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数年以来弊社のIt-PassportsのIT試験分野での研究を通して、弊社はこの職業での重要な存在になります。弊社の開発したソフトは非常に全面的です。ISTQBのCT-AI試験ソフトは販売量が一番高いソフトの一で、受験生をよく助けて受験生に試験に合格させます。知られているのはISTQBのCT-AI試験に合格すればITという職業でよく発展しています。
質問 # 122
A company is using a spam filter to attempt to identify which emails should be marked as spam. Detection rules are created by the filter that causes a message to be classified as spam. An attacker wishes to have all messages internal to the company be classified as spam. So, the attacker sends messages with obvious red flags in the body of the email and modifies the from portion of the email to make it appear that the emails have been sent by company members. The testers plan to use exploratory data analysis (EDA) to detect the attack and use this information to prevent future adversarial attacks.
How could EDA be used to detect this attack?
正解：C
解説：
Exploratory Data Analysis (EDA) is an essential technique for examining datasets to uncover patterns, trends, and anomalies, including outliers. In this case, the attacker manipulates the spam filter by injecting emails with red flags and masking them as internal company emails. The primary goal of EDA here is to detect these adversarial modifications.
* Detecting Outliers:
* EDA techniques such as statistical analysis, clustering, and visualization can reveal patterns in email metadata (e.g., sender details, email content, frequency).
* Outlier detection methods like Z-score, IQR (Interquartile Range), or machine learning-based anomaly detection can identify emails that significantly deviate from typical internal communications.
* Identifying Distribution Shifts:
* By analyzing the frequency and characteristics of emails flagged as spam, testers can detect if the attack has introduced unusual patterns.
* If a surge of internal emails is suddenly classified as spam, EDA can help verify whether these classifications are consistent with historical data.
* Feature Analysis for Adversarial Patterns:
* EDA enables visualization techniques such as scatter plots or histograms to distinguish normal emails from manipulated ones.
* Examining email metadata (e.g., changes in headers, unusual wording in email bodies) can reveal adversarial tactics.
* Counteracting Adversarial Attacks:
* Once anomalies are identified, the spam filter's detection rules can be improved by retraining the model on corrected datasets.
* The adversarial examples can be added to the training data to enhance the robustness of the filter against future attacks.
* Exploratory Data Analysis (EDA) is used to detect outliers and adversarial attacks."EDA is where data are examined for patterns, relationships, trends, and outliers. It involves the interactive, hypothesis-driven exploration of data."
* EDA can identify poisoned or manipulated data by detecting anomalies and distribution shifts.
"Testing to detect data poisoning is possible using EDA, as poisoned data may show up as outliers."
* EDA helps validate ML models and detect potential vulnerabilities."The use of exploratory techniques, primarily driven by data visualization, can help validate the ML algorithm being used, identify changes that result in efficient models, and leverage domain expertise." References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, as EDA is specifically useful for detecting outliers, which can help identify manipulated spam emails.
質問 # 123
Which ONE of the below is NOT likely to cause a data quality issue affecting a single ML model?
正解：A
解説：
The correct answer is C. Incorrect weights . The CT-AI syllabus lists typical dataset quality issues, including wrong data, incomplete data, mislabeled data, insufficient data, data not pre-processed, obsolete data, unbalanced data, unfair data, duplicate data, irrelevant data, privacy issues, and security issues. It specifically notes that wrong data may arise from a faulty sensor, and that incomplete data may be caused by security issues, hardware issues, or human error. Security issues may also involve fraudulent or misleading data deliberately inserted into training data.
Incorrect weights are different in nature. Weights are parameters learned within certain ML models, especially neural networks; they are part of the trained model, not a characteristic of the dataset itself. Incorrect or poorly optimized weights may produce poor predictions, but they do not constitute a data quality issue in the dataset. By contrast, faulty sensors can create wrong captured data, hardware issues can lead to missing data, and security issues can compromise or poison data.
References/topics: CT-AI Syllabus Chapter 4, Section 4.3 "Dataset Quality Issues"; Section 4.4 "Data Quality and its Effect on the ML Model."
質問 # 124
"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
正解：A
解説：
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.
Reference:
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.
質問 # 125
A robotic AI based system is being built by a logistics company to operate within its unmanned warehouses.
The warehouses can all be very different and new ones are being added each year. It is expected that the system will not require retraining for each warehouse, and will be able to learn the location of different items and move them to specified locations on request. Which ONE of the following attributes should be MOST carefully considered when specifying the objectives and acceptance criteria for the system?
正解：A
解説：
The correct answer is B. Adaptability . The scenario emphasizes different warehouse layouts, new warehouses being added, and the expectation that the robotic system will operate without retraining for each environment. The CT-AI syllabus defines adaptability as the ease with which a system can be modified for new situations, including different hardware and changing operational environments. It states that flexibility and adaptability are useful where the operational environment is not fully known, the system must cope with new operational environments, the system is expected to adapt to new situations, or it must determine when to change behaviour.
In this logistics context, acceptance criteria should specify how the robot adapts to unfamiliar warehouse structures, how quickly it learns item locations, what resources it may consume while adapting, and whether it still meets functional and non-functional requirements after adaptation. Option A is less central because the example does not involve fairness across protected or stakeholder groups. Option C is always relevant but not the main technical acceptance attribute. Option D may matter, but the dominant requirement is adaptation to changing environments.
References/topics: CT-AI Syllabus Chapter 2, Section 2.1 "Flexibility and Adaptability"; Chapter 8, Section 8.8 "Test Objectives and Acceptance Criteria."
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質問 # 126
"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
正解：A
解説：
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.
質問 # 127
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