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NEW QUESTION # 78
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: B
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.
References:
* 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 # 79
Which of the following is an example of a clustering problem that can be resolved by unsupervised learning?
Answer: D
Explanation:
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).
NEW QUESTION # 80
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?
Answer: D
Explanation:
AI and ML models can play a significant role in optimizing defect resolution processes. According to the ISTQB Certified Tester AI Testing (CT-AI) Syllabus, ML models can be used toanalyze defect reports, prioritize critical defects, and assign defects to developersbased on historical defect resolution patterns.
The key AI applications for defect management include:
* Defect Categorization- NLP techniques can analyze defect reports and classify them based on metadata like severity and impact.
* Defect Prioritization- ML models trained on past defects can predict which issues are likely to cause failures, allowing teams toprioritizethe most critical issues.
* Defect Assignment- AI-based models can suggest which developers are best suited for specific defects, optimizing the resolution process based on past performance and specialization.
From the given answer choices:
* Option A (Automatic Prioritization)is useful but does not directlyreduce backlog efficientlyby considering developer expertise and workload balancing.
* Option C (Root Cause Analysis for Process Improvement)is along-term strategybut does not directly address backlog reduction.
* Option D (Defect Prediction for Testing Focus)helps preemptively identify issues but does not resolve the existing backlog.
Thus,Option Bis the best choice as it aligns with AI's capability toassign defects to the most suitable developersbased on historical data, ensuring efficient defect resolution and backlog reduction.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 11.2 (Using AI to Analyze Reported Defects)
* ISTQB CT-AI Syllabus v1.0, Section 11.5 (Using AI for Defect Prediction).
NEW QUESTION # 81
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: D
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 # 82
Which ONE of the following options BEST DESCRIBES clustering?
SELECT ONE OPTION
Answer: B
Explanation:
Clustering is a type of machine learning technique used to group similar data points into clusters. It is a key concept in unsupervised learning, where the algorithm tries to find patterns or groupings in data without prior knowledge of output classes. Let's analyze each option:
A . Clustering is classification of a continuous quantity.
This is incorrect. Classification typically involves discrete categories, whereas clustering involves grouping similar data points. Classification of continuous quantities is generally referred to as regression.
B . Clustering is supervised learning.
This is incorrect. Clustering is an unsupervised learning technique because it does not rely on labeled data.
C . Clustering is done without prior knowledge of output classes.
This is correct. In clustering, the algorithm groups data points into clusters without any prior knowledge of the classes. It discovers the inherent structure in the data.
D . Clustering requires you to know the classes.
This is incorrect. Clustering does not require prior knowledge of classes. Instead, it aims to identify and form the classes or groups based on the data itself.
Therefore, the correct answer is C because clustering is an unsupervised learning technique done without prior knowledge of output classes.
NEW QUESTION # 83
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