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質問 # 15
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
- A. Using an Al-based tool to optimize the regression test suite by analyzing past test results
- B. Using of a random subset of tests.
- C. Automating test scripts using Al-based test automation tools.
- D. Identifying suitable tests by looking at the complexity of the test cases.
正解：A
解説：
* 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.
質問 # 16
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
- A. The challenge of mimicking undefined scenarios generated due to self-learning
- B. Challenges in the creation of scenarios of human handover for autonomous systems.
- C. Challenges resulting from low accuracy of the models.
- D. The challenge of providing explainability to the decisions made by the system.
正解：B
解説：
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.
質問 # 17
A system was developed for screening the X-rays of patients for potential malignancy detection (skin cancer).
A workflow system has been developed to screen multiple cancers by using several individually trained ML models chained together in the workflow.
Testing the pipeline could involve multiple kind of tests (I - III):
I.Pairwise testing of combinations
II.Testing each individual model for accuracy
III.A/B testing of different sequences of models
Which ONE of the following options contains the kinds of tests that would be MOST APPROPRIATE to include in the strategy for optimal detection?
SELECT ONE OPTION
- A. Only III
- B. I and III
- C. Only II
- D. I and II
正解：D
解説：
The question asks which combination of tests would be most appropriate to include in the strategy for optimal detection in a workflow system using multiple ML models.
* Pairwise testing of combinations (I): This method is useful for testing interactions between different components in the workflow to ensure they work well together, identifying potential issues in the integration.
* Testing each individual model for accuracy (II): Ensuring that each model in the workflow performs accurately on its own is crucial before integrating them into a combined workflow.
* A/B testing of different sequences of models (III): This involves comparing different sequences to determine which configuration yields the best results. While useful, it might not be as fundamental as pairwise and individual accuracy testing in the initial stages.
References:
* ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing and Section 9.3 on Testing ML Models emphasize the importance of testing interactions and individual model accuracy in complex ML workflows.
質問 # 18
Which ONE of the following describes a situation of back-to-back testing the LEAST?
SELECT ONE OPTION
- A. Comparison of the results of the current neural network ML model on the current data set with a slightly modified data set.
- B. Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.
- C. 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
- D. 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.
正解：B
解説：
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.
質問 # 19
"BioSearch" is creating an Al model used for predicting cancer occurrence via examining X-Ray images. The accuracy of the model in isolation has been found to be good. However, the users of the model started complaining of the poor quality of results, especially inability to detect real cancer cases, when put to practice in the diagnosis lab, leading to stopping of the usage of the model.
A testing expert was called in to find the deficiencies in the test planning which led to the above scenario.
Which ONE of the following options would you expect to MOST likely be the reason to be discovered by the test expert?
SELECT ONE OPTION
- A. A lack of focus on choosing the right functional-performance metrics.
- B. A lack of similarity between the training and testing data.
- C. The input data has not been tested for quality prior to use for testing.
- D. A lack of focus on non-functional requirements testing.
正解：B
解説：
The question asks which deficiency is most likely to be discovered by the test expert given the scenario of poor real-world performance despite good isolated accuracy.
* A lack of similarity between the training and testing data (A): This is a common issue in ML where the model performs well on training data but poorly on real-world data due to a lack of representativeness in the training data. This leads to poor generalization to new, unseen data.
* The input data has not been tested for quality prior to use for testing (B): While data quality is important, this option is less likely to be the primary reason for the described issue compared to the representativeness of training data.
* A lack of focus on choosing the right functional-performance metrics (C): Proper metrics are crucial, but the issue described seems more related to the data mismatch rather than metric selection.
* A lack of focus on non-functional requirements testing (D): Non-functional requirements are important, but the scenario specifically mentions issues with detecting real cancer cases, pointing more towards data issues.
References:
* ISTQB CT-AI Syllabus Section 4.2 on Training, Validation, and Test Datasets emphasizes the importance of using representative datasets to ensure the model generalizes well to real-world data.
* Sample Exam Questions document, Question #40 addresses issues related to data representativeness and model generalization.
質問 # 20
Which ONE of the following statements is a CORRECT adversarial example in the context of machine learning systems that are working on image classifiers.
SELECT ONE OPTION
- A. These attacks can't be prevented by retraining the model with these examples augmented tothe training data.
- B. These examples are model specific and are not likely to cause another model trained on same task to fail.
- C. These attack examples cause a model to predict the correct class with slightly less accuracy even though they look like the original image.
- D. Black box attacks based on adversarial examples create an exact duplicate model of the original.
正解：B
解説：
* A. Black box attacks based on adversarial examples create an exact duplicate model of the original.
* Black box attacks do not create an exact duplicate model. Instead, they exploit the model by querying it and using the outputs to craft adversarial examples without knowledge of the internal workings.
* B. These attack examples cause a model to predict the correct class with slightly less accuracy even though they look like the original image.
* Adversarial examples typically cause the model to predict the incorrect class rather than just reducing accuracy. These examples are designed to be visually indistinguishable from the original image but lead to incorrect classifications.
* C. These attacks can't be prevented by retraining the model with these examples augmented to the training data.
* This statement is incorrect because retraining the model with adversarial examples included in the training data can help the model learn to resist such attacks, a technique known as adversarial training.
* D. These examples are model specific and are not likely to cause another model trained on the same task to fail.
* Adversarial examples are often model-specific, meaning that they exploit the specific weaknesses of a particular model. While some adversarial examples might transfer between models, many are tailored to the specific model they were generated for and may not affect other models trained on the same task.
Therefore, the correct answer isDbecause adversarial examples are typically model-specific and may not cause another model trained on the same task to fail.
質問 # 21
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
- A. Different features like ADAS, Lane Change Assistance etc.
- B. Different Road Types
- C. ML model metrics to evaluate the functional performance
- D. Different weather conditions
正解：C
解説：
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.
質問 # 22
Which ONE of the following activities is MOST relevant when addressing the scenario where you have more than the required amount of data available for the training?
SELECT ONE OPTION
- A. Data augmentation
- B. Data sampling
- C. Data labeling
- D. Feature selection
正解：B
解説：
* A. Feature selection
* Feature selection is the process of selecting the most relevant features from the data. While important, it is not directly about handling excess data.
* B. Data sampling
* Data sampling involves selecting a representative subset of the data for training. When there is more data than needed, sampling can be used to create a manageable dataset that maintains the statistical properties of the full dataset.
* C. Data labeling
* Data labeling involves annotating data for supervised learning. It is necessary for training models but does not address the issue of having excess data.
* D. Data augmentation
* Data augmentation is used to increase the size of the training dataset by creating modified versions of existing data. It is useful when there is insufficient data, not when there is excess data.
Therefore, the correct answer isBbecause data sampling is the most relevant activity when dealing with an excess amount of data for training.
質問 # 23
A company producing consumable goods wants to identify groups of people with similar tastes for the purpose of targeting different products for each group. You have to choose and apply an appropriate ML type for this problem.
Which ONE of the following options represents the BEST possible solution for this above-mentioned task?
SELECT ONE OPTION
- A. Clustering
- B. Classification
- C. Regression
- D. Association
正解：A
解説：
* A. Regression
* Regression is used to predict a continuous value and is not suitable for grouping people based on similar tastes.
* B. Association
* Association is used to find relationships between variables in large datasets, often in the form of rules (e.g., market basket analysis). It does not directly group individuals but identifies patterns of co-occurrence.
* C. Clustering
* Clustering is an unsupervised learning method used to group similar data points based on their features. It is ideal for identifying groups of people with similar tastes without prior knowledge of the group labels. This technique will help the company segment its customer base effectively.
* D. Classification
* Classification is a supervised learning method used to categorize data points into predefined classes. It requires labeled data for training, which is not the case here as we want to identify groups without predefined labels.
Therefore, the correct answer isCbecause clustering is the most suitable method for grouping people with similar tastes for targeted product marketing.
質問 # 24
Data used for an object detection ML system was found to have been labelled incorrectly in many cases.
Which ONE of the following options is most likely the reason for this problem?
SELECT ONE OPTION
- A. Security issues
- B. Accuracy issues
- C. Bias issues
- D. Privacy issues
正解：B
質問 # 25
In a conference on artificial intelligence (Al), a speaker made the statement, "The current implementation of Al using models which do NOT change by themselves is NOT true Al*. Based on your understanding of Al, is this above statement CORRECT or INCORRECT and why?
SELECT ONE OPTION
- A. This statement is correct. In general, today the term Al is utilized incorrectly.
- B. This statement is incorrect. Current Al is true Al and there is no reason to believe that this fact will change over time.
- C. This statement is incorrect. What is considered Al today will continue to be Al even as technology evolves and changes.
- D. This statement is correct. In general, what is considered Al today may change over time.
正解：D
解説：
A: This statement is incorrect. Current AI is true AI and there is no reason to believe that this fact will change over time.
* AI is an evolving field, and the definition of what constitutes AI can change as technology advances.
B: This statement is correct. In general, what is considered AI today may change over time.
* The term AI is dynamic and has evolved over the years. What is considered AI today might be viewed as standard computing in the future. Historically, as technologies become mainstream, they often cease to be considered "AI".
C: This statement is incorrect. What is considered AI today will continue to be AI even as technology evolves and changes.
* This perspective does not account for the historical evolution of the definition of AI. As new technologies emerge, the boundaries of AI shift.
D: This statement is correct. In general, today the term AI is utilized incorrectly.
* While some may argue this, it is not a universal truth. The term AI encompasses a broad range of technologies and applications, and its usage is generally consistent with current technological capabilities.
質問 # 26
Which ONE of the following options is an example that BEST describes a system with Al-based autonomous functions?
SELECT ONE OPTION
- A. A fully automated manufacturing plant that uses no software.
- B. A system that utilizes human beings for all important decisions.
- C. A system that utilizes a tool like Selenium.
- D. A system that is fully able to respond to its environment.
正解：D
解説：
AI-Based Autonomous Functions:An AI-based autonomous system is one that can respond to its environment without human intervention. The other options either involve human decisions or do not use AI at all.
Reference:ISTQB_CT-AI_Syllabus_v1.0, Sections on Autonomy and Testing Autonomous AI-Based Systems.
質問 # 27
Which ONE of the following options BEST DESCRIBES clustering?
SELECT ONE OPTION
- A. Clustering is done without prior knowledge of output classes.
- B. Clustering is classification of a continuous quantity.
- C. Clustering is supervised learning.
- D. Clustering requires you to know the classes.
正解：A
解説：
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.
質問 # 28
Which ONE of the following options describes a scenario of A/B testing the LEAST?
SELECT ONE OPTION
- A. A comparison of the performance of two different ML implementations on the same input data.
- B. A comparison of two different websites for the same company to observe from a user acceptance perspective.
- C. A comparison of the performance of an ML system on two different input datasets.
- D. A comparison of two different offers in a recommendation system to decide on the more effective offer for same users.
正解：C
解説：
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).
質問 # 29
Which ONE of the following statements correctly describes the importance of flexibility for Al systems?
SELECT ONE OPTION
- A. Flexible Al systems allow for easier modification of the system as a whole.
- B. Al systems are inherently flexible.
- C. Self-learning systems are expected to deal with new situations without explicitly having to program for it.
- D. Al systems require changing of operational environments; therefore, flexibility is required.
正解：A
解説：
Flexibility in AI systems is crucial for various reasons, particularly because it allows for easier modification and adaptation of the system as a whole.
* AI systems are inherently flexible (A): This statement is not correct. While some AI systems may be designed to be flexible, they are not inherently flexible by nature. Flexibility depends on the system's design and implementation.
* AI systems require changing operational environments; therefore, flexibility is required (B): While it's true that AI systems may need to operate in changing environments, this statement does not directly address the importance of flexibility for the modification of the system.
* Flexible AI systems allow for easier modification of the system as a whole (C): This statement correctly describes the importance of flexibility. Being able to modify AI systems easily is critical for their maintenance, adaptation to new requirements, and improvement.
* Self-learning systems are expected to deal with new situations without explicitly having to program for it (D): This statement relates to the adaptability of self-learning systems rather than their overall flexibility for modification.
Hence, the correct answer isC. Flexible AI systems allow for easier modification of the system as a whole.
References:
* ISTQB CT-AI Syllabus Section 2.1 on Flexibility and Adaptability discusses the importance of flexibility in AI systems and how it enables easier modification and adaptability to new situations.
* Sample Exam Questions document, Question #30 highlights the importance of flexibility in AI systems.
質問 # 30
Which ONE of the following options does NOT describe a challenge for acquiring test data in ML systems?
SELECT ONE OPTION
- A. Nature of data constantly changes with lime.
- B. Test data being sourced from public sources.
- C. Data for the use case is being generated at a fast pace.
- D. Compliance needs require proper care to be taken of input personal data.
正解：C
解説：
Challenges for Acquiring Test Data in ML Systems:Compliance needs, the changing nature of data over time, and sourcing data from public sources are significant challenges. Data being generated quickly is generally not a challenge; it can actually be beneficial as it provides more data for training and testing.
Reference:ISTQB_CT-AI_Syllabus_v1.0, Sections on Data Preparation and Data Quality Issues.
質問 # 31
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