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私たちISTQBのCT-AI学習クイズは、仕上げの体系的な分析の分野での近年の試験状況のさまざまな専門家から作られ、学生の要求をできるだけ満たし、同時にチェックとレビューを行う専門スタッフがいますCT-AI実践教材、学生の学習に高品質の情報を楽しんでもらいました。 試験の多様性により、MogiExamのCT-AI学習教材もさまざまな種類の学習教材にまとめられているため、学生は必要なCT-AIガイド急流の情報をすばやく見つけることができます。
質問 # 138
A local business has a mail pickup/delivery robot for their office. The robot currently uses a track to move between pickup/drop-off locations. When it arrives at a destination, the robot stops to allow a human to remove or deposit mail. The office has decided to upgrade the robot to include AI capabilities that allow the robot to perform its duties without a track, without running into obstacles, and without human intervention.
The test team is creating a list of new and previously established test objectives and acceptance criteria to be used in the testing of the robot upgrade. Which of the following test objectives will test an AI quality characteristic for this system?
正解：D
解説：
In the syllabus, theevolutioncharacteristic for AI-based systems means the ability of the system to evolve and adapt its behavior in response to changes in the environment or in its own performance:
"Evolution is the system's ability to change itself to adapt to new situations, different hardware, or a changing operational environment." (Reference: ISTQB CT-AI Syllabus v1.0, Section 2.3)
質問 # 139
Consider a natural language processing (NLP) algorithm that attempts to predict the next word that you would like to type in a text message. An update to the algorithm has been created that should increase the accuracy of the predictions based on user typing patterns. The old algorithm was rated for accuracy by the users. Then, after the new update was released, the users rated the updated algorithm. A statistical test was used to compare between the two versions of the algorithm to see whether or not the update should remain in place.
This is an example of what type of testing?
正解：A
解説：
A/B testing is a statistical testing method that compares two different versions of a system to determine which one performs better. In this scenario, theold NLP algorithmwas rated for accuracy, and after the update, the new algorithmwas also rated by users. A statistical test was performed to compare the two versions, which is the fundamental approach ofA/B testing.
A/B testing is commonly used in:
* User experience testing(e.g., comparing different versions of a website).
* ML model evaluation(e.g., comparing two AI-based classifiers).
* Performance assessment(e.g., determining if a new recommendation algorithm is more effective).
This approach allows for data-driven decisions, ensuring that any changes to the system result in meaningful improvements.
* Section 9.4 - A/B Testingstates that A/B testing is used to compare updates in AI-based systems to determine if the newer version is better.
Reference from ISTQB Certified Tester AI Testing Study Guide:
質問 # 140
A test engineer is planning testing for a wearable medical device using AI. The medical device will detect possible heart issues in patients and dispatch emergency services automatically. It is not expected that many patients will have heart issues, and this is reflected in the available data. In this case, it is decided it is more important that emergency services are not sent un-necessarily, than it is to detect actual heart problems.
Which ONE of the following metrics should the test engineer choose to ensure that the emergency services are sent only when needed?
正解：B
解説：
The correct answer is D. Precision . In this scenario, a positive prediction means that the system detects a possible heart issue and dispatches emergency services. The stated priority is to avoid sending emergency services unnecessarily. That means the test engineer must minimize false positives , because a false positive would incorrectly classify a patient as needing emergency assistance. The CT-AI syllabus states that precision is suitable when the cost of false positives is high and confidence in positive outcomes needs to be high.
Accuracy is a weak choice because the data is imbalanced: most patients are not expected to have heart issues, so a model could appear accurate while still making unacceptable emergency-dispatch errors. MSE and R- squared are regression metrics, not classification metrics, and the problem is a classification decision: dispatch or do not dispatch. Recall would be appropriate if the main concern were not missing true heart issues, but the question explicitly prioritizes avoiding unnecessary dispatches. Therefore, precision is the best functional performance metric for this acceptance objective.
References/topics: CT-AI Syllabus Chapter 5, Sections 5.1 and 5.4 "Selecting ML Functional Performance Metrics."
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質問 # 141
Which ONE of the below types of testing is NOT a type of experience based testing applied to an AI based system?
正解：B
解説：
The correct answer is B . The CT-AI syllabus identifies experience-based testing as including error guessing, exploratory testing, and checklist-based testing , all of which may be applied to AI-based systems. It also explicitly relates exploratory testing to exploratory data analysis, where data is examined for patterns, relationships, trends, outliers, distribution, format, and ranges.
Option A matches exploratory testing and EDA, because the tester explores training data to guide subsequent testing. Option C matches error guessing, because the tester uses knowledge of previous ML-system failures, such as biased training data, to identify likely faults. Option D matches checklist-based testing, because the syllabus cites Google's ML test checklist as an effective checklist approach for ML systems.
Option B is not, by itself, an experience-based AI testing type. Usability testing may be important for AI- based systems, especially where users consume predictions, recommendations, or explanations, but it is classified as a quality-characteristic or user-experience concern, not as one of the named experience-based techniques in this syllabus context.
References/topics: CT-AI Syllabus Chapter 9, Section 9.6 "Experience-Based Testing of AI-Based Systems"; Section 9.6.1 "Exploratory Testing and EDA."
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質問 # 142
A neural network has been designed and created to assist day-traders improve efficiency when buying and selling commodities in a rapidly changing market. Suppose the test team executes a test on the neural network where each neuron is examined. For this network the shortest path indicates a buy, and it will only occur when the one-day predicted value of the commodity is greater than the spot price by 0.75%. The neurons are stimulated by entering commodity prices and testers verify that they activate only when the future value exceeds the spot price by at least 0.75%.
Which of the following statements BEST explains the type of coverage being tested on the neural network?
正解：B
解説：
Threshold coverageis a specific type of coverage measure used in neural network testing. It ensures that each neuron in the network achieves an activation value greater than a specified threshold. This is particularly relevant to the scenario described, where testers verify that neurons activate only when the future value of the commodity exceeds the spot price by at least0.75%.
* Threshold-based activation:The test case in the question isexplicitly verifying whether neurons activate only when a certain threshold (0.75%) is exceeded.This aligns perfectly with the definition ofthreshold coverage.
* Common in Neural Network Testing:Threshold coverage is used to measurewhether each neuron in a neural network reaches a specified activation value, ensuring that the neural network behaves as expected when exposed to different test inputs.
* Precedent in Research:TheDeepXplore frameworkused a threshold of0.75%to identify incorrect behaviors in neural networks, making this coverage criterion well-documented in AI testing research.
* (B) Neuron Coverage#
* Neuron coverageonly checks whether a neuron activates (non-zero value)at some point during testing. It does not consider specific activation thresholds, making it less precise for this scenario.
* (C) Sign-Change Coverage#
* This coverage measures whether each neuron exhibitsboth positive and negative activation values, which isnot relevant to the given scenario(where activation only matters when exceeding a specific threshold).
* (D) Value-Change Coverage#
* This coverage requires each neuron to producetwo activation values that differ by a chosen threshold, but the question focuses onwhether activation occurs beyond a fixed threshold, not changes in activation values.
* Threshold coverage ensures that neurons exceed a given activation threshold"Full threshold coverage requires that each neuron in the neural network achieves an activation value greater than a specified threshold. The researchers who created the DeepXplore framework suggested neuron coverage should be measured based on an activation value exceeding a threshold, changing based on the situation." Why is Threshold Coverage Correct?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, asthreshold coverage ensures the neural network's activation is correctly evaluated based on the required condition (0.75%).
質問 # 143
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Certified Tester AI Testing Exam試験は大多数の受験者にとって難しい難題であることは広く受け入れられていますが、関連するCT-AI認定はこの分野の労働者にとって非常に重要であるため、多くの労働者はこの課題に取り組む必要があります。 幸いなことに、この種の質問について心配する必要はありません。このWebサイトMogiExamで最適なソリューションを見つけることができるので、CT-AIトレーニング資料です。 テクノロジー、人材、施設への継続的な投資により、当社ISTQBの未来はこれまでになく輝かしく見えました。 優れたCT-AI試験問題により、CT-AI試験に合格します。
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