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Title: 值得信賴的ISTQB CT-AI：Certified Tester AI Testing Exam最新考題 -優秀的VCESoft最新CT-AI考古題 [Print This Page]
Author: tonygre944    Time: yesterday 02:49
Title: 值得信賴的ISTQB CT-AI：Certified Tester AI Testing Exam最新考題 -優秀的VCESoft最新CT-AI考古題
BONUS!!! 免費下載VCESoft CT-AI考試題庫的完整版：https://drive.google.com/open?id=1eHmG891G5JiOOgIB_RlyOZleKt8HS_SM
我們的ISTQB CT-AI考古題資料是多功能的，簡單容易操作，亦兼容。通過使用我們上述題庫資料幫助你完成高品質的CT-AI認證，無論你擁有什么設備，我們題庫資料都支持安裝使用。最新的CT-AI考題資料不僅能幫助考生提高IT技能，還能保證你的利益，提供給你最好的服務，VCESoft將成為你一個值得信賴的伙伴。一年之內，你還享有更新你擁有題庫的權利，你就可以得到最新版的ISTQB CT-AI試題。
ISTQB CT-AI 考試大綱：
主題簡介
主題 1
  • 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.
主題 2
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
主題 3
  • 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.
主題 4
  • 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.
主題 5
  • systems from those required for conventional systems.
主題 6
  • 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.
主題 7
  • 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.

>> CT-AI最新考題 <<
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最新的 ISTQB AI Testing CT-AI 免費考試真題 (Q101-Q106):問題 #101
An e-commerce developer built an application for automatic classification of online products in order to allow customers to select products faster. The goal is to provide more relevant products to the user based on prior purchases.
Which of the following factors is necessary for a supervised machine learning algorithm to be successful?
答案：B
解題說明：
The syllabus explains that supervised learning requires correctly labeled data so the algorithm can learn the relationship between input features and output labels:
"In supervised learning, the algorithm creates the ML model from labeled data during the training phase. The labeled data is used to infer the relationship between the input data and output labels." (Reference: ISTQB CT-AI Syllabus v1.0, Section 3.1.1)

問題 #102
You have been developing test automation for an e-commerce system. One of the problems you are seeing is that object recognition in the GUI is having frequent failures. You have determined this is because the developers are changing the identifiers when they make code updates.
How could AI help make the automation more reliable?
答案：B

問題 #103
Which of the following aspects is a challenge when handling test data for an AI-based system?
答案：C
解題說明：
Handlingtest datain AI-based systems presents numerous challenges, particularly in terms ofdata privacy and confidentiality. AI models often require vast amounts of training data, some of which may containpersonal, sensitive, or confidential information. Ensuringcompliance with data protection laws (e.g., GDPR, CCPA)and implementingsecure data-handling practicesis a major challenge in AI testing.
* Data Privacy Regulations
* AI-based systems frequently process personal data, such as images, names, and transaction details, leading toprivacy concerns.
* Compliance with regulations such asGDPR (General Data Protection Regulation)andCCPA (California Consumer Privacy Act)requiresproper anonymization, encryption, or redactionof sensitive data before using it for testing.
* Data Security Challenges
* AI models mayleak confidential informationif proper security measures are not in place.
* Protectingtraining and test data from unauthorized accessis crucial to maintainingtrust and compliance.
* Legal and Ethical Considerations
* Organizations mustobtain legal approvalbefore using certain datasets, especially those containinghealth records, financial data, or personally identifiable information (PII).
* Testers may need toemploy synthetic dataordata maskingtechniques to minimize exposure risks.
* (B) Output data or intermediate data#
* While analyzing output data is important, it does notpose a significant challengecompared to handlingpersonal or confidential test data.
* (C) Video frame speed or aspect ratio#
* These aretechnical challengesin processing AI models but do not fall underdata privacy or ethical considerations.
* (D) Data frameworks or machine learning frameworks#
* Choosing an appropriateML framework (e.g., TensorFlow, PyTorch)is important, but it is nota major challenge related to test data handling.
* Handling personal or confidential data is a critical challenge in AI testing"ersonal or otherwise confidential data may need special techniques for sanitization, encryption, or redaction.Legal approval for use may also be required." Why is Option A Correct?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, asdata privacy and confidentiality are major challenges when handling test data for AI-based systems.

問題 #104
Which statement regarding data preparation in the ML workflow is correct?
Choose ONE option (1 out of 4)
答案：B
解題說明：
The ISTQB CT-AI syllabus describes theML data preparation workflowin Section2.2 - Data Preparation.
Data preparation consists ofdata gathering,cleaning,transformation, andsampling. The syllabus emphasizes that one significant challenge duringdata gatheringis combining data frommultiple heterogeneous sources, which often differ in structure, quality, and format. Ensuring the resulting dataset is accurate, complete, and representative can be complex, making this a critical challenge in the ML workflow.
This aligns directly with OptionC.
Option A is incorrect because erroneous data correction is part ofcleaning, not transformation. Option B contradicts the syllabus: while automation can help,not all steps should be automateddue to the need for expert oversight, especially in detecting subtle data quality issues. Option D is incorrect because sampling continues to involve risk-particularly around representativeness-and the syllabus emphasizes caution, not complacency.
Thus, OptionCis the only statement that accurately reflects the syllabus.

問題 #105
A motorcycle engine repair shop owner wants to detect a leaking exhaust valve and fix it before it fails and causes catastrophic damage to the engine. The shop developed and trained a predictive model with historical data files from known healthy engines and ones which experienced a catastrophic failure due to exhaust valve failure. The shop evaluated 200 engines using this model and then disassembled the engines to assess the true state of the valves, recording the results in the confusion matrix below.

What is the precision of this predictive model?
答案：C
解題說明：
The syllabus defines precision as:
"recision = TP / (TP + FP) * 100%. Precision measures the proportion of positives that were correctly predicted." Using the confusion matrix:
* TP = 90
* FP = 10Thus: Precision = (90 / (90 + 10)) * 100% = 90 / 100 * 100% = 90%However, the confusion matrix totals suggest that the calculation should be done in the formrecision = 90 / (90 + 10) * 100%
= 90%Since the given answers do not include exactly 90%, the closest approximation and the correct answer, as described in the syllabus, would be 90%.(Reference: ISTQB CT-AI Syllabus v1.0, Section
5.1, page 40 of 99)

問題 #106
......
隨著社會的發展，現在ISTQB行業得到了人們的青睞，也有越來越多的人們想考取ISTQB方面的資格認證證書，在事業上更進一步。這個時候你應該想到的是VCESoft網站，它是你CT-AI考試合格的好幫手。VCESoft的強大考古題是CT-AI技術專家們多年來總結出來的經驗和結果，站在這些前人的肩膀上，會讓你離成功更進一步。
最新CT-AI考古題: https://www.vcesoft.com/CT-AI-pdf.html
P.S. VCESoft在Google Drive上分享了免費的、最新的CT-AI考試題庫：https://drive.google.com/open?id=1eHmG891G5JiOOgIB_RlyOZleKt8HS_SM




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