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Title: 真実的なCT-AI模試エンジン &合格スムーズCT-AI対応問題集 |有難いCT-AI日本語版対策ガイド [Print This Page]
Author: ricksha682    Time: yesterday 05:10
Title: 真実的なCT-AI模試エンジン &合格スムーズCT-AI対応問題集 |有難いCT-AI日本語版対策ガイド
2026年JPNTestの最新CT-AI PDFダンプおよびCT-AI試験エンジンの無料共有：https://drive.google.com/open?id=1d6v-nHmLLGlNvpBJ4aoBSs7B3IJyKvVF
IT業界の中でたくさんの野心的な専門家がいって、IT業界の中でより一層頂上まで一歩更に近く立ちたくてISTQBのCT-AI試験に参加して認可を得たくて、ISTQB のCT-AI試験が難度の高いので合格率も比較的低いです。ISTQBのCT-AI試験を申し込むのは賢明な選択で今のは競争の激しいIT業界では、絶えず自分を高めるべきです。しかし多くの選択肢があるので君はきっと悩んでいましょう。
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
  • systems from those required for conventional systems.
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
  • 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 5
  • 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 6
  • 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 7
  • 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 8
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based

>> CT-AI模試エンジン <<
ISTQB CT-AI対応問題集、CT-AI日本語版対策ガイド弊社のJPNTest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。
ISTQB Certified Tester AI Testing Exam 認定 CT-AI 試験問題 (Q44-Q49):質問 # 44
A system is to be developed to detect lung cancer using X-ray images.
Which statement BEST describes the difference between a conventional system and an AI system with supervised machine learning?
Choose ONE option (1 out of 4)
正解：D
解説：
The syllabus explains the fundamental distinction betweenconventional systemsandAI-based systems using supervised machine learningin Section1.3 - AI-Based and Conventional Systems. A conventional system relies on human-programmed logic-such as branches, conditions, and explicit rules-to interpret input data.
The system behaves exactly as specified by its developers.
In contrast,AI systems using supervised learning automatically learn patternsfrom labeled data. The syllabus states that"patterns in data are used by the system to determine how it should react in the future...
The AI determines on its own what patterns or features in the data can be used". This aligns directly with Option C: an AI system identifies relevant diagnostic patterns in X-ray images during training, whereas a conventional system requires human experts to explicitly program those patterns.
Option A is incorrect because AI outputs are typicallylessexplainable, not more. Option B is incorrect because both systems can use thesame X-ray images; ML does not require structurally different images. Option D is oversimplified and not fully accurate; while training data is central to ML, AI systems also include architecture, algorithms, and preprocessing-not just data.
Thus,Option Cis the correct and syllabus-aligned answer.

質問 # 45
Which challenge to testing self-learning systems puts you at risk of a data attack?
Choose ONE option (1 out of 4)
正解：C
解説：
The ISTQB CT-AI syllabus describes thatself-learning systems continuously adjust their behaviorduring operation as new data arrives. Section4.1 - Challenges of Testing AI-Based Systemshighlights that such systems are vulnerable todata attacks, particularly through adversarial inputs, poisoning, or malicious drift.
The risk arises because unexpected changes in the input distribution may alter the learned model in harmful ways. OptionD - Unexpected changescorresponds directly to this syllabus-defined risk.
Option A refers to system specification issues but does not relate to data attacks. Option B discusses environment complexity, which makes testing difficult but is not tied to adversarial threats. Option C (insufficient testing time) affects quality but does not specifically increase vulnerability to malicious data manipulation.
Unexpected changes-including data drift, poisoned samples, or maliciously constructed training data-pose the greatest risk. When a self-learning system adapts to altered data patterns, it may unknowingly learn incorrect associations, causing model degradation or manipulation. Therefore,Option Dcorrectly identifies the challenge that increases exposure to data attacks.

質問 # 46
Which ONE of the following statements correctly describes the importance of flexibility for Al systems?
SELECT ONE OPTION
正解：C
解説：
Flexibility in AI systems is crucial for various reasons, particularly because it allows for easier modification and adaptation of the system as a whole.
* AI systems are inherently flexible (A): This statement is not correct. While some AI systems may be designed to be flexible, they are not inherently flexible by nature. Flexibility depends on the system's design and implementation.
* AI systems require changing operational environments; therefore, flexibility is required (B):
While it's true that AI systems may need to operate in changing environments, this statement does not directly address the importance of flexibility for the modification of the system.
* Flexible AI systems allow for easier modification of the system as a whole (C): This statement correctly describes the importance of flexibility. Being able to modify AI systems easily is critical for their maintenance, adaptation to new requirements, and improvement.
* Self-learning systems are expected to deal with new situations without explicitly having to program for it (D): This statement relates to the adaptability of self-learning systems rather than their overall flexibility for modification.
Hence, the correct answer isC. Flexible AI systems allow for easier modification of the system as a whole.
:
ISTQB CT-AI Syllabus Section 2.1 on Flexibility and Adaptability discusses the importance of flexibility in AI systems and how it enables easier modification and adaptability to new situations.
Sample Exam Questions document, Question #30 highlights the importance of flexibility in AI systems.

質問 # 47
Which of the following technologies for implementing AI is considered to be a reasoning technique?
Choose ONE option (1 out of 4)
正解：C
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 # 48
You have access to the training data that was used to train an AI-based system. You can review this information and use it as a guideline when creating your tests. What type of characteristic is this?
正解：B
解説：
AI-based systems can sometimes behave likeblack boxes, where the internal decision-making process is unclear.Transparencyrefers to theability to inspect and understand the training data, algorithms, and decision- making processof the AI system.
* Transparency ensures that testers and stakeholders can review how an AI system was trained.
* Access totraining datais a key factor in transparency because it allows testers toanalyze biases, completeness, and representativenessof the dataset.
* Transparency is an essential characteristic of explainable AI (XAI).
* Having access to training data means that testers can investigate how data influences AI behavior.
* Regulatory and ethical AI guidelines emphasize transparency.
* Many AI ethics frameworks, such asGDPR and Trustworthy AI guidelines, recommend transparency to ensurefair and explainable AI decision-making.
* (A) Autonomy#
* Autonomy refers to an AI system's ability to make decisions independentlywithout human intervention. However,having access to training data does not relate to autonomy, which is more about self-learning and decision-making without human control.
* (B) Explorability#
* Explorability refers to the ability to test AI systems interactivelyto understand their behavior, but it does not directly relate to accessing training data.
* (D) Accessibility#
* Accessibility refers to the ease with which people can use the system, not the ability to inspect the training data.
* Transparency is the ease with which the training data and algorithm used to generate a model can be understood."Transparency: This is considered to be the ease with which the algorithm and training data used to generate the model can be determined." Why is Option C Correct?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option C is the correct answer, astransparency involves access to training data, allowing testers to understand AI decision-making processes.

質問 # 49
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CT-AI対応問題集: https://www.jpntest.com/shiken/CT-AI-mondaishu
ちなみに、JPNTest CT-AIの一部をクラウドストレージからダウンロードできます：https://drive.google.com/open?id=1d6v-nHmLLGlNvpBJ4aoBSs7B3IJyKvVF
Author: christa403    Time: yesterday 19:25
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