BTW, DOWNLOAD part of iPassleader CT-AI dumps from Cloud Storage: https://drive.google.com/open?id=11AZbPxtUSUlJWVGePqZiiUF2KhqZStin
iPassleader has one of the most comprehensive and top-notch ISTQB CT-AI Exam Questions. We eliminated the filler and simplified the Certified Tester AI Testing Exam exam preparation process so you can ace the ISTQB certification exam on your first try. Our ISTQB CT-AI Questions include real-world examples to help you learn the fundamentals of the subject not only for the ISTQB exam but also for your future job.
| Topic | Details |
|---|---|
| Topic 1 |
|
| Topic 2 |
|
| Topic 3 |
|
| Topic 4 |
|
| Topic 5 |
|
| Topic 6 |
|
| Topic 7 |
|
| Topic 8 |
|
| Topic 9 |
|
| Topic 10 |
|
| Topic 11 |
|
If you are craving for getting promotion in your company, you must master some special skills which no one can surpass you. To suit your demands, our company has launched the Certified Tester AI Testing Exam CT-AI exam materials especially for office workers. For on one hand, they are busy with their work, they have to get the ISTQB CT-AI Certification by the little spread time.
NEW QUESTION # 110
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)
Answer: B
Explanation:
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.
NEW QUESTION # 111
There is a growing backlog of unresolved defects for your project. You know the developers have an ML model that they have created which has learned which developers work on which type of software and the speed with which they resolve issues. How could you use this model to help reduce the backlog and implement more efficient defect resolution?
Answer: C
Explanation:
AI and ML models can play a significant role in optimizing defect resolution processes. According to the ISTQB Certified Tester AI Testing (CT-AI) Syllabus, ML models can be used toanalyze defect reports, prioritize critical defects, and assign defects to developersbased on historical defect resolution patterns.
The key AI applications for defect management include:
* Defect Categorization- NLP techniques can analyze defect reports and classify them based on metadata like severity and impact.
* Defect Prioritization- ML models trained on past defects can predict which issues are likely to cause failures, allowing teams toprioritizethe most critical issues.
* Defect Assignment- AI-based models can suggest which developers are best suited for specific defects, optimizing the resolution process based on past performance and specialization.
From the given answer choices:
* Option A (Automatic Prioritization)is useful but does not directlyreduce backlog efficientlyby considering developer expertise and workload balancing.
* Option C (Root Cause Analysis for Process Improvement)is along-term strategybut does not directly address backlog reduction.
* Option D (Defect Prediction for Testing Focus)helps preemptively identify issues but does not resolve the existing backlog.
Thus,Option Bis the best choice as it aligns with AI's capability toassign defects to the most suitable developersbased on historical data, ensuring efficient defect resolution and backlog reduction.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 11.2 (Using AI to Analyze Reported Defects)
* ISTQB CT-AI Syllabus v1.0, Section 11.5 (Using AI for Defect Prediction).
NEW QUESTION # 112
Which of the following characteristics of AI-based systems make it more difficult to ensure they are safe?
Answer: B
Explanation:
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)
NEW QUESTION # 113
You are a test manager planning testing for an invoice financing company. The company buys unpaid invoices from companies and provides them with immediate cash.
The company is replacing their existing conventional system, which takes company accounting records as inputs, with an ML system that classifies each invoice for sales as something that should, or should not be bought. Significant historical production data is available. It is important that invoices are not bought incorrectly.
Which ONE of the following test techniques would be MOST appropriate for you to plan for system testing?
Answer: C
Explanation:
Back-to-back testing is the most appropriate technique in this scenario. It involves comparing the outputs of the new machine learning system with the outputs of the existing conventional system, using the same input data. This approach allows to verify that the ML system performs at least as well as the existing system, particularly in avoiding incorrect purchases of invoices, which is crucial for the company's operations.
NEW QUESTION # 114
Which of the following is a problem with AI-generated test cases that are generated from the requirements?
Answer: B
Explanation:
AI-generated test cases are often created using machine learning (ML) models or heuristic algorithms. While these can be effective in generating large numbers of test cases quickly, they oftensuffer from the "test oracle problem."
* Test Oracle Problem:A test oracle is the mechanism used to determine the expected output of a test case. AI-generated test cases oftenlack expected resultsbecause AI-based tools do not inherently understand what the correct output should be.
* Difficulty in Verification:Without expected results, verifying test cases becomes challenging. Testers mustrely on heuristics, anomaly detection, or significant failures, rather than traditional pass/fail conditions.
* A (Slow Execution Time):AI-generated tests are typically automated and designed for efficiency. They are not inherently slow and often executefasterthan manually written tests.
* B (Defect-Prone Due to Nuance Issues):While AI-generated tests may struggle with some complexities in requirements, they primarilylack expected results, rather than failing due to an inability to detect nuances.
* C (Complicated Debugging Due to Many Steps):AI-generated testsreducedebugging complexity by limiting the number of steps required to reproduce failures.
* ISTQB CT-AI Syllabus (Section 11.3: Using AI for Test Case Generation)
* "AI-generated test cases often lack expected results, making it difficult to verify correctness without a test oracle.".
* "Verification often relies on detecting significant failures rather than having predefined expected results.".
Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Since AI-generated test cases frequentlylack expected results, verification becomes difficult, requiring testers tofocus on major failuresrather than precise pass/fail conditions. Thus, thecorrect answer is D.
NEW QUESTION # 115
......
The study material is available in three easy-to-access formats. The first one is PDF format which is printable and portable. You can access it anywhere with your smart devices like smartphones, tablets, and laptops. In addition, you can even print PDF questions in order to study anywhere and pass Certified Tester AI Testing Exam (CT-AI) certification exam.
CT-AI Exam Pass4sure: https://www.ipassleader.com/ISTQB/CT-AI-practice-exam-dumps.html
P.S. Free 2026 ISTQB CT-AI dumps are available on Google Drive shared by iPassleader: https://drive.google.com/open?id=11AZbPxtUSUlJWVGePqZiiUF2KhqZStin