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短時間で一番質高いISTQBのCT-AI練習問題を探すことができますか？もしできなかったら、我々のCT-AI試験資料を試していいですか？我が社のCT-AI問題集は多くの専門家が数年間で努力している成果ですから、短い時間をかかってISTQBのCT-AI試験に参加できて、予想以外の成功を得られます。それで、ISTQBのCT-AIに参加する予定がある人々は速く行動しましょう。
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Certified Tester AI Testing Exam CT-AIは、技術的な精度の最高水準を高め、認定された主題と専門家のみを使用します。最新の正確なCT-AI試験トレントをクライアントに提供し、提供する質問と回答は実際の試験に基づいています。合格率が高く、約98％-100％であることをお約束します。また、CT-AIテストブレインダンプは高いヒット率を高め、試験を刺激してCT-AI試験の準備を整えることができます。あなたの成功は、CT-AI試験問題に縛られています。
質問 # 91
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?
正解：B
解説：
Object recognition issues in test automation often arise whendevelopers frequently change object identifiers in the GUI. AI can enhance the stability of GUI automation by:
* Using multiple criteria for object identification
* AI cantrack UI elements using multiple attributessuch asXPath, label, ID, class, and screen coordinatesrather than relying on a single identifier that may change over time.
* This approach makes the automationless brittle and more adaptive to changes in the UI.
* Why other options are incorrect?
* B (Ignore unrecognizable objects to avoid failures): Ignoring objects instead of identifying them properly wouldlead to incomplete or incorrect test execution.
* C (Dynamically name objects and alter source code): AI-based testing tools donot modify application source code; they work byadjusting the recognition strategy.
* D (Anticipate developer changes and pre-alter automation code): While AI can adapt,it does not predict future changes to the GUI, making this option unrealistic.
Thus,Option A is the best answer, as AI tools enhance object recognitionby dynamically selecting the most stable and persistent identification methods, improving test automation reliability.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 11.6.1 (Using AI to Test Through the Graphical User Interface (GUI))
* ISTQB CT-AI Syllabus v1.0, Section 11.6.2 (Using AI to Test the GUI).
質問 # 92
You have been asked for your opinion on the ML approach to be used for a new age verification system. A training dataset is available with many pictures of faces and the subject's age.
When new users sign-up to a mobile app, they have to take a picture of their face. If the system believes they are under 18 years old it will ask them to provide a picture of their passport to prove their age.
Which ONE of the following approaches do you expect to be MOST likely to succeed?
正解：D
解説：
A supervised learning classification system is most likely to succeed in this scenario because it involves using labeled data (the pictures of faces with corresponding ages) to train a model. The system can then classify whether a person is under or over 18 years old. The approach of re- training based on new data (including whether they later provided ID showing their age) ensures continuous improvement of the model. This method directly aligns with the goal of age verification.
質問 # 93
A car insurance company is using a new AI service to reward defensive driving behavior among its policyholders. The driving behavior is recorded in a rating number (score).
The AI service determines this score from the following input values:
Reference speed v_max in km/h
Average speed v_mean in km/h
Average acceleration a_pos in m/s²
Average braking deceleration a_neg in m/s²
The more defensive the driving behavior is (slow driving, low acceleration, low braking deceleration), the higher is the score.
Three initial test cases (Test 1 to Test 3) are used for testing the AI service. In addition, new test cases A-D are proposed.
Which of the new tests is NOT a follow-up test case for metamorphic testing?
Choose ONE option! (1 out of 4)
正解：D
解説：
According to the ISTQB CT-AI syllabus,metamorphic testingworks by applyingmetamorphic relations (MRs): predictable input transformations that should lead to predictable output changes. From the initial test data, clear relations emerge for defensive driving scoring. The score increases when:
* v_mean decreases,
* a_pos decreases,
* a_neg becomes less negative,and decreases when the opposite occurs.
A valid metamorphic follow-up test must modify inputs in a direction consistent with at least one MR while keeping the expected output direction predictable.
Test Alowers v_mean compared to Test 1, with similar acceleration values. This directly satisfies the MR that lower speed # higher score.
Test Cincreases both acceleration and braking intensity compared to Test 2, making the reduced score range (30-70) consistent with more aggressive driving.
Test Dmodifies acceleration and braking magnitudes in ways consistent with Test 3's defensive-driving scoring boundaries.
Test B, however, changes multiple variables in contradictory directions:
* v_mean increases (worse)
* a_pos increases (worse)
* a_neg becomes less negative (better)
Because these changes conflict,the expected score trend becomes unpredictable, violating the premise of a metamorphic follow-up test.
ThusTest B cannot be considered a metamorphic follow-up, which makesOption Ccorrect.
質問 # 94
Which ONE of the following statements BEST describes how system complexity can cause challenges when testing an AI-based system?
正解：D
解説：
Unexpected changes in system behavior can occur due to the complexity of AI-based systems.
These systems often involve many interacting components, which can lead to unpredictable results or variations in performance, making it difficult to anticipate how the system will behave under certain conditions. This presents a significant challenge in testing, as such behavior can be difficult to reproduce or control.
質問 # 95
A word processing company is developing an automatic text correction tool. A machine learning algorithm was used to develop the auto text correction feature. The testers have discovered that when they start typing
"Isle of Wight" it fills in "Isle of Eight". Several UAT testers have accepted this change without noticing.
What type of bias is this?
正解：D
解説：
The syllabus describes automation bias as:
"A type of bias caused by a person favoring the recommendations of an automated decision-making system over other sources." This is also known as complacency bias, where testers accept automated system outputs without questioning them.
(Reference: ISTQB CT-AI Syllabus v1.0, Glossary, Page 89 of 99)
質問 # 96
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