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ISTQB CT-AIソフトウエア、CT-AI試験番号
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ISTQB Certified Tester AI Testing Exam 認定 CT-AI 試験問題 (Q28-Q33):
質問 # 28
You are testing an autonomous vehicle which uses AI to determine proper driving actions and responses. You have evaluated the parameters and combinations to be tested and have determined that there are too many to test in the time allowed. It has been suggested that you use pairwise testing to limit the parameters. Given the complexity of the software under test, what is likely the outcome from using pairwise testing?
- A. Pairwise cannot be applied to this problem because there is AI involved and the evolving values may result in unexpected results that cannot be verified
- B. All high priority defects will be identified using this method
- C. While the number of tests needed can be reduced, there may still be a large enough set of tests that automation will be required to execute all of them
- D. The number of parameters to test can be reduced to less than a dozen
正解：C
解説：
The syllabus states that while pairwise testing is effective at finding defects by reducing the number of test cases needed, the resulting test suite can still be extensive and require automation:
"Even the use of pairwise testing can result in extensive test suites... automation and virtual test environments often become necessary to allow the required tests to be run." (Reference: ISTQB CT-AI Syllabus v1.0, Section 9.2, Page 67 of 99)
質問 # 29
Which ONE of the following statements correctly describes the importance of flexibility for Al systems?
SELECT ONE OPTION
- A. Flexible Al systems allow for easier modification of the system as a whole.
- B. Al systems are inherently flexible.
- C. Al systems require changing of operational environments; therefore, flexibility is required.
- D. Self-learning systems are expected to deal with new situations without explicitly having to program for it.
正解：A
解説：
Flexibility in AI systems is crucial for various reasons, particularly because it allows for easier modification and adaptation of the system as a whole.
* AI systems are inherently flexible (A): This statement is not correct. While some AI systems may be designed to be flexible, they are not inherently flexible by nature. Flexibility depends on the system's design and implementation.
* AI systems require changing operational environments; therefore, flexibility is required (B):
While it's true that AI systems may need to operate in changing environments, this statement does not directly address the importance of flexibility for the modification of the system.
* Flexible AI systems allow for easier modification of the system as a whole (C): This statement correctly describes the importance of flexibility. Being able to modify AI systems easily is critical for their maintenance, adaptation to new requirements, and improvement.
* Self-learning systems are expected to deal with new situations without explicitly having to program for it (D): This statement relates to the adaptability of self-learning systems rather than their overall flexibility for modification.
Hence, the correct answer isC. Flexible AI systems allow for easier modification of the system as a whole.
:
ISTQB CT-AI Syllabus Section 2.1 on Flexibility and Adaptability discusses the importance of flexibility in AI systems and how it enables easier modification and adaptability to new situations.
Sample Exam Questions document, Question #30 highlights the importance of flexibility in AI systems.
質問 # 30
"Splendid Healthcare" has started developing a cancer detection system based on ML. The type of cancer they plan on detecting has 2% prevalence rate in the population of a particular geography. It is required that the model performs well for both normal and cancer patients.
Which ONE of the following combinations requires MAXIMIZATION?
SELECT ONE OPTION
- A. Maximize accuracy and recall
- B. Maximize recall and precision
- C. Maximize precision and accuracy
- D. Maximize specificity number of classes
正解：B
解説：
* Prevalence Rate and Model Performance:
* The cancer detection system being developed by "Splendid Healthcare" needs to account for the fact that the type of cancer has a 2% prevalence rate in the population. This indicates that the dataset is highly imbalanced with far fewer positive (cancer) cases compared to negative (normal) cases.
* Importance of Recall:
* Recall, also known as sensitivity or true positive rate, measures the proportion of actual positive cases that are correctly identified by the model. In medical diagnosis, especially cancer detection, recall is critical because missing a positive case (false negative) could have severe consequences for the patient. Therefore, maximizing recall ensures that most, if not all, cancer cases are detected.
* Importance of Precision:
* Precision measures the proportion of predicted positive cases that are actually positive. High precision reduces the number of false positives, meaning fewer people will be incorrectly diagnosed with cancer. This is also important to avoid unnecessary anxiety and further invasive testing for those who do not have the disease.
* Balancing Recall and Precision:
* In scenarios where both false negatives and false positives have significant consequences, it is crucial to balance recall and precision. This balance ensures that the model is not only good at detecting positive cases but also accurate in its predictions, reducing both types of errors.
* Accuracy and Specificity:
* While accuracy (the proportion of total correct predictions) is important, it can be misleading in imbalanced datasets. In this case, high accuracy could simply result from the model predicting the majority class (normal) correctly. Specificity (true negative rate) is also important, but for a cancer detection system, recall and precision take precedence to ensure positive cases are correctly and accurately identified.
* Conclusion:
* Therefore, for a cancer detection system with a low prevalence rate, maximizing both recall and precision is crucial to ensure effective and accurate detection of cancer cases.
This explanation aligns with the principles outlined in the ISTQB CT-AI Syllabus, particularly sections on performance metrics for ML models and handling imbalanced datasets (Chapter 5: ML Functional Performance Metrics).
質問 # 31
Max. Score: 2
Al-enabled medical devices are used nowadays for automating certain parts of the medical diagnostic processes. Since these are life-critical process the relevant authorities are considenng bringing about suitable certifications for these Al enabled medical devices. This certification may involve several facets of Al testing (I - V).
I.Autonomy
II.Maintainability
III.Safety
IV.Transparency
V.Side Effects
Which ONE of the following options contains the three MOST required aspects to be satisfied for the above scenario of certification of Al enabled medical devices?
SELECT ONE OPTION
- A. Aspects I, II, and III
- B. Aspects I, IV, and V
- C. Aspects III, IV, and V
- D. Aspects II, III and IV
正解：C
解説：
For AI-enabled medical devices, the most required aspects for certification are safety, transparency, and side effects. Here's why:
* Safety (Aspect III): Critical for ensuring that the AI system does not cause harm to patients.
* Transparency (Aspect IV): Important for understanding and verifying the decisions made by the AI system.
* Side Effects (Aspect V): Necessary to identify and mitigate any unintended consequences of the AI system.
Why Not Other Options:
* Autonomy and Maintainability (Aspects I and II): While important, they are secondary to the immediate concerns of safety, transparency, and managing side effects in life-critical processes.
References:This explanation is aligned with the critical quality characteristics for AI-based systems as mentioned in the ISTQB CT-AI syllabus, focusing on the certification of medical devices.
質問 # 32
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?
- A. Use it to prioritize defects automatically based on the time expected for the fix to be made, the speed of the fix, and the likelihood of regressions.
- B. Use it to assign defects to the best developer to resolve the problem and to load balance the defect assignments among the developers.
- C. Use it to review the code and determine where more defects are likely to occur so that testing can be targeted to those areas.
- D. Use it to determine the root cause of each defect and develop a process improvement plan that can be implemented to remove the most common root causes.
正解：B
解説：
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).
質問 # 33
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ISTQB CT-AI はこれで決まり
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