Which ONE of the following characteristics is the least likely to cause safety related issues for an Al system?
Correct Answer:
B
The question asks which characteristic is least likely to cause safety-related issues for an AI system. Let's evaluate each option:
✑ Non-determinism (A): Non-deterministic systems can produce different outcomes
even with the same inputs, which can lead to unpredictable behavior and potential safety issues.
✑ Robustness (B): Robustness refers to the ability of the system to handle errors,
anomalies, and unexpected inputs gracefully. A robust system is less likely to cause safety issues because it can maintain functionality under varied conditions.
✑ High complexity (C): High complexity in AI systems can lead to difficulties in
understanding, predicting, and managing the system's behavior, which can cause safety-related issues.
✑ Self-learning (D): Self-learning systems adapt based on new data, which can lead
to unexpected changes in behavior. If not properly monitored and controlled, this can result in safety issues.
References:
✑ ISTQB CT-AI Syllabus Section 2.8 on Safety and AI discusses various factors affecting the safety of AI systems, emphasizing the importance of robustness in maintaining safe operation.
An image classification system is being trained for classifying faces of humans. The distribution of the data is 70% ethnicity A and 30% for ethnicities B, C and D. Based ONLY on the above information, which of the following options BEST describes the situation of this image classification system?
SELECT ONE OPTION
Correct Answer:
B
✑ A. This is an example of expert system bias.
✑ B. This is an example of sample bias.
✑ C. This is an example of hyperparameter bias.
✑ D. This is an example of algorithmic bias.
Based on the provided information, option B (sample bias) best describes the situation because the training data is skewed towards ethnicity A, potentially leading to biased model performance.
Which ONE of the following options represents a technology MOST TYPICALLY used to implement Al?
SELECT ONE OPTION
Correct Answer:
D
Technology Most Typically Used to Implement AI: Genetic algorithms are a well-known technique used in AI. They are inspired by the process of natural selection and are used to find approximate solutions to optimization and search problems. Unlike search engines, procedural programming, or case control structures, genetic algorithms are specifically designed for evolving solutions and are commonly employed in AI implementations. Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 1.4 AI Technologies, which identifies different technologies used to implement AI.
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
Correct Answer:
C
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 is least likely to 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, the least likely reason for the incredible growth in the number of parameters is C. 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.
Which ONE of the following models BEST describes a way to model defect prediction by looking at the history of bugs in modules by using code quality metrics of modules of historical versions as input?
SELECT ONE OPTION
Correct Answer:
D
Defect prediction models aim to identify parts of the software that are likely to contain defects by analyzing historical data and code quality metrics. The primary goal is to use this predictive information to allocate testing and maintenance resources effectively. Let's break down why option D is the correct choice:
✑ Understanding Classification Models:
✑ Input Data - Code Quality Metrics:
✑ Historical Data:
✑ Why Option D is Correct:
✑ Eliminating Other Options:
References:
✑ ISTQB CT-AI Syllabus, Section 9.5, Metamorphic Testing (MT), describes various testing techniques including classification models for defect prediction.
✑ "Using AI for Defect Prediction" (ISTQB CT-AI Syllabus, Section 11.5.1).
