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CT-AI学習ガイドは、ユーザーの要求に十分に応えるため、メモリを分離するための少しの知識になりますが、それらを一緒に追加すると、時間を活用できる日が非常に多くあります。 CT-AI試験準備により、ユーザーはいつでもどこでもがれきの時間を使って勉強し、勉強と生活をより合理的に調整することができます。私たちのCT-AIシミュレーションマテリアルを選択するのは良い選択です。私たちのステップに従ってください。自分を信じて、あなたは完璧にそれをすることができます！
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高品質のCT-AI準備ガイドを購入できるだけでなく、当社から大きな勇気と信頼を得ることもできます。多くのオンライン教育プラットフォームのリソースは、購入後に使用するためにユーザー登録によって提供される必要がありますが、それは当社のウェブサイトでは簡単です。 CT-AIガイドトレントの無料デモを提供しています。登録せずにいつでもダウンロードできます。高速配信-支払い後、10分以内にCT-AI試験トレントを受信できるため、迅速かつ効率的に学習できます。 何を待っていますか？ CT-AI試験問題を購入してください。
質問 # 32
Which of the following statements about explainable AI is correct?
Choose ONE option (1 out of 4)
正解：C
解説：
Section2.10 - Explainability and Transparencyof the ISTQB CT-AI syllabus describes explainable AI as the ability of a system to provide human-understandable insight into its decisions. The syllabus referencesThe Royal Society's reportas a foundational source explaining why explainability is important. Among the stated motivations is the need toincrease user trust and confidencein AI systems by making their decisions understandable and justifiable. Therefore, OptionCdirectly reflects the syllabus content .
Option A is incorrect because interpretability doesnotrefer to determining correctness of outputs; rather, it refers to understandinghowthe model arrives at outputs. Option B incorrectly frames explainability as the ability to investigate algorithms or training data; explainability is aboutunderstanding the model's decision- making, not reverse engineering its components. Option D is incorrect because explainability doesnot eliminate the need for risk and vulnerability assessments; the syllabus clearly emphasizes that testing, risk assessment, and robustness checks remain critical even when a model is explainable.
Thus, the only statement consistent with the syllabus isOption C.
質問 # 33
You have been developing test automation for an e-commerce system. One of the problems you are seeing is that object recognition in the GUI is having frequent failures. You have determined this is because the developers are changing the identifiers when they make code updates. How could AI help make the automation more reliable?
正解：D
解説：
The syllabus discusses using AI-based tools to reduce GUI test brittleness:
"AI can be used to reduce the brittleness of this approach, by employing AI-based tools to identify the correct objects using various criteria (e.g., XPath, label, id, class, X/Y coordinates), and to choose the historically most stable identification criteria."
質問 # 34
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 # 35
Which ONE of the following options describes a scenario of A/B testing the LEAST?
SELECT ONE OPTION
正解：D
解説：
A/B testing, also known as split testing, is a method used to compare two versions of a product or system to determine which one performs better. It is widely used in web development, marketing, and machine learning to optimize user experiences and model performance. Here's why option C is the least descriptive of an A/B testing scenario:
* Understanding A/B Testing:
* In A/B testing, two versions (A and B) of a system or feature are tested against each other. The objective is to measure which version performs better based on predefined metrics such as user engagement, conversion rates, or other performance indicators.
* Application in Machine Learning:
* In ML systems, A/B testing might involve comparing two different models, algorithms, or system configurations on the same set of data to observe which yields better results.
* Why Option C is the Least Descriptive:
* Option C describes comparing the performance of an ML system on two different input datasets.
This scenario focuses on the input data variation rather than the comparison of system versions or features, which is the essence of A/B testing. A/B testing typically involves a controlled experiment with two versions being tested under the same conditions, not different datasets.
* Clarifying the Other Options:
* A. A comparison of two different websites for the same company to observe from a user acceptance perspective: This is a classic example of A/B testing where two versions of a website are compared.
* B. A comparison of two different offers in a recommendation system to decide on the more effective offer for the same users: This is another example of A/B testing in a recommendation system.
* D. A comparison of the performance of two different ML implementations on the same input data: This fits the A/B testing model where two implementations are compared under the same conditions.
References:
* ISTQB CT-AI Syllabus, Section 9.4, A/B Testing, explains the methodology and application of A/B testing in various contexts.
* "Understanding A/B Testing" (ISTQB CT-AI Syllabus).
質問 # 36
A tourist calls an airline to book a ticket and is connected with an automated system which is able to recognize speech, understand requests related to purchasing a ticket, and provide relevant travel options. When the tourist asks about the expected weather at the destination or potential impacts on operations because of the tight labor market the only response from the automated system is: "I don't understand your question." This AI system should be categorized as?
正解：D
解説：
Narrow AI (also known as Weak AI) is designed to perform specific tasks or solve particular problems within a limited domain, such as booking a ticket or recognizing speech. In this case, the AI system is specialized in handling flight bookings and related tasks but is not capable of addressing broader or more complex questions, like weather conditions or labor market impacts.
質問 # 37
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知識は、将来価値のある報酬を提供できる無形資産と定義されているため、neverめないでください。また、CT-AI試験の準備は、試験に効果的に対処するのに十分な知識を提供できます。試験の受験者のニーズを満たすために、当社の専門家は完璧な配置とメッセージの科学的編集で当社のCT-AI練習資料を作成したため、完璧な資料を見つけるために他の多数の資料を勉強する必要はありません。 CT-AI試験クイズは、最高のヘルプを提供します。そして、CT-AIトレーニング資料は決してあなたを失望させません。
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