This ensures that the service integrates well with other systems and that the API performs as expected.
Testing the speed of the training of the model (C): This is least likely to be part of the ML model testing phase. The speed of training is more relevant during the development phase when optimizing and tuning the model. During testing, the focus is more on the model's performance and behavior rather than how quickly it was trained.
Testing the speed of the prediction by the model (D): Testing the speed of prediction is important to ensure that the model meets performance requirements in a production environment, especially for real-time applications.
Reference:
ISTQB CT-AI Syllabus Section 3.2 on ML Workflow and Section 5 on ML Functional Performance Metrics discuss the focus of testing during the model testing phase, which includes accuracy and prediction speed but not the training speed.

質問 # 44
Which of the following technologies for implementing AI is considered to be a reasoning technique?
Choose ONE option (1 out of 4)

• A. Deductive classifiers
• B. Genetic algorithms
• C. Random Forest
• D. Linear regression

正解：A

解説：
TheISTQB Certified Tester AI Testing Syllabus v1.0explicitly categorizes different AI implementation technologies in Section1.4 - AI Technologies. Within this section, AI methods are grouped into categories, one of which is"Reasoning techniques."These reasoning techniques includerule engines, deductive classifiers, case-based reasoning, and procedural reasoning. Because deductive classifiers are directly listed under this set of reasoning approaches, they are recognized as a reasoning-based AI technology.
Reasoning techniques differ from machine learning approaches because they rely onstructured, predefined rules or logicto reach conclusions. Deductive classifiers use logical inference and symbolic reasoning to classify inputs by applying encoded knowledge. This makes them fundamentally different from statistical or data-driven ML algorithms.
The other options-Linear regression,Random Forest, andGenetic algorithms-are listed by the syllabus asmachine learning techniques, not reasoning methods. Linear regression performs numerical prediction, Random Forest is an ensemble decision-tree ML model, and genetic algorithms are optimization-based ML approaches inspired by evolutionary processes. None of these involve symbolic logical deduction.
Thus, based on the authoritative definitions in the syllabus,Deductive classifiers (Option A)is the only technology classified as a reasoning technique.

質問 # 45
A test engineer is planning testing for a wearable medical device using AI. The medical device will detect possible heart issues in patients and dispatch emergency services automatically. It is not expected that many patients will have heart issues, and this is reflected in the available data.
In this case, it is decided it is more important that emergency services are not sent un- necessarily, than it is to detect actual heart problems.
Which ONE of the following metrics should the test engineer choose to ensure that the emergency services are sent only when needed?

• A. MSE
• B. Precision
• C. R-Squared
• D. Accuracy

正解：B

解説：
Precision is the most appropriate metric in this case, as it focuses on the proportion of true positive results (correctly identifying heart issues) out of all the positive predictions made by the model. Since it is more important that emergency services are not sent unnecessarily (false positives), optimizing for precision ensures that the model is more cautious about dispatching emergency services, minimizing false alarms.

質問 # 46
Al-enabled medical devices are used nowadays for automating certain parts of the medical diagnostic processes. Since these are life-critical process the relevant authorities are considenng bringing about suitable certifications for these Al enabled medical devices. This certification may involve several facets of Al testing (I - V).
I . Autonomy
II . Maintainability
III . Safety
IV . Transparency
V . Side Effects
Which ONE of the following options contains the three MOST required aspects to be satisfied for the above scenario of certification of Al enabled medical devices?

• A. Aspects I, IV, and V
• B. Aspects I, II, and III
• C. Aspects III, IV, and V
• D. Aspects II, III and IV

正解：C

解説：
For AI-enabled medical devices, the most required aspects for certification are safety, transparency, and side effects.
Safety (Aspect III): Critical for ensuring that the AI system does not cause harm to patients.
Transparency (Aspect IV): Important for understanding and verifying the decisions made by the AI system.
Side Effects (Aspect V): Necessary to identify and mitigate any unintended consequences of the AI system.

質問 # 47
Which of the following statements about bias in AI-based systems is MOST correct?

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

正解：A

解説：
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.

質問 # 48
Which statement regarding flexibility and adaptability of AI-based systems is correct?

• A. Adaptability and flexibility are important when the system needs to change its behavior and determine the change on its own.
• B. Adaptability is considered to be the ability of the system to be used in unspecified situations.
• C. Flexibility is considered to be the ease with which the system can be reprogrammed to a changed operating condition.
• D. Self-learning AI-based systems are classified according to whether they are adaptable only or flexible only.

正解：A

解説：
The ISTQB CT-AI syllabus defines these two concepts clearly inSection 2.1 - Flexibility and Adaptability. Flexibility is described as the ability of a system to operate in situationsnot explicitly covered in its original requirements, while adaptability refers to how easily the system can be modified to handle new environments or conditions. The syllabus stresses that both flexibility and adaptability are crucial, particularly inself-learning AI systemsthat may need to respond to changes in their environment and adjust their behavior accordingly. It states that systems must be capable of determining when and how to adjust behavior in evolving situations, especially when the operational environment is not fully known at deployment time . This directly aligns with Option A.

質問 # 49
The following confusion matrix represents the functional performance of a classifier.

Which ONE of the following is the correct calculation for the accuracy of the classifier?

• A. (60 + 11) / (60 + 11 + 20 + 9) * 100% = 71%
• B. 60 / (60 + 9) * 100% = 87%
• C. 2 * (60 / (60 + 20) * 60 / (60 + 9)) / (60 / (60 + 20) + 60 / (60 + 9)) * 100% = 80%
• D. 60 / (60 + 20) * 100% = 75%

正解：A

解説：
Accuracy = (True Positives + True Negatives) / (Total Samples)
In this case:
- True Positives (TP) = 60
- True Negatives (TN) = 11
- False Positives (FP) = 20
- False Negatives (FN) = 9
Accuracy = (60 + 11) / (60 + 11 + 20 + 9) * 100% = 71%

質問 # 50
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?

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

正解：B

解説：
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).

質問 # 51
The training of an ML model... What type of bias is LEAST important to look for when testing the model?
Choose ONE option (1 out of 4)

• A. Inappropriate bias
• B. Automation bias
• C. Algorithmic bias
• D. Sample bias

正解：B

解説：
The ISTQB CT-AI syllabus distinguishes between several types of bias relevant in AI testing, including sample bias,algorithmic bias, andinappropriate bias. In Section3.3 - Bias in AI-Based Systems, the syllabus stresses the importance of identifying biases that originate fromtraining data,model development, anddecision logic. Sample bias occurs when the training data does not adequately represent the population; algorithmic bias arises when the model produces systematically skewed results due to learned patterns; inappropriate bias involves ethically or socially problematic distortions in the outcomes. All three of these bias types directly affect theoutputs of the AI modeland are therefore highly relevant when testing an industrial inspection system intended to reliably detect defects. These biases can lead to defective items being missed or false alarms being raised, which impacts quality assurance significantly .
Automation bias, however, is fundamentally different. It refers to ahuman cognitive bias, where users (e.g., inspectors) overly trust or rely on the AI system's output. While important in user-interaction testing, it isnota biaswithin the ML model itself. Since the question asks which bias isleast important when testing the model, automation bias can be legitimately deprioritized duringmodel-level testing. Therefore, OptionBis correct.

質問 # 52
Which ONE of the following tests is MOST likely to describe a useful test to help detect different kinds of biases in ML pipeline?
SELECT ONE OPTION

• A. Check the input test data for potential sample bias.
• B. Test the model during model evaluation for data bias.
• C. Testing the distribution shift in the training data for inappropriate bias.
• D. Testing the data pipeline for any sources for algorithmic bias.

正解：B

解説：
Detecting biases in the ML pipeline involves various tests to ensure fairness and accuracy throughout the ML process.
* Testing the distribution shift in the training data for inappropriate bias (A): This involves checking if there is any shift in the data distribution that could lead to bias in the model. It is an important test but not the most direct method for detecting biases.
* Test the model during model evaluation for data bias (B): This is a critical stage where the model is evaluated to detect any biases in the data it was trained on. It directly addresses potential data biases in the model.
* Testing the data pipeline for any sources for algorithmic bias (C): This test is crucial as it helps identify biases that may originate from the data processing and transformation stages within the pipeline. Detecting sources of algorithmic bias ensures that the model does not inherit biases from these processes.
* Check the input test data for potential sample bias (D): While this is an important step, it focuses more on the input data and less on the overall data pipeline.
Hence, the most likely useful test to help detect different kinds of biases in the ML pipeline isB. Test the model during model evaluation for data bias.
References:
* ISTQB CT-AI Syllabus Section 8.3 on Testing for Algorithmic, Sample, and Inappropriate Bias discusses various tests that can be performed to detect biases at different stages of the ML pipeline.
* Sample Exam Questions document, Question #32 highlights the importance of evaluating the model for biases.

質問 # 53
A word processing company is developing an automatic text correction tool. A machine learning algorithm was used to develop the auto text correction feature. The testers have discovered when they start typing "Isle of Wight" it fills in "Isle of Eight". Several UAT testers have accepted this change without noticing. What type of bias is this?

• A. Automation/Complacency
• B. Complacency/Disregard
• C. Ignorance/Cognitive
• D. Geographical/Locality

正解：A

解説：
The syllabus describes automation bias as:
"A type of bias caused by a person favoring the recommendations of an automated decision- making system over other sources." This is also known as complacency bias, where testers accept automated system outputs without questioning them.

質問 # 54
You are testing an autonomous vehicle which uses AI to determine proper driving actions and responses. You have evaluated the parameters and combinations to be tested and have determined that there are too many to test in the time allowed. It has been suggested that you use pairwise testing to limit the parameters. Given the complexity of the software under test, what is likely the outcome from using pairwise testing?

• A. Pairwise cannot be applied to this problem because there is AI involved and the evolving values may result in unexpected results that cannot be verified.
• B. All high priority defects will be identified using this method.
• C. While the number of tests needed can be reduced, there may still be a large enough set of tests that automation will be required to execute all of them.
• D. The number of parameters to test can be reduced to less than a dozen.

正解：C

解説：
The syllabus states that while pairwise testing is effective at finding defects by reducing the number of test cases needed, the resulting test suite can still be extensive and require automation:
"Even the use of pairwise testing can result in extensive test suites... automation and virtual test environments often become necessary to allow the required tests to be run."

質問 # 55
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合格保証付きのISTQB AI Testing CT-AI試験問題集：https://www.passtest.jp/ISTQB/CT-AI-shiken.html

CT-AI練習テストエンジンで今すぐ使おう162試験問題：https://drive.google.com/open?id=1NrOx0qSphax1_FyMCvLTG6y1_H7seYDF