CT-AI認證考試題庫 -最新最有效的CT-AI考試學習資料

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ISTQB CT-AI Exam Overview:

Certification Vendor:	ISTQB
Exam Name:	ISTQB Certified Tester - AI Testing
Exam Number:	CT-AI
Certificate Validity Period:	Lifetime (no expiration)
Related Certifications:	ISTQB CTFL ISTQB CTAL-TTA
Exam Format:	Multiple Choice
Available Languages:	English
Exam Price:	EUR 250
Real Exam Qty:	40
Exam Duration:	60 minutes
Passing Score:	65%
Sample Questions:	ISTQB CT-AI Sample Questions
Exam Way:	Online proctored or in-person at authorized testing centers
Pre Condition:	ISTQB CTFL (Certified Tester Foundation Level) certification is recommended but not mandatory
Official Syllabus URL:	https://www.istqb.org/certifications/artificial-intelligence-testing-certification

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ISTQB CT-AI 考試大綱：

主題	簡介
主題 1	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
主題 2	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 3	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 4	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 5	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
主題 6	systems from those required for conventional systems.
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	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.

最新的 ISTQB AI Testing CT-AI 免費考試真題 (Q55-Q60):

問題 #55
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?

A. The robot must evolve to optimize its routing
B. The robot must record the time of each delivery which is compiled into a report
C. The robot must recharge for no more than six hours a day
D. The robot must complete 99.99% of its deliveries each day

答案：A

解題說明：
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."

問題 #56
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 determinedthat 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. 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.
B. 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.
C. All high priority defects will be identified using this method.
D. The number of parameters to test can be reduced to less than a dozen.

答案：A

解題說明：
Pairwise testing is a combinatorial testing technique that reduces the number of test cases by focusing on testing interactions between pairs of parameters rather than all possible combinations. It is widely used in AI- based systems, including autonomous vehicles, where the number of possible input parameter combinations can be extremely high.
* Option A:"The number of parameters to test can be reduced to less than a dozen."
* This is incorrect. While pairwise testing significantly reduces the number of test cases, it does not necessarily limit them to a fixed number like a dozen. The final number of tests depends on the number of parameters and their possible values.
* Option B:"All high priority defects will be identified using this method."
* This is incorrect. While pairwise testing is effective in detecting defects caused by interactions between two parameters, it may not uncover defects resulting from more complex interactions involving three or more parameters.
* Option 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."
* This is the correct answer. Even though pairwise testing reduces the number of test cases, AI- based systems such as autonomous vehicles still have a large number of test scenarios. Therefore, automation is often necessary to execute all test cases within the available time.
* Option D:"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."
* This is incorrect. Pairwise testing can still be applied to AI-based systems, including those that evolve over time. However, additional testing techniques may be required to verify evolving behavior.
* Pairwise Testing for AI Systems:"Pairwise testing is widely used because it effectively reduces the number of test cases while maintaining defect detection capability".
* Automation Requirement:"In practice, even with pairwise testing, extensive test suites may still require automation".
Analysis of the Answer Options:ISTQB CT-AI Syllabus References:

問題 #57
Which of the following statements about ML functional performance metrics is correct?
Choose ONE option (1 out of 4)

A. The receiver operating characteristic curve shows, depending on parameters, how well the model distinguishes between different clusters.
B. Metrics used to measure clustering include intra-cluster metrics that measure the proximity of a cluster's data points.
C. The silhouette coefficient describes how well the regression model fits the dependent variables.
D. The R-squared metric indicates how well the model distinguishes between different classes based on the ROC curve.

答案：B

解題說明：
The ISTQB CT-AI syllabus explains ML performance metrics in Section3.2 - Evaluating ML Models. For clustering, which is an unsupervised learning method, the syllabus lists metrics such asintra-cluster distance, inter-cluster distance, and coherence measures. Intra-cluster metrics evaluate how close data points are within a cluster, which directly corresponds to Option A.
Option B is incorrect becauseR-squaredis a regression metric measuring goodness-of-fit, not classification performance, and has no connection to ROC curves. Option C is wrong because thesilhouette coefficientis also a clustering metric, measuring cohesion vs. separation-not regression accuracy. Option D is incorrect because ROC curves evaluatebinary or multiclass classification, not clustering.
Thus, OptionAis the only accurate statement based on the syllabus.

問題 #58
Which ONE of the below is MOST likely to indicate a problem with underfitting in an ML model?

A. The model is inaccurate on data similar to the training data
B. The model is vulnerable to adversarial attacks
C. The model fails to generalize on new data
D. The model uses a large amount of resources to make a prediction

答案：A

解題說明：
Underfitting occurs when the model is too simplistic to capture the underlying patterns in the training data. This results in poor performance not only on new data but also on the data it was trained on. Thus, if the model is inaccurate on data similar to the training data, it suggests underfitting.

問題 #59
Which of the following approaches would help overcome testing challenges associated with probabilistic and non-deterministic AI-based systems?

A. Run the test several times to generate a statistically valid test result to ensure that an appropriate number of answers are accurate.
B. Run the test several times to ensure that the AI always returns the same correct test result.
C. Decompose the system test into multiple data ingestion tests to determine if the AI system is getting precise and accurate input data.
D. Decompose the system test into multiple data ingestion tests to determine if the AI system is getting a sufficient volume of input data.

答案：A

解題說明：
Probabilistic and non-deterministic AI-based systems do not always produce the same output for identical inputs. This makes traditional testing approaches ineffective. Instead, the best approach is to run tests multiple times and analyze results statistically. Statistical Validity: Running tests multiple times ensures that observed results are statistically significant. Instead of relying on a single test run, analyzing multiple iterations helps determine trends, probabilities, and outliers.
Expected Result Tolerance: AI-based systems may produce different results within an acceptable range. Defining acceptable tolerances (e.g., "result must be within 2% of the optimal value") improves test effectiveness.

問題 #60
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