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[General] Pass-Sure CT-AI考試重點和資格考試中的領先供應商和奇妙的ISTQB Certified Tester AI Testing Exam

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【General】 Pass-Sure CT-AI考試重點和資格考試中的領先供應商和奇妙的ISTQB Certified Tester AI Testing Exam

Posted at yesterday 00:13      View：8 | Replies：1        Print      Only Author   [Copy Link] 1#
2026 NewDumps最新的CT-AI PDF版考試題庫和CT-AI考試問題和答案免費分享：https://drive.google.com/open?id=1VAb-a9iCy4_Wx9it0biZFpFHOD8tgEsZ
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ISTQB CT-AI 考試大綱：
主題簡介
主題 1
  • systems from those required for conventional systems.
主題 2
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
主題 3
  • 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 4
  • 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 5
  • 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 6
  • 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 7
  • ML Functional Performance Metrics: In this section, the topics covered include how to calculate the ML functional performance metrics from a given set of confusion matrices.
主題 8
  • 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 9
  • Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.

CT-AI考試證照綜述，CT-AI考試資訊你在煩惱什麼呢？是因為ISTQB的CT-AI認證考試而煩惱嗎？確實，CT-AI考試是一門很難通過的考試。但是你也不用過分擔心。只要你利用了適當的方法，輕鬆地通過考試也不是不可能的。那麼你知道什麼是適當的方法嗎？使用NewDumps的CT-AI資料就是一種最好不過的方法。NewDumps一直以來幫助了很多參加IT認定考試的考生，並且得到了大家的一致好評。這個資料可以保證你一次通過考試，請放心使用。
最新的 ISTQB AI Testing CT-AI 免費考試真題 (Q50-Q55):問題 #50
Which of the following is THE LEAST appropriate tests to be performed for testing a feature related to autonomy?
SELECT ONE OPTION
  • A. Test for human handover when it should actually not be relinquishing control.
  • B. Test for human handover requiring mandatory relinquishing control.
  • C. Test for human handover after a given time interval.
  • D. Test for human handover to give rest to the system.
答案：A
解題說明：
* Testing Autonomy: Testing for human handover when it should not be relinquishing control is the least appropriate because it contradicts the very definition of autonomous systems. The other tests are relevant to ensuring smooth operation and transitions between human and AI control.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Testing Autonomous AI-Based Systems and Testing for Human-AI Interaction.

問題 #51
"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
  • A. A lack of focus on choosing the right functional-performance metrics.
  • B. The input data has not been tested for quality prior to use for testing.
  • C. A lack of focus on non-functional requirements testing.
  • D. A lack of similarity between the training and testing data.
答案：D
解題說明：
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.
References:
* 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.

問題 #52
Which ONE of the following tests is MOST likely to describe a useful test to help detect different kinds of biases in ML pipeline?
SELECT ONE OPTION
  • A. Test the model during model evaluation for data bias.
  • B. Testing the distribution shift in the training data for inappropriate bias.
  • C. Check the input test data for potential sample bias.
  • D. Testing the data pipeline for any sources for algorithmic bias.
答案：A
解題說明：
Detecting biases in the ML pipeline involves various tests to ensure fairness and accuracy throughout the ML process.
Testing the distribution shift in the training data for inappropriate bias (A): This involves checking if there is any shift in the data distribution that could lead to bias in the model. It is an important test but not the most direct method for detecting biases.
Test the model during model evaluation for data bias (B): This is a critical stage where the model is evaluated to detect any biases in the data it was trained on. It directly addresses potential data biases in the model.
Testing the data pipeline for any sources for algorithmic bias (C): This test is crucial as it helps identify biases that may originate from the data processing and transformation stages within the pipeline. Detecting sources of algorithmic bias ensures that the model does not inherit biases from these processes.
Check the input test data for potential sample bias (D): While this is an important step, it focuses more on the input data and less on the overall data pipeline.
Hence, the most likely useful test to help detect different kinds of biases in the ML pipeline is B. Test the model during model evaluation for data bias.
Reference:
ISTQB CT-AI Syllabus Section 8.3 on Testing for Algorithmic, Sample, and Inappropriate Bias discusses various tests that can be performed to detect biases at different stages of the ML pipeline.
Sample Exam Questions document, Question #32 highlights the importance of evaluating the model for biases.

問題 #53
Which ONE of the following options represents a technology MOST TYPICALLY used to implement Al?
SELECT ONE OPTION
  • A. Search engines
  • B. Case control structures
  • C. Procedural programming
  • D. Genetic algorithms
答案：D
解題說明：
* Technology Most Typically Used to Implement AI: Genetic algorithms are a well-known technique used in AI . They are inspired by the process of natural selection and are used to find approximate solutions to optimization and search problems. Unlike search engines, procedural programming, or case control structures, genetic algorithms are specifically designed for evolving solutions and are commonly employed in AI implementations.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 1.4 AI Technologies, which identifies different technologies used to implement AI.

問題 #54
Which of the following neural network coverage criteria can be adapted for its application?
Choose ONE option (1 out of 4)
  • A. Sign-Sign coverage
  • B. Neuron coverage
  • C. Sign-Change coverage
  • D. Threshold coverage
答案：D
解題說明：
Section4.2 - Test Coverage Criteria for AI Modelsof the ISTQB CT-AI syllabus describes neural network- specific coverage methods. Among the techniques,threshold coverageis explicitly noted asadaptable, meaning testers may choose different thresholds to determine whether neuron activation is considered
"covered." This flexibility makes threshold coverage adjustable to the model architecture, problem domain, and required test thoroughness.
Options A and B (Sign-Sign and Sign-Change coverage) are more rigid structural criteria and are not described as adaptable within the syllabus. They focus on sign patterns of neuron activations and do not allow altering thresholds. Option D, neuron coverage, measures the proportion of neurons activated at least once.
Although simple, it is not defined as an adaptable criterion. Its limitations are documented: it provides shallow insight and too easily achieves high coverage.
Onlythreshold coverageallows testers to adjust activation thresholds for more refined coverage measurement, makingOption Cthe correct choice.

問題 #55
......
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Posted at yesterday 12:06        Only Author  2#
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