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| Certification Vendor: | ISTQB |
|---|---|
| Exam Name: | ISTQB Certified Tester AI Testing (CT-AI) Exam |
| Exam Number: | CT-AI |
| Real Exam Qty: | 40 |
| Exam Format: | Multiple Choice |
| Exam Duration: | 60 minutes |
| Related Certifications: | ISTQB Certified Tester Foundation Level (CTFL) |
| Available Languages: | English |
| Certificate Validity Period: | Lifetime |
| Passing Score: | 65% |
| Recommended Training: | ISTQB Accredited Training Providers |
| Exam Registration: | ISTQB Official Website |
| Sample Questions: | ISTQB CT-AI Sample Questions |
| Exam Way: | Online and onsite proctored exam via accredited ISTQB examination providers |
| Pre Condition: | Recommended prior knowledge of ISTQB Foundation Level (CTFL) and basic understanding of software testing concepts and machine learning fundamentals. |
| Official Syllabus URL: | https://www.istqb.org |
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質問 # 107
An e-commerce developer built an application for automatic classification of online products in order to allow customers to select products faster. The goal is to provide more relevant products to the user based on prior purchases.
Which of the following factors is necessary for a supervised machine learning algorithm to be successful?
正解：C
解説：
The syllabus explains that supervised learning requires correctly labeled data so the algorithm can learn the relationship between input features and output labels:
"In supervised learning, the algorithm creates the ML model from labeled data during the training phase. The labeled data is used to infer the relationship between the input data and output labels." (Reference: ISTQB CT-AI Syllabus v1.0, Section 3.1.1)
質問 # 108
A tourist calls an airline to book a ticket and is connected with an automated system which is able to recognize speech, understand requests related to purchasing a ticket, and provide relevant travel options. When the tourist asks about the expected weather at the destination or potential impacts on operations because of the tight labor market the only response from the automated system is: "I don't understand your question." This AI system should be categorized as?
正解：B
解説：
Narrow AI (also known as Weak AI) is designed to perform specific tasks or solve particular problems within a limited domain, such as booking a ticket or recognizing speech. In this case, the AI system is specialized in handling flight bookings and related tasks but is not capable of addressing broader or more complex questions, like weather conditions or labor market impacts.
質問 # 109
Which of the following is an example of overfitting?
正解：D
質問 # 110
Pairwise testing can be used in the context of self-driving cars for controlling an explosion in the number of combinations of parameters.
Which ONE of the following options is LEAST likely to be a reason for this incredible growth of parameters?
SELECT ONE OPTION
正解：C
解説：
Pairwise testing is used to handle the large number of combinations of parameters that can arise in complex systems like self-driving cars. The question asks which of the given options isleast likelyto be a reason for the explosion in the number of parameters.
Different Road Types (A): Self-driving cars must operate on various road types, such as highways, city streets, rural roads, etc. Each road type can have different characteristics, requiring the car's system to adapt and handle different scenarios. Thus, this is a significant factor contributing to the growth of parameters.
Different Weather Conditions (B): Weather conditions such as rain, snow, fog, and bright sunlight significantly affect the performance of self-driving cars. The car's sensors and algorithms must adapt to these varying conditions, which adds to the number of parameters that need to be considered.
ML Model Metrics to Evaluate Functional Performance (C): While evaluating machine learning (ML) model performance is crucial, it does not directly contribute to the explosion of parameter combinations in the same way that road types, weather conditions, and car features do. Metrics are used to measure and assess performance but are not themselves variable conditions that the system must handle.
Different Features like ADAS, Lane Change Assistance, etc. (D): Advanced Driver Assistance Systems (ADAS) and other features add complexity to self-driving cars. Each feature can have multiple settings and operational modes, contributing to the overall number of parameters.
Hence, theleast likelyreason for the incredible growth in the number of parameters isC. ML model metrics to evaluate the functional performance.
ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing discusses the application of this technique to manage the combinations of different variables in AI-based systems, including those used in self-driving cars.
Sample Exam Questions document, Question #29 provides context for the explosion in parameter combinations in self-driving cars and highlights the use of pairwise testing as a method to manage this complexity.
質問 # 111
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：D
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 # 112
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CT-AI試験解答: https://www.passtest.jp/ISTQB/CT-AI-shiken.html
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