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NEW QUESTION # 54
A neural network has been designed and created to assist day-traders improve efficiency when buying and selling commodities in a rapidly changing market. Suppose the test team executes a test on the neural network where each neuron is examined. For this network the shortest path indicates a buy, and it will only occur when the one-day predicted value of the commodity is greater than the spot price by 0.75%. The neurons are stimulated by entering commodity prices and testers verify that they activate only when the future value exceeds the spot price by at least 0.75%.
Which of the following statements BEST explains the type of coverage being tested on the neural network?
Answer: D
Explanation:
Threshold coverageis a specific type of coverage measure used in neural network testing. It ensures that each neuron in the network achieves an activation value greater than a specified threshold. This is particularly relevant to the scenario described, where testers verify that neurons activate only when the future value of the commodity exceeds the spot price by at least0.75%.
* Threshold-based activation:The test case in the question isexplicitly verifying whether neurons activate only when a certain threshold (0.75%) is exceeded.This aligns perfectly with the definition ofthreshold coverage.
* Common in Neural Network Testing:Threshold coverage is used to measurewhether each neuron in a neural network reaches a specified activation value, ensuring that the neural network behaves as expected when exposed to different test inputs.
* Precedent in Research:TheDeepXplore frameworkused a threshold of0.75%to identify incorrect behaviors in neural networks, making this coverage criterion well-documented in AI testing research.
* (B) Neuron Coverage#
* Neuron coverageonly checks whether a neuron activates (non-zero value)at some point during testing. It does not consider specific activation thresholds, making it less precise for this scenario.
* (C) Sign-Change Coverage#
* This coverage measures whether each neuron exhibitsboth positive and negative activation values, which isnot relevant to the given scenario(where activation only matters when exceeding a specific threshold).
* (D) Value-Change Coverage#
* This coverage requires each neuron to producetwo activation values that differ by a chosen threshold, but the question focuses onwhether activation occurs beyond a fixed threshold, not changes in activation values.
* Threshold coverage ensures that neurons exceed a given activation threshold"Full threshold coverage requires that each neuron in the neural network achieves an activation value greater than a specified threshold. The researchers who created the DeepXplore framework suggested neuron coverage should be measured based on an activation value exceeding a threshold, changing based on the situation." Why is Threshold Coverage Correct?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, asthreshold coverage ensures the neural network's activation is correctly evaluated based on the required condition (0.75%).
NEW QUESTION # 55
Which statement regarding AI for defect prediction is correct?
Choose ONE option (1 out of 4)
Answer: B
Explanation:
Section5.3 - AI Support for Defect Predictionof the ISTQB CT-AI syllabus explains that AI-based defect prediction models rely onhistorical patterns, including past defects, code behavior, and similar system configurations. ML models trained on prior defect data can identifycomponents likely to contain defects when new changes resemble previous defect-inducing patterns. This directly supports OptionA, which states that defect prediction is most effective when based on previous similar constellations.
Option B is incorrect: ML can predictwhich componentsare likely to fail, not only whether defects exist.
Option C is incomplete; code metrics help, but defect prediction relies onmanycontextual features (historical defects, code churn, commit frequency, etc.). Option D is wrong because defect prediction isnotbased on formal principles and typically requiresmany features, not just a few.
Thus,Option Ais the correct and syllabus-consistent answer.
NEW QUESTION # 56
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: D
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 # 57
You are conducting a user acceptance test of a decision-support recommendation system used in a data processing business. In addition to testing the functional performance of the recommendation systems, what else would you be MOST likely to test?
Answer: D
Explanation:
In addition to testing the functional performance of the recommendation system, it is also important to test the amount of system resources consumed by each recommendation. This includes evaluating factors like processing time, memory usage, and the efficiency of the system when making recommendations. This ensures that the system performs well under the required load and is scalable for business use.
NEW QUESTION # 58
"AllerEgo" is a product that uses sell-learning to predict the behavior of a pilot under combat situation for a variety of terrains and enemy aircraft formations. Post training the model was exposed to the real- world data and the model was found to be behaving poorly. A lot of data quality tests had been performed on the data to bring it into a shape fit for training and testing.
Which ONE of the following options is least likely to describes the possible reason for the fall in the performance, especially when considering the self-learning nature of the Al system?
SELECT ONE OPTION
The difficulty of defining criteria for improvement before the model can be accepted.
The fast pace of change did not allow sufficient time for testing.
The unknown nature and insufficient specification of the operating environment might have caused the poor performance.
There was an algorithmic bias in the Al system.
Answer: A
Explanation:
Given the context of the self-learning nature and the need for real-time adaptability, option A is least likely to describe the fall in performance because it deals with acceptance criteria rather than real-time performance issues.
NEW QUESTION # 59
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