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| Certification Vendor: | ISTQB |
|---|---|
| Exam Name: | ISTQB Certified Tester - AI Testing |
| Exam Number: | CT-AI |
| Exam Duration: | 60 minutes |
| Real Exam Qty: | 40 |
| Exam Price: | EUR 250 |
| Available Languages: | English |
| Certificate Validity Period: | Lifetime (no expiration) |
| Exam Format: | Multiple Choice |
| Passing Score: | 65% |
| Related Certifications: | ISTQB CTFL ISTQB CTAL-TTA |
| Sample Questions: | ISTQB CT-AI Sample Questions |
| Exam Way: | Online proctored or in-person at authorized testing centers |
| Pre Condition: | ISTQB CTFL (Certified Tester Foundation Level) certification is recommended but not mandatory |
| Official Syllabus URL: | https://www.istqb.org/certifications/artificial-intelligence-testing-certification |
영어가 서툴러 국제승인 인기 IT인증자격증 필수시험 과목인ISTQB인증 CT-AI시험에 도전할 엄두도 낼수 없다구요? 이런 생각은 이글을 보는 순간 버리세요. ISTQB인증 CT-AI시험을 패스하려면Pass4Test가 고객님의 곁을 지켜드립니다. Pass4Test의ISTQB인증 CT-AI덤프는 ISTQB인증 CT-AI시험패스 특효약입니다. 영어가 서툴러고 덤프범위안의 문제만 기억하면 되기에 영어로 인한 문제는 걱정하지 않으셔도 됩니다.
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질문 # 19
Which of the following is THE LEAST appropriate tests to be performed for testing a feature related to autonomy?
SELECT ONE OPTION
정답：A
설명：
* Testing Autonomy: Testing for human handover when it should not be relinquishing control is the least appropriate because it contradicts the very definition of autonomous systems. The other tests are relevant to ensuring smooth operation and transitions between human and AI control.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Testing Autonomous AI-Based Systems and Testing for Human-AI Interaction.
질문 # 20
The following confusion matrix represents the functional performance of a classifier.
Which ONE of the following is the correct calculation for the accuracy of the classifier?
정답：B
설명：
The correct answer is D . In the confusion matrix, the values are: true positive TP = 60 , false positive FP = 20
, false negative FN = 9 , and true negative TN = 11 . Accuracy measures the percentage of all classifications that are correct. The CT-AI syllabus defines the formula as: Accuracy = (TP + TN) / (TP + TN + FP + FN)
× 100% .
Substituting the given values gives: (60 + 11) / (60 + 11 + 20 + 9) × 100% = 71 / 100 × 100% = 71% .
Option A is mathematically invalid and does not apply the confusion matrix formula. Option B calculates recall-like behaviour using TP / (TP + FN) rather than accuracy. Option C calculates the F1-score using precision and recall, not accuracy. Option D is therefore the only option that correctly counts both forms of correct prediction: correctly predicted positives and correctly predicted negatives.
References/topics: CT-AI Syllabus Chapter 5, Section 5.1 "Confusion Matrix."
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질문 # 21
Which ONE of the following options is an example that BEST describes a system with Al-based autonomous functions?
정답：C
설명：
AI-Based Autonomous Functions: An AI-based autonomous system is one that can respond to its environment without human intervention. The other options either involve human decisions or do not use AI at all.
질문 # 22
A ML engineer is trying to determine the correctness of the new open-source implementation *X", of a supervised regression algorithm implementation. R-Square is one of the functional performance metrics used to determine the quality of the model.
Which ONE of the following would be an APPROPRIATE strategy to achieve this goal?
SELECT ONE OPTION
정답：A
설명：
A . Add 10% of the rows randomly and create another model and compare the R-Square scores of both the models.
Adding more data to the training set can affect the R-Square score, but it does not directly verify the correctness of the implementation.
B . Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.
Changing the order of input features should not significantly affect the R-Square score if the implementation is correct, but this approach is more about testing model robustness rather than correctness of the implementation.
C . Compare the R-Square score of the model obtained using two different implementations that utilize two different programming languages while using the same algorithm and the same training and testing data.
This approach directly compares the performance of two implementations of the same algorithm. If both implementations produce similar R-Square scores on the same training and testing data, it suggests that the new implementation "X" is correct.
D . Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.
Dropping data can lead to variations in the R-Square score but does not directly verify the correctness of the implementation.
Therefore, option C is the most appropriate strategy because it directly compares the performance of the new implementation "X" with another implementation using the same algorithm and datasets, which helps in verifying the correctness of the implementation.
질문 # 23
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 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?
정답：A
설명：
Automation bias, also known as complacency bias, occurs when humans over-rely on automated systems and fail to question or validate the system's output. In this scenario, the auto-text correction feature of the word processing tool incorrectly suggests "Isle of Eight" instead of "Isle of Wight." The issue arises because multiple UAT testers accept the incorrect suggestion without noticing it, demonstrating a reliance on the AI- based system rather than their own judgment.
Automation bias is commonly seen in:
* Text correction systems, where users accept incorrect suggestions without verifying them.
* Medical diagnosis AI tools, where doctors may rely too much on AI recommendations.
* Autonomous driving systems, where drivers become overly dependent on automation and fail to react in critical situations.
* Section 7.4 - Testing for Automation Bias in AI-Based Systemsexplains that automation bias occurs when people accept AI-generated outputs without verifying them, often leading to incorrect decisions.
Reference from ISTQB Certified Tester AI Testing Study Guide:
질문 # 24
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