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| Certification Vendor: | ISTQB |
|---|---|
| Exam Name: | ISTQB Certified Tester - AI Testing Exam |
| Exam Number: | CT-AI |
| Exam Format: | Multiple-choice questions, 1-2 points per question |
| Related Certifications: | ISTQB Certified Tester Testing with Generative AI (CT-GenAI) ISTQB Certified Tester Foundation Level (CTFL) |
| Available Languages: | English, Korean, Spanish, Portuguese, German, Japanese, Chinese, French |
| Real Exam Qty: | 40 |
| Certificate Validity Period: | Valid indefinitely (no expiration) |
| Exam Duration: | 60 (75 for non-native language) |
| Exam Price: | €180 - €250 (varies by region and provider) |
| Passing Score: | 65% (29/44 points for v2.0; 31/47 points for v1.0) |
| Recommended Training: | ISTQB Accredited Training Providers CT-AI Syllabus v2.0 |
| Exam Registration: | Pearson VUE iSQI Exam Registration ISTQB Official Registration |
| Sample Questions: | ISTQB CT-AI Sample Questions |
| Exam Way: | Online remote proctored / Onsite test center |
| Pre Condition: | Must hold ISTQB Certified Tester Foundation Level (CTFL) certification |
| Official Syllabus URL: | https://istqb.org/certifications/certified-tester-ai-testing-ct-ai/ |
Pass4Testの経験豊富な専門家チームはISTQBのCT-AI認定試験に向かって専門性の問題集を作って、とても受験生に合っています。Pass4Testの商品はIT業界中で高品質で低価格で君の試験のために専門に研究したものでございます。
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質問 # 97
Which ONE of the following options describes the LEAST LIKELY usage of Al for detection of GUI changes due to changes in test objects?
SELECT ONE OPTION
正解：C
解説：
* A. Using a pixel comparison of the GUI before and after the change to check the differences.
Pixel comparison is a traditional method and does not involve AI . It compares images at the pixel level, which can be effective but is not an intelligent approach. It is not considered an AI usage and is the least likely usage of AI for detecting GUI changes.
* B. Using computer vision to compare the GUI before and after the test object changes.
Computer vision involves using AI techniques to interpret and process images. It is a likely usage of AI for detecting changes in the GUI .
* C. Using vision-based detection of the GUI layout changes before and after test object changes.
Vision-based detection is another AI technique where the layout and structure of the GUI are analyzed to detect changes. This is a typical application of AI .
* D. Using a ML-based classifier to flag if changes in GUI are to be flagged for humans.
An ML-based classifier can intelligently determine significant changes and decide if they need human review, which is a sophisticated AI application.
質問 # 98
How can a tester check the system for bias as part of a review of data sources, acquisition, and preprocessing?
Choose ONE option (1 out of 4)
正解：A
解説：
Bias detection at thedata levelis performed by reviewingdata acquisition and preprocessing steps, as explained in Section2.3 - Data Quality and Biasof the ISTQB CT-AI syllabus. Sample bias arises when data is distorted or when preprocessing introduces unintended shifts-for example, by filtering, normalization, or labeling steps that disproportionately affect subsets of the data. OptionBcorrectly reflects this: reviewers can identify whether preprocessing steps have altered the dataset in a way that introducessample distortions. This aligns perfectly with syllabus guidance on reviewing data pipelines for bias sources.
Option A is incorrect because algorithmic bias originates from themodel, not data collection procedures.
Option C is incorrect because LIME is anexplainabilitymethod applied post-model, not in data reviews.
Option D incorrectly states "algorithmic bias," but preprocessing affectssample bias, not algorithmic bias.
Thus, OptionBcorrectly matches the syllabus' definition of how bias can be detected during data-related reviews.
質問 # 99
"BioSearch" is creating an Al model used for predicting cancer occurrence via examining X-Ray images. The accuracy of the model in isolation has been found to be good. However, the users of the model started complaining of the poor quality of results, especially inability to detect real cancer cases, when put to practice in the diagnosis lab, leading to stopping of the usage of the model.
A testing expert was called in to find the deficiencies in the test planning which led to the above scenario.
Which ONE of the following options would you expect to MOST likely be the reason to be discovered by the test expert?
SELECT ONE OPTION
正解：C
解説：
The question asks which deficiency is most likely to be discovered by the test expert given the scenario of poor real-world performance despite good isolated accuracy.
* A lack of similarity between the training and testing data (A): This is a common issue in ML where the model performs well on training data but poorly on real-world data due to a lack of representativeness in the training data. This leads to poor generalization to new, unseen data.
* The input data has not been tested for quality prior to use for testing (B): While data quality is important, this option is less likely to be the primary reason for the described issue compared to the representativeness of training data.
* A lack of focus on choosing the right functional-performance metrics (C): Proper metrics are crucial, but the issue described seems more related to the data mismatch rather than metric selection.
* A lack of focus on non-functional requirements testing (D): Non-functional requirements are important, but the scenario specifically mentions issues with detecting real cancer cases, pointing more towards data issues.
:
ISTQB CT-AI Syllabus Section 4.2 on Training, Validation, and Test Datasets emphasizes the importance of using representative datasets to ensure the model generalizes well to real-world data.
Sample Exam Questions document, Question #40 addresses issues related to data representativeness and model generalization.
質問 # 100
Which ONE of the following characteristics is the least likely to cause safety related issues for an Al system?
SELECT ONE OPTION
正解：C
解説：
The question asks which characteristic is least likely to cause safety-related issues for an AI system. Let's evaluate each option:
Non-determinism (A): Non-deterministic systems can produce different outcomes even with the same inputs, which can lead to unpredictable behavior and potential safety issues.
Robustness (B): Robustness refers to the ability of the system to handle errors, anomalies, and unexpected inputs gracefully. A robust system is less likely to cause safety issues because it can maintain functionality under varied conditions.
High complexity (C): High complexity in AI systems can lead to difficulties in understanding, predicting, and managing the system's behavior, which can cause safety-related issues.
Self-learning (D): Self-learning systems adapt based on new data, which can lead to unexpected changes in behavior. If not properly monitored and controlled, this can result in safety issues.
Reference:
ISTQB CT-AI Syllabus Section 2.8 on Safety and AI discusses various factors affecting the safety of AI systems, emphasizing the importance of robustness in maintaining safe operation.
質問 # 101
Which of the following is an example of an input change where it would be expected that the AI system should be able to adapt?
正解：B
解説：
The syllabus explains that input changes that arein the same domainas what was used for training are expected to be handled with adaptability:
"Adaptability refers to the ability of a system to adjust its behavior in response to changes in its environment or inputs. This includes changes to the inputs which are still within the expected operational range of the system, such as resolution changes in images or sensor data." (Reference: ISTQB CT-AI Syllabus v1.0, Section 7.6 and 8.2)
質問 # 102
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