This page was exported from Testking Free Dumps [ http://blog.testkingfree.com ] Export date:Thu Jan 16 18:49:31 2025 / +0000 GMT ___________________________________________________ Title: Practice CT-AI_v1.0_World Questions With Certification guide Q&A from Training Expert [Q10-Q24] --------------------------------------------------- Practice CT-AI_v1.0_World Questions With Certification guide Q&A from Training Expert TestKingFree Free ISQI CT-AI_v1.0_World Test Practice Test Questions Exam Dumps Q10. 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  Different Road Types  Different weather conditions  ML model metrics to evaluate the functional performance  Different features like ADAS, Lane Change Assistance etc. 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.Q11. A software component uses machine learning to recognize the digits from a scan of handwritten numbers. In the scenario above, which type of Machine Learning (ML) is this an example of?SELECT ONE OPTION  Reinforcement learning  Regression  Classification  Clustering Recognizing digits from a scan of handwritten numbers using machine learning is an example of classification.Here’s a breakdown:* Classification: This type of machine learning involves categorizing input data into predefined classes.In this scenario, the input data (handwritten digits) are classified into one of the 10 digit classes (0-9).* Why Not Other Options:* Reinforcement Learning: This involves learning by interacting with an environment to achieve a goal, which does not fit the problem of recognizing digits.* Regression: This is used for predicting continuous values, not discrete categories like digit recognition.* Clustering: This involves grouping similar data points together without predefined classes, which is not the case here.References:The explanation is based on the definitions of different machine learning types as outlined in the ISTQB CT-AI syllabus, specifically under supervised learning and classification.Q12. Which ONE of the following options BEST DESCRIBES clustering?SELECT ONE OPTION  Clustering is classification of a continuous quantity.  Clustering is supervised learning.  Clustering is done without prior knowledge of output classes.  Clustering requires you to know the classes. 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 isCbecause clustering is an unsupervised learning technique done without prior knowledge of output classes.Q13. Which ONE of the following describes a situation of back-to-back testing the LEAST?SELECT ONE OPTION  Comparison of the results of a current neural network model ML model implemented in platform A (for example Pytorch) with a similar neural network model ML model implemented in platform B (for example Tensorflow), for the same data.  Comparison of the results of a home-grown neural network model ML model with results in a neural network model implemented in a standard implementation (for example Pytorch) for same data  Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.  Comparison of the results of the current neural network ML model on the current data set with a slightly modified data set. Back-to-back testing is a method where the same set of tests are run on multiple implementations of the system to compare their outputs. This type of testing is typically used to ensure consistency and correctness by comparing the outputs of different implementations under identical conditions. Let’s analyze the options given:* A. Comparison of the results of a current neural network model ML model implemented in platform A (for example Pytorch) with a similar neural network model ML model implemented in platform B (for example Tensorflow), for the same data.* This option describes a scenario where two different implementations of the same type of model are being compared using the same dataset. This is a typical back-to-back testing situation.* B. Comparison of the results of a home-grown neural network model ML model with results in a neural network model implemented in a standard implementation (for example Pytorch) for the same data.* This option involves comparing a custom implementation with a standard implementation, which is also a typical back-to-back testing scenario to validate the custom model against a known benchmark.* C. Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.* This option involves comparing two different types of models (a neural network and a decision tree). This is not a typical scenario for back-to-back testing because the models are inherently different and would not be expected to produce identical results even on the same data.* D. Comparison of the results of the current neural network ML model on the current data set with a slightly modified data set.* This option involves comparing the outputs of the same model on slightly different datasets. This could be seen as a form of robustness testing or sensitivity analysis, but not typical back-to-back testing as it doesn’t involve comparing multiple implementations.Based on this analysis, optionCis the one that describes a situation of back-to-back testing the least because it compares two fundamentally different models, which is not the intent of back-to-back testing.Q14. Which ONE of the following characteristics is the least likely to cause safety related issues for an Al system?SELECT ONE OPTION  Non-determinism  Robustness  High complexity  Self-learning 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.Q15. Which ONE of the following combinations of Training, Validation, Testing data is used during the process of learning/creating the model?SELECT ONE OPTION  Training data – validation data – test data  Training data – validation data  Training data * test data  Validation data – test data The process of developing a machine learning model typically involves the use of three types of datasets:* Training Data:This is used to train the model, i.e., to learn the patterns and relationships in the data.* Validation Data:This is used to tune the model’s hyperparameters and to prevent overfitting during the training process.* Test Data:This is used to evaluate the final model’s performance and to estimate how it will perform on unseen data.Let’s analyze each option:* A. Training data – validation data – test data* This option correctly includes all three types of datasets used in the process of creating and validating a model. The training data is used for learning, validation data for tuning, and test data for final evaluation.* B. Training data – validation data* This option misses the test data, which is crucial for evaluating the model’s performance on unseen data after the training and validation phases.* C. Training data – test data* This option misses the validation data, which is important for tuning the model and preventing overfitting during training.* D. Validation data – test data* This option misses the training data, which is essential for the initial learning phase of the model.Therefore, the correct answer isAbecause it includes all necessary datasets used during the process of learning and creating the model: training, validation, and test data.Q16. 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?SELECT ONE OPTION  Individual bias at the neuron level, activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.  Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.  Individual bias at the neuron level, and weights assigned to the connections between theneurons.  Individual bias at the neuron level, and activation values of neurons in the previous layer. In a neural network, the activation value of a neuron is determined by a combination of inputs from the previous layer, the weights of the connections, and the bias at the neuron level. Here’s a detailed breakdown:* Inputs for Activation Value:* Activation Values of Neurons in the Previous Layer:These are the outputs from neurons in the preceding layer that serve as inputs to the current neuron.* Weights Assigned to the Connections:Each connection between neurons has an associated weight, which determines the strength and direction of the input signal.* Individual Bias at the Neuron Level:Each neuron has a bias value that adjusts the input sum, allowing the activation function to be shifted.* Calculation:* The activation value is computed by summing the weighted inputs from the previous layer and adding the bias.* Formula: z=(wiai)+bz = sum (w_i cdot a_i) + bz=(wiai)+b, where wiw_iwi are the weights, aia_iai are the activation values from the previous layer, and bbb is the bias.* The activation function (e.g., sigmoid, ReLU) is then applied to this sum to get the final activation value.* Why Option A is Correct:* Option A correctly identifies all components involved in computing the activation value: the individual bias, the activation values of the previous layer, and the weights of the connections.* Eliminating Other Options:* B. Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons: This option misses the bias, which is crucial.* C. Individual bias at the neuron level, and weights assigned to the connections between the neurons: This option misses the activation values from the previous layer.* D. Individual bias at the neuron level, and activation values of neurons in the previous layer:This option misses the weights, which are essential.References:* ISTQB CT-AI Syllabus, Section 6.1, Neural Networks, discusses the components and functioning of neurons in a neural network.* “Neural Network Activation Functions” (ISTQB CT-AI Syllabus, Section 6.1.1).Q17. “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 bebehaving 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. * A. The difficulty of defining criteria for improvement before the model can be accepted.* Defining criteria for improvement is a challenge in the acceptance of AI models, but it is not directly related to the performance drop in real-world scenarios. It relates more to the evaluation and deployment phase rather than affecting the model’s real-time performance post-deployment.* B. The fast pace of change did not allow sufficient time for testing.* This can significantly affect the model’s performance. If the system is self-learning, it needs to adapt quickly, and insufficient testing time can lead to incomplete learning and poor performance.* C. The unknown nature and insufficient specification of the operating environment might have caused the poor performance.* This is highly likely to affect performance. Self-learning AI systems require detailed specifications of the operating environment to adapt and learn effectively. If the environment is insufficiently specified, the model may fail to perform accurately in real-world scenarios.* D. There was an algorithmic bias in the AI system.* Algorithmic bias can significantly impact the performance of AI systems. If the model has biases, it will not perform well across different scenarios and data distributions.Given the context of the self-learning nature and the need for real-time adaptability, optionAis least likely to describe the fall in performance because it deals with acceptance criteria rather than real-time performance issues.Q18. Which ONE of the following is the BEST option to optimize the regression test selection and prevent the regression suite from growing large?SELECT ONE OPTION  Identifying suitable tests by looking at the complexity of the test cases.  Using of a random subset of tests.  Automating test scripts using Al-based test automation tools.  Using an Al-based tool to optimize the regression test suite by analyzing past test results * A. Identifying suitable tests by looking at the complexity of the test cases.* While complexity analysis can help in selecting important test cases, it does not directly address the issue of optimizing the entire regression suite effectively.* B. Using a random subset of tests.* Randomly selecting test cases may miss critical tests and does not ensure an optimized regression suite. This approach lacks a systematic method for ensuring comprehensive coverage.* C. Automating test scripts using AI-based test automation tools.* Automation helps in running tests efficiently but does not inherently optimize the selection of tests to prevent the suite from growing too large.* D. Using an AI-based tool to optimize the regression test suite by analyzing past test results.* This is the most effective approach as AI-based tools can analyze historical test data, identify patterns, and prioritize tests that are more likely to catch defects based onpast results. This method ensures an optimized and manageable regression test suite by focusing on the most impactful test cases.Therefore, the correct answer isDbecause using an AI-based tool to analyze past test results is the best option to optimize regression test selection and manage the size of the regression suite effectively.Q19. Which ONE of the following options describes the LEAST LIKELY usage of Al for detection of GUI changes due to changes in test objects?SELECT ONE OPTION  Using a pixel comparison of the GUI before and after the change to check the differences.  Using a computer vision to compare the GUI before and after the test object changes.  Using a vision-based detection of the GUI layout changes before and after test object changes.  Using a ML-based classifier to flag if changes in GUI are to be flagged for humans. A: Using a pixel comparison of the GUI before and after the change to check the differences.* Pixel comparison is a traditional method and does not involve AI. It compares images at the pixel level, which can be effective but is not an intelligent approach. It is not considered an AI usage and is the least likely usage of AI for detecting GUI changes.B: Using computer vision to compare the GUI before and after the test object changes.* Computer vision involves using AI techniques to interpret and process images. It is a likely usage of AI for detecting changes in the GUI.C: Using vision-based detection of the GUI layout changes before and after test object changes.* Vision-based detection is another AI technique where the layout and structure of the GUI are analyzed to detect changes. This is a typical application of AI.D: Using a ML-based classifier to flag if changes in GUI are to be flagged for humans.* An ML-based classifier can intelligently determine significant changes and decide if they need human review, which is a sophisticated AI application.Q20. Arihant Meditation is a startup using Al to aid people in deeper and better meditation based on analysis of various factors such as time and duration of the meditation, pulse and blood pressure, EEG patters etc. among others. Their model accuracy and other functional performance parameters have not yet reached their desired level.Which ONE of the following factors is NOT a factor affecting the ML functional performance?SELECT ONE OPTION  The data pipeline  The quality of the labeling  Biased data  The number of classes Factors Affecting ML Functional Performance:The data pipeline, quality of the labeling, and biased data are all factors that significantly affect the performance of machine learning models. The number of classes, while relevant for the model structure, is not a direct factor affecting the performance metrics such as accuracy or bias.Reference:ISTQB_CT-AI_Syllabus_v1.0, Sections on Data Quality and its Effect on the ML Model and ML Functional Performance Metrics.Q21. Which ONE of the following options describes a scenario of A/B testing the LEAST?SELECT ONE OPTION  A comparison of two different websites for the same company to observe from a user acceptance perspective.  A comparison of two different offers in a recommendation system to decide on the more effective offer for same users.  A comparison of the performance of an ML system on two different input datasets.  A comparison of the performance of two different ML implementations on the same input data. A/B testing, also known as split testing, is a method used to compare two versions of a product or system to determine which one performs better. It is widely used in web development, marketing, and machine learning to optimize user experiences and model performance. Here’s why option C is the least descriptive of an A/B testing scenario:* Understanding A/B Testing:* In A/B testing, two versions (A and B) of a system or feature are tested against each other. The objective is to measure which version performs better based on predefined metrics such as user engagement, conversion rates, or other performance indicators.* Application in Machine Learning:* In ML systems, A/B testing might involve comparing two different models, algorithms, or system configurations on the same set of data to observe which yields better results.* Why Option C is the Least Descriptive:* Option C describes comparing the performance of an ML system on two different input datasets.This scenario focuses on the input data variation rather than the comparison of system versions or features, which is the essence of A/B testing. A/B testing typically involves a controlled experiment with two versions being tested under the same conditions, not different datasets.* Clarifying the Other Options:* A. A comparison of two different websites for the same company to observe from a user acceptance perspective: This is a classic example of A/B testing where two versions of a website are compared.* B. A comparison of two different offers in a recommendation system to decide on the more effective offer for the same users: This is another example of A/B testing in a recommendation system.* D. A comparison of the performance of two different ML implementations on the same input data: This fits the A/B testing model where two implementations are compared under the same conditions.References:* ISTQB CT-AI Syllabus, Section 9.4, A/B Testing, explains the methodology and application of A/B testing in various contexts.* “Understanding A/B Testing” (ISTQB CT-AI Syllabus).Q22. Which ONE of the following options does NOT describe an Al technology related characteristic which differentiates Al test environments from other test environments?SELECT ONE OPTION  Challenges resulting from low accuracy of the models.  The challenge of mimicking undefined scenarios generated due to self-learning  The challenge of providing explainability to the decisions made by the system.  Challenges in the creation of scenarios of human handover for autonomous systems. AI test environments have several unique characteristics that differentiate them from traditional test environments. Let’s evaluate each option:* A. Challenges resulting from low accuracy of the models.* Low accuracy is a common challenge in AI systems, especially during initial development and training phases. Ensuring the model performs accurately in varied and unpredictable scenarios is a critical aspect of AI testing.* B. The challenge of mimicking undefined scenarios generated due to self-learning.* AI systems, particularly those that involve machine learning, can generate undefined or unexpected scenarios due to their self-learning capabilities. Mimicking and testing these scenarios is a unique challenge in AI environments.* C. The challenge of providing explainability to the decisions made by the system.* Explainability, or the ability to understand and articulate how an AI system arrives at its decisions, is a significant and unique challenge in AI testing. This is crucial for trust and transparency in AI systems.* D. Challenges in the creation of scenarios of human handover for autonomous systems.* While important, the creation of scenarios for human handover in autonomous systems is not a characteristic unique to AI test environments. It is more related to the operational and deployment challenges of autonomous systems rather than the intrinsic technology-related characteristics of AI.Given the above points, optionDis the correct answer because it describes a challenge related to operational deployment rather than a technology-related characteristic unique to AI test environments.Q23. Which ONE of the following tests is LEAST likely to be performed during the ML model testing phase?SELECT ONE OPTION  Testing the accuracy of the classification model.  Testing the API of the service powered by the ML model.  Testing the speed of the training of the model.  Testing the speed of the prediction by the model. The question asks which test is least likely to be performed during the ML model testing phase. Let’s consider each option:* Testing the accuracy of the classification model (A): Accuracy testing is a fundamental part of the ML model testing phase. It ensures that the model correctly classifies the data as intended and meets the required performance metrics.* Testing the API of the service powered by the ML model (B): Testing the API is crucial, especially if the ML model is deployed as part of a service. This ensures that the service integrates well with other systems and that the API performs as expected.* Testing the speed of the training of the model (C): This is least likely to be part of the ML model testing phase. The speed of training is more relevant during the development phase when optimizing and tuning the model. During testing, the focus is more on the model’s performance and behavior rather than how quickly it was trained.* Testing the speed of the prediction by the model (D): Testing the speed of prediction is important to ensure that the model meets performance requirements in a production environment, especially for real-time applications.References:* ISTQB CT-AI Syllabus Section 3.2 on ML Workflow and Section 5 on ML Functional Performance Metrics discuss the focus of testing during the model testing phase, which includes accuracy and prediction speed but not the training speed.Q24. In a certain coffee producing region of Colombia, there have been some severe weather storms, resulting in massive losses in production. This caused a massive drop in stock price of coffee.Which ONE of the following types of testing SHOULD be performed for a machine learning model for stock-price prediction to detect influence of such phenomenon as above on price of coffee stock.SELECT ONE OPTION  Testing for accuracy  Testing for bias  Testing for concept drift  Testing for security Type of Testing for Stock-Price Prediction Models:Concept drift refers to the change in the statistical properties of the target variable over time. Severe weather storms causing massive lossesin coffee production and affecting stock prices would require testing for concept drift to ensure that the model adapts to new patterns in data over time.Reference:ISTQB_CT-AI_Syllabus_v1.0, Section 7.6 Testing for Concept Drift, which explains the need to test for concept drift in models that might be affected by changing external factors. Loading … Prepare Top ISQI CT-AI_v1.0_World Exam Audio Study Guide Practice Questions Edition: https://www.testkingfree.com/ISQI/CT-AI_v1.0_World-practice-exam-dumps.html --------------------------------------------------- Images: https://blog.testkingfree.com/wp-content/plugins/watu/loading.gif https://blog.testkingfree.com/wp-content/plugins/watu/loading.gif --------------------------------------------------- --------------------------------------------------- Post date: 2024-11-27 16:25:56 Post date GMT: 2024-11-27 16:25:56 Post modified date: 2024-11-27 16:25:56 Post modified date GMT: 2024-11-27 16:25:56