ISTQB CT-AI模擬モード、CT-AI最新な問題集

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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
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
トピック 2
  • Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
トピック 3
  • 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 4
  • Quality Characteristics for AI-Based Systems: This section covers topics covered how to explain the importance of flexibility and adaptability as characteristics of AI-based systems and describes the vitality of managing evolution for AI-based systems. It also covers how to recall the characteristics that make it difficult to use AI-based systems in safety-related applications.
トピック 5
  • systems from those required for conventional systems.
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
  • Neural Networks and Testing: This section of the exam covers defining the structure and function of a neural network including a DNN and the different coverage measures for neural networks.
トピック 8
  • 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 9
  • 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.

>> ISTQB CT-AI模擬モード <<

権威のあるCT-AI模擬モードと更新するCT-AI最新な問題集

CT-AI試験の急流を学び、CT-AI試験を準備するのに20〜30時間しかかかりません。多くの人々、特に現職のスタッフは仕事、学習、家族生活、その他の重要な事柄で忙しく、CT-AI試験を学習して準備する時間とエネルギーがほとんどありません。しかし、CT-AIテストトレントを購入すれば、最も重要なことにメインエネルギーを投資し、試験を学習して準備するために毎日1〜2時間を割くことができます。 CT-AI試験の質問と回答は実際の試験に基づいており、Certified Tester AI Testing Exam受験者の一般的な傾向に準拠しています。

ISTQB Certified Tester AI Testing Exam 認定 CT-AI 試験問題 (Q68-Q73):

質問 # 68
Which statement regarding the use of training, validation, and test data sets is correct?
Choose ONE option (1 out of 4)

正解：A

解説：
The ISTQB CT-AI syllabus (Section3.2 - Model Evaluation) specifies the correct usage oftraining, validation, andtestdatasets. It emphasizes that thetest dataset must be representative of the real operational dataand must beequivalent in distribution to the training and validation sets, ensuring a fair and unbiased evaluation. Option D precisely matches this requirement.
Option A contradicts the syllabus because validation and test sets servedifferent purposes: validation is for tuning, test is for final evaluation. Combining them undermines the reliability of results. Option B is incorrect because even with limited data, the syllabus recommends maintaining a test set or using techniques such as cross-validationrather than eliminating testing. Option C is wrong because equal distribution (33/33/33) isnot recommended; typically, the training set is much larger (e.g., 70-80%).
Thus, OptionDis the only statement aligned with the syllabus' guidance.


質問 # 69
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：A

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 # 70
Arihant Meditation is a startup using Al to aid people in deeper and better meditation based on analysis of various factors such as time and duration of the meditation, pulse and blood pressure, EEG patters etc. among others. Their model accuracy and other functional performance parameters have not yet reached their desired level.
Which ONE of the following factors is NOT a factor affecting the ML functional performance?
SELECT ONE OPTION

正解：D

解説：
* Factors Affecting ML Functional Performance: The data pipeline, quality of the labeling, and biased data are all factors that significantly affect the performance of machine learning models. The number of classes, while relevant for the model structure, is not a direct factor affecting the performance metrics such as accuracy or bias.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Data Quality and its Effect on the ML Model and ML Functional Performance Metrics.


質問 # 71
Pairwise testing can be used in the context of self-driving cars for controlling an explosion in the number of combinations of parameters.
Which ONE of the following options is LEAST likely to be a reason for this incredible growth of parameters?

正解：A

解説：
While evaluating machine learning (ML) model performance is crucial, it does not directly contribute to the explosion of parameter combinations in the same way that road types, weather conditions, and car features do. Metrics are used to measure and assess performance but are not themselves variable conditions that the system must handle.


質問 # 72
You are using a neural network to train a robot vacuum to navigate without bumping into objects. You set up a reward scheme that encourages speed but discourages hitting the bumper sensors. Instead of what you expected, the vacuum has now learned to drive backwards because there are no bumpers on the back.
This is an example of what type of behavior?

正解：D

解説：
The syllabus defines reward hacking as:
"Reward hacking can result from an AI-based system achieving a specified goal by using a 'clever' or 'easy' solution that perverts the spirit of the designer's intent." In this case, the vacuum found a loophole in the reward function-driving backwards to avoid bumper triggers while maximizing reward for speed.
(Reference: ISTQB CT-AI Syllabus v1.0, Section 2.6, page 24 of 99)


質問 # 73
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IT認定試験は現在の社会で、特にIT業界で最も人気のある試験だと考えられています。IT認定試験の認証資格は国際社会で広く認可されています。昇進したく、昇給したく、あるいは単に自分の仕事スキルを向上させたいなら、IT認定試験を受験して資格を取得するのはあなたの最もよい選択です。どうですか。あなたもきっとそう思うでしょう。ですから、躊躇しないではやく試験を申し込みましょう。ISTQBのCT-AI認定試験は最近最も人気のある試験ですから、受験したいのですか。試験に準備する方法がわからない場合、CertShikenは教えてあげます。CertShikenで、あなたは試験に関するすべての優れた参考書を見つけることができます。

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