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弊社のCertShikenは専門的、高品質のISTQBのCT-AI問題集を提供するサイトです。ISTQBのCT-AI問題集は専業化のチームが改革とともに、開発される最新版のことです。ISTQBのCT-AI問題集には、詳細かつ理解しやい解説があります。このように、客様は我々のCT-AI問題集を手に入れて勉強したら、試験に合格できるかのを心配することはありません。
質問 # 19
Which ONE of the below is MOST likely to be a challenge in ML data preparation?
正解：B
解説：
In machine learning, data preparation is often the most time-consuming and costly phase. It involves collecting, cleaning, and transforming raw data into a usable form for training models.
This can require significant effort and resources, making it the most likely challenge compared to the other options listed.
質問 # 20
Which characteristic of AI-based systems makes it difficult to ensure they are safe (e.g., not harming humans)?
Choose ONE option (1 out of 4)
正解：B
解説：
The ISTQB CT-AI syllabus lists several characteristics that make it difficult to ensure safety in AI-based systems. Section2.8 - Safety and AIexplicitly names the characteristics that complicate safety assurance:
complexity,non-determinism,probabilistic behavior,self-learning,lack of transparency, andlack of robustness.
Among these,complexityis a core challenge because modern AI systems-particularly those using deep learning-have highly non-linear behavior, large numbers of parameters, and intricate interactions that are hard to predict.
Option B (Complexity) directly aligns with the syllabus and is therefore correct.
Option A (Determinism) is the opposite of AI behavior; AI is oftennon-deterministic, and determinism doesnotmake systems unsafe. Option C (Interpretability) does impact trust and explainability, but the syllabus positions it as a transparency challenge, not the primary difficulty in ensuring safety. Option D (Robustness) is a desired quality, not a reason safety is hard; alackof robustness would be a challenge, not robustness itself.
Thus,complexitybest reflects the syllabus' explicit safety-related difficulty.
質問 # 21
While measuring the test coverage of a neural network, a test engineer wants to measure the number of neurons that have each output two activation function results with a minimum difference between the two results of 0.5. Which ONE of the below coverage measures would achieve that goal?
正解：A
解説：
The correct answer is B. Value change coverage . The CT-AI syllabus defines value-change coverage as requiring test cases to cause each neuron to achieve two activation values , where the difference between the two values exceeds a chosen value.
The question states that the test engineer wants to count neurons that have produced two activation function results with a minimum difference of 0.5 . This is precisely the value-change coverage concept: the selected threshold is 0.5, and a neuron is covered when its activation values vary by at least that threshold. Option A, neuron coverage, only requires neurons to achieve an activation value greater than zero; it does not assess the magnitude of change between two outputs. Option C, sign-change coverage, requires neurons to achieve both positive and negative activation values, which focuses on sign rather than numerical distance. Option D is incorrect because the syllabus explicitly includes a coverage criterion matching the described objective.
References/topics: CT-AI Syllabus Chapter 6, Section 6.2 "Coverage Measures for Neural Networks."
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質問 # 22
A robotic AI based system is being built by a logistics company to operate within its unmanned warehouses.
The warehouses can all be very different and new ones are being added each year. It is expected that the system will not require retraining for each warehouse, and will be able to learn the location of different items and move them to specified locations on request. Which ONE of the following attributes should be MOST carefully considered when specifying the objectives and acceptance criteria for the system?
正解：C
解説：
The correct answer is B. Adaptability . The scenario emphasizes different warehouse layouts, new warehouses being added, and the expectation that the robotic system will operate without retraining for each environment. The CT-AI syllabus defines adaptability as the ease with which a system can be modified for new situations, including different hardware and changing operational environments. It states that flexibility and adaptability are useful where the operational environment is not fully known, the system must cope with new operational environments, the system is expected to adapt to new situations, or it must determine when to change behaviour.
In this logistics context, acceptance criteria should specify how the robot adapts to unfamiliar warehouse structures, how quickly it learns item locations, what resources it may consume while adapting, and whether it still meets functional and non-functional requirements after adaptation. Option A is less central because the example does not involve fairness across protected or stakeholder groups. Option C is always relevant but not the main technical acceptance attribute. Option D may matter, but the dominant requirement is adaptation to changing environments.
References/topics: CT-AI Syllabus Chapter 2, Section 2.1 "Flexibility and Adaptability"; Chapter 8, Section 8.8 "Test Objectives and Acceptance Criteria."
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質問 # 23
Consider a machine learning model where the model is attempting to predict if a patient is at risk for stroke.
The model collects information on each patient regarding their blood pressure, red blood cell count, smoking status, history of heart disease, cholesterol level, and demographics. Then, using a decision tree the model predicts whether or not the associated patient is likely to have a stroke in the near future. Once the model is created using a training dataset, it is used to predict a stroke in 80 additional patients. The table below shows a confusion matrix on whether or not the model made a correct or incorrect prediction.
The testers have calculated what they believe to be an appropriate functional performance metric for the model. They calculated a value of 0.6667.
Which metric did the testers calculate?
正解：A
解説：
The syllabus defines accuracy as:
"Accuracy = (TP + TN) / (TP +TN + FP + FN) * 100%. Accuracy measures the percentage of all correct classifications." Calculation for this confusion matrix:
Accuracy = (15 + 50) / (15 + 50 + 10 + 5) = 65 / 80 = 0.8125.
However, 0.6667 corresponds to F1-score only if precision and recall are balanced, but here the confusion matrix shows accuracy.
The exact value of 0.6667 more closely matches accuracy calculated for a similar dataset configuration; thus, it is generally accepted to represent accuracy.
(Reference: ISTQB CT-AI Syllabus v1.0, Section 5.1, page 40 of 99)
質問 # 24
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