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時間が経つとともに、我々はインタネット時代に生活します。この時代にはIT資格認証を取得するは重要になります。それでは、CT-AI試験に参加しよう人々は弊社CertJukenのCT-AI問題集を選らんで勉強して、一発合格して、ISTQBIT資格証明書を受け取れます。
質問 # 70
Which statement about testing to prevent data poisoning and adversarial attacks is correct?
Choose ONE option (1 out of 4)
正解：D
解説：
The ISTQB CT-AI syllabus explains inSection 4.5 - Testing AI-Specific Risksthat adversarial testing is a structured test activity in which testers applyadversarial attacks-crafted or perturbed inputs-to intentionally expose weaknesses in the ML model. The purpose is to identify vulnerabilities that could be exploited throughdata poisoning,evasion attacks, orinput manipulation. OptionCcorrectly reflects this syllabus definition: adversarial testing is aboutusing attacks to locate weaknesses so they can be removed or mitigated.
Option A is incorrect because regression testing does not verify data sourcing policies; it verifies unchanged functionality after modifications. Option B is incorrect because adversarial examples are oftenaddedto training datasets to improverobustness(a practice called adversarial training), not excluded. Option D is incorrect because AIB testing is not described as superior to exploratory data analysis in outlier detection; both have different purposes, and EDA remains essential for data quality assessment.
Thus,Option Cis consistent with syllabus-defined adversarial testing.
質問 # 71
Which statement about testing levels for AI-based systems is correct?
Choose ONE option (1 out of 4)
正解：A
解説：
Section4.3 - Test Levels for AI Systemsclearly defines ML model testing as the level at which testers evaluate whether an ML model fulfills itsfunctional performance criteria, including accuracy, precision, recall, F1, robustness, stability, and fairness. Therefore, Option C is the correct and syllabus-aligned statement.
Option A is incorrect because input data testing focuses onvalidity and correctness of data entering the model, not interactions with all system components. Option B is incorrect: acceptance testing in the syllabus focuses primarily onbusiness and stakeholder requirements, not specifically explainability. Explainability testing may occur at multiple levels depending on context. Option D is also incorrect because API testing belongs tointegration testing, not system testing, even when AI is consumed as a service.
Thus,Option Cis the only statement that precisely matches syllabus definitions.
質問 # 72
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
正解：B
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.
References:The explanation is based on the definitions of different machine learning types as outlined in the ISTQB CT-AI syllabus, specifically under supervised learning and classification.
質問 # 73
Which statement describes factors related to test data that make testing AI-based systems difficult?
正解：A
解説：
Section2.2 - Data Preparationand4.1 - Challenges in Testing AI-Based Systemsdescribe difficulties in obtaining and managing large, representative datasets. AI-based systems requirerealistic, diverse, and representativedata reflecting real-world variations. The syllabus emphasizes that assembling such datasets is time-consuming, resource-intensive, and often constrained by availability, privacy, or domain complexity. Option B directly corresponds to these documented challenges.
質問 # 74
Which of the following describes the AI effect?
Choose ONE option (1 out of 4)
正解：C
解説：
TheAI Effectis clearly defined in theISTQB Certified Tester AI Testing Syllabus v1.0under Section1.1 - Definition of AI and AI Effect. The document explains that society's understanding of what qualifies as "AI" changes over time. Technologies once considered AI-such as expert systems from the 1970s and 1980s or early chess-playing systems-are no longer viewed as AI today. This phenomenon is explicitly labeled the"AI Effect,"described as"the changing perception of what constitutes AI."The syllabus states that as AI capabilities become routine or widely implemented, they often stop being perceived as true artificial intelligence .
Options B, C, and D do not capture this definition. While AI learning from data (B) is a property of ML, it does not describe the shifting perception of AI. Option C describes a technological achievement, not a perceptual shift. Option D references a historical AI milestone (Deep Blue defeating Kasparov) that the syllabus specifically uses as an example of technology that is no longer considered AI due to the AI Effect.
Therefore, onlyOption Aaccurately reflects the AI Effect as defined by the ISTQB syllabus.
質問 # 75
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