Q: Which ONE of the following options is an example that BEST describes a system with Al-based autonomous functions? SELECT ONE OPTION

D Explanation: 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.

Q: ln the near future, technology will have evolved, and Al will be able to learn multiple tasks by itself without needing to be retrained, allowing it to operate even in new environments. The cognitive abilities of Al are similar to a child of 1-2 years.’ In the above quote, which ONE of the following options is the correct name of this type of Al? SELECT ONE OPTION

D Explanation: A. Technological singularity Technological singularity refers to a hypothetical point in the future when AI surpasses human intelligence and can continuously improve itself without human intervention. This scenario involves capabilities far beyond those described in the question. B. Narrow AI Narrow AI, also known as weak AI, is designed to perform a specific task or a narrow range of tasks. It does not have general cognitive abilities and cannot learn multiple tasks by itself without retraining. C. Super AI Super AI refers to an AI that surpasses human intelligence and capabilities across all fields. This is an advanced concept and not aligned with the description of having cognitive abilities similar to a young child. D. General AI General AI, or strong AI, has the ability to understand, learn, and apply knowledge across a wide range of tasks, similar to human cognitive abilities. It aligns with the description of AI that can learn multiple tasks and operate in new environments without needing retraining.

Question 1

Which ONE of the following options describes a scenario of A/B testing the LEAST? SELECT ONE OPTION

Accepted Answer

C

Explanation: 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. Reference: 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).

Question 2

Which of the following is one of the reasons for data mislabelling?

Accepted Answer

A

Explanation: Data mislabeling occurs for several reasons, which can significantly impact the performance of machine learning (ML) models, especially in supervised learning. According to the ISTQB Certified Tester AI Testing (CT-AI) syllabus, mislabeling of data can be caused by the following factors: Random errors by annotators – Mistakes made due to accidental misclassification. Systemic errors – Errors introduced by incorrect labeling instructions or poor training of annotators. Deliberate errors – Errors introduced intentionally by malicious data annotators. Translation errors – Occur when correctly labeled data in one language is incorrectly translated into another language. Subjectivity in labeling – Some labeling tasks require subjective judgment, leading to inconsistencies between different annotators. Lack of domain knowledge – If annotators do not have sufficient expertise in the domain, they may label data incorrectly due to misunderstanding the context. Complex classification tasks – The more complex the task, the higher the probability of labeling mistakes​. Among the answer choices provided, "Lack of domain knowledge" (Option A) is the best answer because expertise is essential to accurately labeling data in complex domains such as medical, legal, or engineering fields. Certified Tester AI Testing Study Guide Reference: ISTQB CT-AI Syllabus v1.0, Section 4.5.2 (Mislabeled Data in Datasets) ISTQB CT-AI Syllabus v1.0, Section 4.3 (Dataset Quality Issues)

Question 3

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

Accepted Answer

D

Explanation: 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 on past results. This method ensures an optimized and manageable regression test suite by focusing on the most impactful test cases. Therefore, the correct answer is D because 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​​.

Question 4

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

Accepted Answer

D

Explanation: 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: Classification models are a type of supervised learning algorithm used to categorize or classify data into predefined classes or labels. In the context of defect prediction, the classification model would classify parts of the code as either "defective" or "non-defective" based on the input features. Input Data - Code Quality Metrics: The input data for these classification models typically includes various code quality metrics such as cyclomatic complexity, lines of code, number of methods, depth of inheritance, coupling between objects, etc. These metrics help the model learn patterns associated with defects. Historical Data: Historical versions of the code along with their defect records provide the labeled data needed for training the classification model. By analyzing this historical data, the model can learn which metrics are indicative of defects. Why Option D is Correct: Option D specifies using a classification model to predict the presence of defects by using code quality metrics as input data. This accurately describes the process of defect prediction using historical bug data and quality metrics. Eliminating Other Options: A . Identifying the relationship between developers and the modules developed by them: This does not directly involve predicting defects based on code quality metrics and historical data. B . Search of similar code based on natural language processing: While useful for other purposes, this method does not describe defect prediction using classification models and code metrics. C . Clustering of similar code modules to predict based on similarity: Clustering is an unsupervised learning technique and does not directly align with the supervised learning approach typically used in defect prediction models. Reference: 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).

Question 5

Which ONE of the following combinations of Training, Validation, Testing data is used during the process of learning/creating the model? SELECT ONE OPTION

Accepted Answer

A

Explanation: 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 is A because it includes all necessary datasets used during the process of learning and creating the model: training, validation, and test data​​.

Question 6

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

Accepted Answer

C

Explanation: 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 is C because clustering is the most suitable method for grouping people with similar tastes for targeted product marketing​​.

Question 7

“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

Accepted Answer

A

Explanation: 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. Reference: 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.

Question 8

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

Accepted Answer

A

Explanation: 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=∑(wi ⋅ ⋅ ai)+bz = \sum (w_i \cdot a_i) + bz=∑(wi​ ai​)+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. Reference: 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).

Question 9

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

Accepted Answer

B

Explanation: The question refers to a problem where data used for an object detection ML system was labelled incorrectly. This issue is most closely related to "accuracy issues." Here's a detailed explanation: Accuracy Issues: The primary goal of labeling data in machine learning is to ensure that the model can accurately learn and make predictions based on the given labels. Incorrectly labeled data directly impacts the model's accuracy, leading to poor performance because the model learns incorrect patterns. Why Not Other Options: Security Issues: This pertains to data breaches or unauthorized access, which is not relevant to the problem of incorrect data labeling. Privacy Issues: This concerns the protection of personal data and is not related to the accuracy of data labeling. Bias Issues: While bias in data can affect model performance, it specifically refers to systematic errors or prejudices in the data rather than outright incorrect labeling. Reference: This explanation is consistent with the concepts covered in the ISTQB CT-AI syllabus under dataset quality issues and their impact on machine learning models​​.

Question 10

You have access to the training data that was used to train an AI-based system. You can review this information and use it as a guideline when creating your tests. What type of characteristic is this?

Accepted Answer

C

Explanation: AI-based systems can sometimes behave like black boxes, where the internal decision-making process is unclear. Transparency refers to the ability to inspect and understand the training data, algorithms, and decision-making process of the AI system. Why is Option C Correct? Transparency ensures that testers and stakeholders can review how an AI system was trained. Access to training data is a key factor in transparency because it allows testers to analyze biases, completeness, and representativeness of the dataset. Transparency is an essential characteristic of explainable AI (XAI). Having access to training data means that testers can investigate how data influences AI behavior. Regulatory and ethical AI guidelines emphasize transparency. Many AI ethics frameworks, such as GDPR and Trustworthy AI guidelines, recommend transparency to ensure fair and explainable AI decision-making. Why Other Options are Incorrect? (A) Autonomy ❌ Autonomy refers to an AI system’s ability to make decisions independently without human intervention. However, having access to training data does not relate to autonomy, which is more about self-learning and decision-making without human control. (B) Explorability ❌ Explorability refers to the ability to test AI systems interactively to understand their behavior, but it does not directly relate to accessing training data. (D) Accessibility ❌ Accessibility refers to the ease with which people can use the system, not the ability to inspect the training data. Reference from ISTQB Certified Tester AI Testing Study Guide Transparency is the ease with which the training data and algorithm used to generate a model can be understood. "Transparency: This is considered to be the ease with which the algorithm and training data used to generate the model can be determined."​ Thus, option C is the correct answer, as transparency involves access to training data, allowing testers to understand AI decision-making processes. Thank you for your visit.

Question 11

The stakeholders of a machine learning model have confirmed that they understand the objective
and purpose of the model, and ensured that the proposed model aligns with their business priorities.
They have also selected a framework and a machine learning model that they will be using.
What should be the next step to progress along the machine learning workflow?

Accepted Answer

A

Explanation: The machine learning (ML) workflow follows a structured sequence of steps. Once stakeholders have agreed on the objectives, business priorities, and the framework/model selection, the next logical step is to prepare and pre-process the data before training the model. Data Preparation is crucial because machine learning models rely heavily on the quality of input data. Poor data can result in biased, inaccurate, or unreliable models​. The process involves data acquisition, cleaning, transformation, augmentation, and feature engineering​. Preparing the data ensures it is in the right format, free from errors, and representative of the problem domain, leading to better generalization in training. Why Other Options Are Incorrect: A (Tune the ML Algorithm): Hyperparameter tuning occurs after the model has been trained and evaluated​. C (Agree on Acceptance Criteria): Acceptance criteria should already have been defined in the initial objective-setting phase before framework and model selection​. D (Evaluate the Framework and Model): The selection of the framework and ML model has already been completed. The next step is data preparation, not reevaluation​. Supporting Reference from ISTQB Certified Tester AI Testing Study Guide: ISTQB CT-AI Syllabus (Section 3.2: ML Workflow - Data Preparation Phase) "Data preparation comprises data acquisition, pre-processing, and feature engineering. Exploratory data analysis (EDA) may be performed alongside these activities"​. "The data used to train, tune, and test the model must be representative of the operational data that will be used by the model"​. Conclusion: Since the model selection is complete, the next step in the ML workflow is to prepare and pre- process the data to ensure it is ready for training and testing. Thus, the correct answer is B.

Question 12

A motorcycle engine repair shop owner wants to detect a leaking exhaust valve and fix it before it
falls and causes catastrophic damage to the engine. The shop developed and trained a predictive
model with historical data files from known health engines and ones which experienced a
catastrophic fails due to exhaust valve failure. The shop evaluated 200 engines using this model and
then disassembled the engines to assess the true state of the valves, recording the results in the
confusion matrix below.
What is the precision of this predictive model

Accepted Answer

D

Explanation: Precision is a performance metric used to evaluate the accuracy of positive predictions in a classification model. It is defined by the formula: Precision=TPTP+FP×100%	ext{Precision} = \frac{TP}{TP + FP} 	imes 100\%Precision=TP+FPTP​×100% Where: TP (True Positives) = Number of correctly predicted positive cases FP (False Positives) = Number of incorrectly predicted positive cases The confusion matrix provided in the question would typically list these values. Based on ISTQB's guidelines for calculating precision, selecting the correct number of true positives and false positives from the given data should yield 94.2% as the precision​. Reference from ISTQB Certified Tester AI Testing Study Guide: Section 5.1 - Confusion Matrix and ML Functional Performance Metrics explains the calculation of precision using the confusion matrix​.

Question 13

Which ONE of the following options is an example that BEST describes a system with Al-based autonomous functions? SELECT ONE OPTION

Accepted Answer

D

Explanation: ​ 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​​​​.

Question 14

ln the near future, technology will have evolved, and Al will be able to learn multiple tasks by itself
without needing to be retrained, allowing it to operate even in new environments. The cognitive
abilities of Al are similar to a child of 1-2 years.’
In the above quote, which ONE of the following options is the correct name of this type of Al?
SELECT ONE OPTION

Accepted Answer

D

Explanation: ​ A. Technological singularity Technological singularity refers to a hypothetical point in the future when AI surpasses human intelligence and can continuously improve itself without human intervention. This scenario involves capabilities far beyond those described in the question. ​ B. Narrow AI Narrow AI, also known as weak AI, is designed to perform a specific task or a narrow range of tasks. It does not have general cognitive abilities and cannot learn multiple tasks by itself without retraining. ​ C. Super AI Super AI refers to an AI that surpasses human intelligence and capabilities across all fields. This is an advanced concept and not aligned with the description of having cognitive abilities similar to a young child. ​ D. General AI General AI, or strong AI, has the ability to understand, learn, and apply knowledge across a wide range of tasks, similar to human cognitive abilities. It aligns with the description of AI that can learn multiple tasks and operate in new environments without needing retraining.

Question 15

A beer company is trying to understand how much recognition its logo has in the market. It plans to
do that by monitoring images on various social media platforms using a pre-trained neural network
for logo detection. This particular model has been trained by looking for words, as well as matching
colors on social media images. The company logo has a big word across the middle with a bold blue
and magenta border.
Which associated risk is most likely to occur when using this pre-trained model?

Accepted Answer

D

Explanation: A major risk when using a pre-trained neural network for logo detection is that it may inherit biases and defects from the original dataset and training process. This means that the model could misidentify or fail to recognize certain logos due to: Differences in data preparation: The original training data may have used a different preprocessing method than the new dataset, leading to inconsistencies. Limited transparency: The exact details of the dataset and biases within it may not be known, which can cause unexpected behavior. Bias in logo detection: If the model was trained on a dataset with certain color or text p

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