CT-AI更新，CT-AI考古題分享

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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	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	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 4	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 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	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.

>> CT-AI更新 <<

CT-AI考古題分享 - CT-AI資訊

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最新的 ISTQB AI Testing CT-AI 免費考試真題 (Q51-Q56):

問題 #51
Which of the below is NOT a characteristic that makes it difficult to use AI-based systems?

A. Lack of robustness
B. Lack of standards
C. Complexity
D. Lack of transparency

答案：A

解題說明：
While AI-based systems can face issues with complexity, lack of standards, and lack of transparency, the lack of robustness is not typically a characteristic that makes AI systems difficult to use. In fact, many AI systems can be designed to be robust, but their complexity, lack of transparency, and the absence of established standards can pose challenges in their implementation and adoption.

問題 #52
Which of the following is an example of a clustering problem that can be resolved by unsupervised learning?

A. Classifying muffin purchases based on the perceived attractiveness of their packaging
B. Estimating the expected purchase of cat food after a particularly successful ad campaign
C. Grouping individual fish together based on their types of fins
D. Associating shoppers with their shopping tendencies

答案：D

解題說明：
Clustering is a form ofunsupervised learning, which groups data points based onsimilarities without predefined labels. According toISTQB CT-AI Syllabus, clustering is used in scenarios where:
* The objective is to find natural groupings in data.
* The dataset does not have labeled outputs.
* Patterns and structures need to be identified automatically.
Analyzing the answer choices:
* A. Associating shoppers with their shopping tendencies # Correct
* Shoppers can be grouped based on purchasing behaviors(e.g., luxury shoppers vs. budget- conscious shoppers), which is a typical clustering application in market segmentation.
* B. Grouping individual fish together based on their types of fins # Incorrect
* If thetypes of fins are labeled, it becomes aclassification problem, which requires supervised learning.
* C. Classifying muffin purchases based on packaging attractiveness # Incorrect
* Classification, not clustering, because attractiveness scores or labels must be predefined.
* D. Estimating the expected purchase of cat food after an ad campaign # Incorrect
* This is a prediction task, best suited forregression models, which are part of supervised learning.
Thus,Option A is the best answer, asclusteringis used togroup shoppers based on tendencies without predefined labels.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 3.1.2 (Unsupervised Learning - Clustering and Association)
* ISTQB CT-AI Syllabus v1.0, Section 3.3 (Selecting a Form of ML - Clustering).

問題 #53
There is a growing backlog of unresolved defects for your project. You know the developers have an ML model that they have created which has learned which developers work on which type of software and the speed with which they resolve issues. How could you use this model to help reduce the backlog and implement more efficient defect resolution?

A. Use it to prioritize defects automatically based on the time expected for the fix to be made, the speed of the fix, and the likelihood of regressions
B. Use it to determine the root cause of each defect and develop a process improvement plan that can be implemented to remove the most common root causes
C. Use it to review the code and determine where more defects are likely to occur so that testing can be targeted to those areas
D. Use it to assign defects to the best developer to resolve the problem and to load balance the defect assignments among the developers

答案：D

解題說明：
The syllabus explains that ML models can be used to analyze reported defects and suggest which developers are best suited to fix them based on historical data about defect assignment and resolution speed:
"Assignment: ML models can suggest which developers are best suited to fix particular defects, based on the defect content and previous developer assignments." (Reference: ISTQB CT-AI Syllabus v1.0, Section 11.2, page 78 of 99)

問題 #54
"Splendid Healthcare" has started developing a cancer detection system based on ML. The type of cancer they plan on detecting has 2% prevalence rate in the population of a particular geography. It is required that the model performs well for both normal and cancer patients. Which ONE of the following combinations requires MAXIMIZATION?

A. Maximize recall and precision
B. Maximize specificity number of classes
C. Maximize accuracy and recall
D. Maximize precision and accuracy

答案：A

解題說明：
Prevalence Rate and Model Performance:
The cancer detection system being developed by "Splendid Healthcare" needs to account for the fact that the type of cancer has a 2% prevalence rate in the population. This indicates that the dataset is highly imbalanced with far fewer positive (cancer) cases compared to negative (normal) cases.
Importance of Recall:
Recall, also known as sensitivity or true positive rate, measures the proportion of actual positive cases that are correctly identified by the model. In medical diagnosis, especially cancer detection, recall is critical because missing a positive case (false negative) could have severe consequences for the patient. Therefore, maximizing recall ensures that most, if not all, cancer cases are detected.
Importance of Precision:
Precision measures the proportion of predicted positive cases that are actually positive. High precision reduces the number of false positives, meaning fewer people will be incorrectly diagnosed with cancer. This is also important to avoid unnecessary anxiety and further invasive testing for those who do not have the disease.
Balancing Recall and Precision:
In scenarios where both false negatives and false positives have significant consequences, it is crucial to balance recall and precision. This balance ensures that the model is not only good at detecting positive cases but also accurate in its predictions, reducing both types of errors.
Accuracy and Specificity:
While accuracy (the proportion of total correct predictions) is important, it can be misleading in imbalanced datasets. In this case, high accuracy could simply result from the model predicting the majority class (normal) correctly. Specificity (true negative rate) is also important, but for a cancer detection system, recall and precision take precedence to ensure positive cases are correctly and accurately identified.
Conclusion:
Therefore, for a cancer detection system with a low prevalence rate, maximizing both recall and precision is crucial to ensure effective and accurate detection of cancer cases.

問題 #55
Which of the following problems would best be solved using the supervised learning category of regression?

A. Determining if an animal is a pig or a cow based on image recognition
B. Recognizing a knife in carry-on luggage at a security checkpoint in an airport scanner
C. Predicting shopper purchasing behavior based on the category of shopper and the positioning of promotional displays within a store
D. Determining the optimal age for a chicken's egg-laying production using input data of the chicken's age and average daily egg production for one million chickens

答案：D

解題說明：
The syllabus states:
"Supervised learning... divides problems into two categories: classification and regression. Regression is used when the problem requires the ML model to predict a numeric output, for example predicting the age of a person based on their habits." (Reference: ISTQB CT-AI Syllabus v1.0, Section 3.1.1, Page 26 of 99)

問題 #56
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