CT-AI題庫分享，CT-AI認證資料

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順便提一下，可以從雲存儲中下載Fast2test CT-AI考試題庫的完整版：https://drive.google.com/open?id=14D2aLFmjmgEgpeA2wJ42k4f7QPHOYydk

Fast2test為你提供了不同版本的資料以方便你的使用。PDF版的CT-AI考古題方便你的閱讀，為你真實地再現考試題目。軟體版本的CT-AI考古題作為一個測試引擎，可以幫助你隨時測試自己的準備情況。如果你想知道你是不是充分準備好了CT-AI考試，那麼你可以利用軟體版的考古題來測試一下自己的水準。這樣你就可以快速找出自己的弱點和不足，進而有利於你的下一步學習安排。

ISTQB CT-AI 考試大綱：

主題簡介
主題 1
  • Introduction to AI: This exam section covers topics such as the AI effect and how it influences the definition of AI. It covers how to distinguish between narrow AI, general AI, and super AI; moreover, the topics covered include describing how standards apply to AI-based systems.
主題 2
  • Methods and Techniques for the Testing of AI-Based Systems: In this section, the focus is on explaining how the testing of ML systems can help prevent adversarial attacks and data poisoning.
主題 3
  • Quality Characteristics for AI-Based Systems: This section covers topics covered how to explain the importance of flexibility and adaptability as characteristics of AI-based systems and describes the vitality of managing evolution for AI-based systems. It also covers how to recall the characteristics that make it difficult to use AI-based systems in safety-related applications.
主題 4
  • Machine Learning ML: This section includes the classification and regression as part of supervised learning, explaining the factors involved in the selection of ML algorithms, and demonstrating underfitting and overfitting.
主題 5
  • Testing AI-Based Systems Overview: In this section, focus is given to how system specifications for AI-based systems can create challenges in testing and explain automation bias and how this affects testing.
主題 6
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
主題 7
  • ML: Data: This section of the exam covers explaining the activities and challenges related to data preparation. It also covers how to test datasets create an ML model and recognize how poor data quality can cause problems with the resultant ML model.
主題 8
  • Neural Networks and Testing: This section of the exam covers defining the structure and function of a neural network including a DNN and the different coverage measures for neural networks.
主題 9
  • Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
主題 10
  • Testing AI-Specific Quality Characteristics: In this section, the topics covered are about the challenges in testing created by the self-learning of AI-based systems.
主題 11
  • systems from those required for conventional systems.

>> CT-AI題庫分享 <<

CT-AI認證資料 - CT-AI證照

世界500強企業中，有超過2/3的企業選擇了 ISTQB 電子商務軟體產品作為其核心的運用。因此，獲得 ISTQB 的認證，即使在強手林立的競爭環境中，你同樣能夠脫穎而出。考生想要通過 CT-AI 考試，最快速的方式是使用 ISTQB 的 CT-AI 考題，很多考生都是通過這種方式成功通過考試，可以快速掌握考試的相關資訊。

最新的 ISTQB AI Testing CT-AI 免費考試真題 (Q114-Q119):

問題 #114
The stakeholders of a machine learning model have confirmed that they understand the objective and purpose of the model, and ensured that the proposed model aligns with their business priorities. They have also selected a framework and a machine learning model that they will be using. What should be the next step to progress along the machine learning workflow?

答案：A

解題說明：
The ML workflow typically involves iterative steps, beginning with data preparation once the model and framework are selected. The syllabus explains:
"The steps shown in Figure 1 (the ML workflow) do not include the integration of the ML model with the non- ML parts of the overall system. Typically, ML models cannot be deployed in isolation and need to be integrated with the non-ML parts... The next step would be data preparation as part of the ML workflow to provide input data to support training by an ML algorithm or prediction by an ML model." (Reference: ISTQB CT-AI Syllabus v1.0, Sections 3.2 & 4.1)


問題 #115
Which of the following is a problem with AI-generated test cases that are generated from the requirements?

答案：C

解題說明：
AI-generated test cases are often created using machine learning (ML) models or heuristic algorithms. While these can be effective in generating large numbers of test cases quickly, they oftensuffer from the "test oracle problem."
* Test Oracle Problem:A test oracle is the mechanism used to determine the expected output of a test case. AI-generated test cases oftenlack expected resultsbecause AI-based tools do not inherently understand what the correct output should be.
* Difficulty in Verification:Without expected results, verifying test cases becomes challenging. Testers mustrely on heuristics, anomaly detection, or significant failures, rather than traditional pass/fail conditions.
* A (Slow Execution Time):AI-generated tests are typically automated and designed for efficiency. They are not inherently slow and often executefasterthan manually written tests.
* B (Defect-Prone Due to Nuance Issues):While AI-generated tests may struggle with some complexities in requirements, they primarilylack expected results, rather than failing due to an inability to detect nuances.
* C (Complicated Debugging Due to Many Steps):AI-generated testsreducedebugging complexity by limiting the number of steps required to reproduce failures.
* ISTQB CT-AI Syllabus (Section 11.3: Using AI for Test Case Generation)
* "AI-generated test cases often lack expected results, making it difficult to verify correctness without a test oracle.".
* "Verification often relies on detecting significant failures rather than having predefined expected results.".
Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Since AI-generated test cases frequentlylack expected results, verification becomes difficult, requiring testers tofocus on major failuresrather than precise pass/fail conditions. Thus, thecorrect answer is D.


問題 #116
Which two test procedures are BEST suited for CleverPropose system testing?
Choose TWO options (2 out of 5)

答案：A,B

解題說明：
The ISTQB CT-AI syllabus explains that AI-based decision-support systems benefit strongly fromback-to- back testingandmetamorphic testingwhen oracle problems exist or when limited regression tests are available. In this scenario, CleverPropose replaces an older advisory system.Back-to-back testing(Option A) is ideal because the outputs of the existing conventional system can serve as areference, enabling comparison against the new AI system. This is exactly what the syllabus recommends when AI is replacing a traditional deterministic system.
Metamorphic testing(Option C) is also appropriate, as stated in Section4.6 - Metamorphic Relations. With limited regression tests and complex decision logic, testers can define metamorphic relations such as "if customer income increases, risk rating should not worsen." These relations allow validation even when exact expected outputs are unavailable.
Exploratory data analysis (Option D) is not a system testing technique. Pairwise testing (Option E) is not well suited for complex AI-based financial advice systems. Adversarial testing (Option B) is more relevant for security-critical or robustness evaluation, not primary system testing for advisory tools.
Thus,A and Care the correct and syllabus-supported choices.


問題 #117
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?

答案：C

解題說明：
For AI-enabled medical devices, the most required aspects for certification are safety, transparency, and side effects.
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.


問題 #118
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?

答案：B

解題說明：
AI systems, particularly machine learning models, need to exhibit adaptability and flexibility to handle slight variations in input data without requiring retraining. The ISTQB CT-AI syllabus outlines adaptability as a crucial feature of AI systems, especially when the system is exposed to variations in its operational environment.
* Option A:"It has been trained to recognize cats and is given an image of a dog."
* This scenario introduces an entirely new class (dogs), which is outside the AI system's expected scope. If the AI was only trained to recognize cats, it would not be expected to recognize dogs correctly without retraining. This does not demonstrate adaptability as expected from an AI system.
* Option B:"It has been trained to recognize human faces at a particular resolution and it is given a human face image captured with a higher resolution."
* This is an example of an AI system encountering a variation of its training data rather than entirely new data. Most AI-based image processing models can adapt to different resolutions by applying downsampling or other pre-processing techniques. Since the data remains within the domain of human faces, the model should be able to process the higher-resolution image without significant issues.
* Option C:"It has been trained to analyze mathematical models and is given a set of landscape pictures to classify."
* This represents a complete shift in the data type from structured numerical data to unstructured image data. The AI system is unlikely to adapt effectively, as it has not been trained on image classification tasks.
* Option D:"It has been trained to analyze customer buying trend data and is given information on supplier cost data."
* This introduces a significant domain shift. Customer buying trends focus on consumer behavior, while supplier cost data relates to pricing structures and logistics. The AI system would likely require retraining to process the new data meaningfully.
* Adaptability Requirements:The syllabus discusses that AI-based systems must be able to adapt to changes in their operational environment and constraints, including minor variations in input quality (such as resolution changes).
* Autonomous Learning & Evolution:AI systems are expected to improve and handle evolving inputs based on prior experience.
* Challenges in Testing Self-Learning Systems:AI systems should be tested to ensure they function correctly when encountering new but related data, such as different resolutions of the same object.
Analysis of the Answer Options:ISTQB CT-AI Syllabus References:Thus,option Bis the best choice as it aligns with the adaptability characteristics expected from AI-based systems.


問題 #119
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ISTQB 的 CT-AI 考古題覆蓋了最新的考試指南，根據真實的 CT-AI 考試真題編訂，確保每位考生順利通過 CT-AI 考試。如果在考試過程中變題了，考生可以享受免費更新一年的考題服務，保障了考生的權利。CT-AI 考試適合於 ISTQB 技術人士開發，目的是為了測驗考生基於各種平臺的設計和開發應用知識技能。考生要考取 CT-AI 認證，必須要擁有兩年開發技術領域的能力。

CT-AI認證資料: https://tw.fast2test.com/CT-AI-premium-file.html

此外，這些Fast2test CT-AI考試題庫的部分內容現在是免費的：https://drive.google.com/open?id=14D2aLFmjmgEgpeA2wJ42k4f7QPHOYydk

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