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| Certification Vendor: | ISTQB |
|---|---|
| Exam Name: | ISTQB Certified Tester AI Testing (CT-AI) Exam |
| Exam Number: | CT-AI |
| Exam Format: | Multiple Choice |
| Passing Score: | 65% |
| Available Languages: | English |
| Real Exam Qty: | 40 |
| Certificate Validity Period: | Lifetime |
| Exam Duration: | 60 minutes |
| Related Certifications: | ISTQB Certified Tester Foundation Level (CTFL) |
| 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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NEW QUESTION # 142
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?
Answer: A
Explanation:
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:
NEW QUESTION # 143
Which statement regarding testing transparency, explainability, or interpretability is MOST correct?
Choose ONE option (1 out of 4)
Answer: A
Explanation:
The ISTQB CT-AI syllabus states in Section2.10 - Explainability, Transparency, and Interpretabilitythat interpretability isuser-dependent, meaning different users understand explanations differently. This is because interpretability depends not only on the ML algorithm but also on the user's domain knowledge, experience, and expectations. OptionBdirectly reflects this syllabus principle: interpretability testing must consideruser background, and explanations must be comprehensible to the intended user group.
Option A is incorrect because explainability testing requires substantial information about the model, data, and expected behavior-not just exploratory effort. Option C is incorrect because explainability isnot generally quantifiable through dynamic testing alone, and the syllabus does not assert model-type specificity in this way. Option D exaggerates LIME's capabilities. LIME offers approximate local explanations, but cannotpreciselystate root causes; the syllabus emphasizes itslimitationsand that explanations are approximations, not exact reasons.
Therefore,Option Bis the most syllabus-aligned and correct statement.
NEW QUESTION # 144
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
Answer: A
Explanation:
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.
NEW QUESTION # 145
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?
Answer: D
Explanation:
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.
NEW QUESTION # 146
The activation value output for a neuron in a neural network is obtained by applying computation to the neuron.
Which ONE of the following options BEST describes the inputs used to compute the activation value?
Answer: C
NEW QUESTION # 147
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