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[General] ISTQB CT-AI前提条件: Certified Tester AI Testing Exam - Pass4Test確実に100％パス

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【General】 ISTQB CT-AI前提条件: Certified Tester AI Testing Exam - Pass4Test確実に100％パス

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P.S.Pass4TestがGoogle Driveで共有している無料の2026 ISTQB CT-AIダンプ：https://drive.google.com/open?id=1Ufq3J0hsuIPrWZQk6-8T4919udyJDdGb
社会でより良く生き残るためには、私たちの社会の要件を理解しなければなりません。理論的な知識に加えて、より実践的なスキルが必要です。 CT-AI実践ガイドを使用すると、認定資格を迅速に取得でき、競争力が大幅に向上します。もちろん、あなたの利益はCT-AI証明書だけではありません。 CT-AI学習教材は、あなたの働き方とライフスタイルを変えます。他の人よりも効率的に作業できます。 CT-AIトレーニング資料は、このような大きな役割を果たすことができます。
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
  • AI ベース システムの品質特性: このセクションでは、AI ベース システムの特性としての柔軟性と適応性の重要性を説明する方法と、AI ベース システムの進化を管理することの重要性について説明します。また、安全関連のアプリケーションで AI ベース システムを使用することを困難にする特性を思い出す方法についても説明します。
トピック 2
  • AIベースのシステムのテスト環境：このセクションでは、AIベースのシステムのテスト環境を区別する要因について説明します。
トピック 3
  • AI ベース システムのテストの概要: このセクションでは、AI ベース システムのシステム仕様がテストでどのような課題を生み出す可能性があるかに焦点を当て、自動化のバイアスとそれがテストにどのように影響するかについて説明します。
トピック 4
  • ニューラル ネットワークとテスト: この試験のセクションでは、DNN を含むニューラル ネットワークの構造と機能の定義、およびニューラル ネットワークのさまざまなカバレッジ測定について説明します。
トピック 5
  • 従来のシステムに必要なものとは異なるシステムです。
トピック 6
  • AI 固有の品質特性のテスト: このセクションでは、AI ベースのシステムの自己学習によって生じるテストの課題について説明します。

ISTQB CT-AI認証資格 & CT-AI対応内容CT-AI学習教材があれば、あなたは自分の夢を叶えます。CT-AI学習教材はすごく人気があります。全世界のお客様からいい評価をもらいました。なんといっても、自分はCT-AI学習教材を利用したら、その資料のよさを感じることができます。大切なのは、CT-AI学習教材の合格率が高いので、多くのお客様はCT-AI認定試験資格証明書を取得したということです。
ISTQB Certified Tester AI Testing Exam 認定 CT-AI 試験問題 (Q106-Q111):質問 # 106
A mobile app start-up company is implementing an AI-based chat assistant for e-commerce customers. In the process of planning the testing, the team realizes that the specifications are insufficient.
Which testing approach should be used to test this system?
  • A. State transition testing
  • B. Static analysis
  • C. Equivalence partitioning
  • D. Exploratory testing
正解：D
解説：
Whentesting an AI-based chat assistantfor e-commerce customers, thelack of sufficient specifications makes it difficult to use structured test techniques. TheISTQB CT-AI Syllabusrecommendsexploratory testingin such cases:
* Why Exploratory Testing?
* Exploratory testing is usefulwhen specifications are incomplete or unclear.
* AI-based systems, particularly those usingnatural language processing (NLP),may not behave deterministically, making scripted test cases ineffective.
* Thetester interacts dynamicallywith the system, identifying unexpected behaviorsnot documented in the specification.
* Analysis of Answer Choices:
* A (Exploratory testing)#Correct, as it is the best approach when specifications are incomplete.
* B (Static analysis)# Incorrect, as static analysis checks code without execution, which isnot helpfulfor AI chatbots.
* C (Equivalence partitioning)# Incorrect, asthis technique requires well-defined inputs and outputs, which are missing due toinsufficient specifications.
* D (State transition testing)# Incorrect, as state-based testingrequires knowledge of valid and invalid transitions, which is difficult with a chatbot lacking a clear specification.
Thus,Option A is the correct answer, asexploratory testing is the best approach when dealing with insufficient specifications in AI-based systems.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 7.7 (Selecting a Test Approach for an ML System)
* ISTQB CT-AI Syllabus v1.0, Section 9.6 (Experience-Based Testing of AI-Based Systems).

質問 # 107
A beer company is trying to understand how much recognition its logo has in the market. It plans to do that by monitoring images on various social media platforms using a pre-trained neural network for logo detection.
This particular model has been trained by looking for words, as well as matching colors on social media images. The company logo has a big word across the middle with a bold blue and magenta border.
Which associated risk is most likely to occur when using this pre-trained model?
  • A. There is no risk, as the model has already been trained
  • B. Insufficient function: the model was not trained to check for colors or words
  • C. Inherited bias: the model could have inherited unknown defects
  • D. Improper data preparation
正解：C
解説：
According to the syllabus, pre-trained models often inherit biases and limitations from the data and processes used in their original training, which may not align with the new use case. Specifically, the syllabus states:
"When using a pre-trained model, the training data and process cannot be fully controlled or known by the user of the model. As a result, the model can inherit biases or inaccuracies that were part of its original development and training process." (Reference: ISTQB CT-AI Syllabus v1.0, Section 1.8.3)

質問 # 108
Which supervised-learning classification/regression statement is correct?
Choose ONE option (1 out of 4)
  • A. Recognizing a dog from many different images is a regression problem
  • B. Deciding whether an object is a bicycle or a motorcycle is a classification problem
  • C. Predicting that diesel prices will increase by ~10% is a classification problem
  • D. In classification, objects are always assigned to exactly two classes
正解：B
解説：
The ISTQB CT-AI syllabus explains supervised learning under Section1.6 - Machine Learning Approaches
. It definesclassificationas predictingcategorical labels, whereasregressionpredictscontinuous numerical values. OptionB-deciding whether an object is a bicycle or a motorcycle-fits the definition of classification precisely because the model chooses between discrete categories. The syllabus also uses similar examples to illustrate classification tasks, reinforcing that this is the correct interpretation .
Option A is incorrect because image recognition of a dog is aclassificationtask, not regression. Option C is incorrect because predicting a 10% price rise involves forecasting anumerical value, which is aregression problem. Option D is incorrect because classification can involveany number of classes, not only two.
Multiclass classification is explicitly mentioned in the syllabus.
Therefore, OptionBis the only answer aligned with the syllabus' definitions.

質問 # 109
Which of the following characteristics of AI-based systems make it more difficult to ensure they are safe?
  • A. Robustness
  • B. Sustainability
  • C. Non-determinism
  • D. Simplicity
正解：C
解説：
The syllabus states that non-determinism is one of the key challenges for ensuring safety in AI-based systems:
"The characteristics of AI-based systems that make it more difficult to ensure they are safe... include:
complexity, non-determinism, probabilistic nature, self-learning, lack of transparency, interpretability and explainability, and lack of robustness." (Reference: ISTQB CT-AI Syllabus v1.0, Section 2.8, page 25 of 99)

質問 # 110
Which assignment of AI techniques to testing support is BEST?
Choose ONE option (1 out of 4)
  • A. Computational optimization techniques for defect prediction
  • B. Probabilistic methods for the prediction of system failures
  • C. Classification for the optimization of regression test cases
  • D. Fuzzy logic for the generation of test cases
正解：B
解説：
The ISTQB CT-AI syllabus (Section5.2 - AI for Testing) explains that various AI approaches can support testing activities. Probabilistic methods-one of the three major AI technique groups-are used topredict system failures, especially when dealing with uncertainty, likelihood estimation, and reliability analysis. This aligns precisely with OptionB.
Option A is incorrect because regression test optimization is typically performed usingsearch-based optimization, not classification. Option C is incorrect because fuzzy logic is more suited to reasoning under vagueness, not generating test cases. Option D is incorrect: defect prediction relies on statistical learning or classification models, not computational optimization.
Thus,Option Bis the most syllabus-consistent mapping of AI techniques to testing tasks.

質問 # 111
......
3つのバージョンを含むCT-AI試験問題の登場により、試験受験者の98％以上が証明書を正常に取得できました。それらは、PDFバージョン、ソフトウェアバージョン、およびAPPオンラインバージョンであり、顧客の要件と相互に関連しています。 CT-AI試験資料のすべての内容は、実際の試験に基づいて特別に作成されています。また、CT-AIシミュレーション問題は、高効率かつ高品質で慎重に配置されています。また、CT-AIガイドの準備は、思いやりのあるアフターサービスによって提供されます。
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