Free CT-AI: Certified Tester AI Testing Exam Questions and Answers
34 verified practice questions for CT-AI.
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Last updated: September 19, 2026
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Question #1
Which ONE of the following characteristics is the least likely to cause safety related issues for an Al system?
Please select an optionIncorrectCorrect 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.
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Which ONE of the following options BEST DESCRIBES clustering? SELECT ONE OPTION
Please select an optionIncorrectCorrect answer: C
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. • B. Clustering is supervised learning. • C. Clustering is done without prior knowledge of output classes. • D. Clustering requires you to know the classes. Therefore, the correct answer is C because clustering is an unsupervised learning technique done without prior knowledge of output classes.
Was this answer correct?Question #3
Which ONE of the following options is the MOST APPROPRIATE stage of the ML workflow to set model and algorithm hyperparameters? SELECT ONE OPTION
Please select an optionIncorrectCorrect answer: C
Setting model and algorithm hyperparameters is an essential step in the machine learning workflow, primarily occurring during the tuning phase. • Evaluating the model (A): This stage involves assessing the model's performance using metrics and does not typically include the setting of hyperparameters. • Deploying the model (B): Deployment is the stage where the model is put into production and used in real-world applications. Hyperparameters should already be set before this stage. • Tuning the model (C): This is the correct stage where hyperparameters are set. Tuning involves adjusting the hyperparameters to optimize the model's performance. • Data testing (D): Data testing involves ensuring the quality and integrity of the data used for training and testing the model. It does not include setting hyperparameters. Hence, the most appropriate stage of the ML workflow to set model and algorithm hyperparameters is C. Tuning the model. References: • ISTQB CT-AI Syllabus Section 3.2 on the ML Workflow outlines the different stages of the ML process, including the tuning phase where hyperparameters are set. • Sample Exam Questions document, Question #31 specifically addresses the stage in the ML workflow where hyperparameters are configured.
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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
Please select an optionIncorrectCorrect 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).
Was this answer correct?Question #5
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
Please select an optionIncorrectCorrect 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.
Was this answer correct?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
Please select an optionIncorrectCorrect answer: C
• A. Regression • B. Association • C. Clustering • D. Classification Therefore, the correct answer is C because clustering is the most suitable method for grouping people with similar tastes for targeted product marketing.
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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
Please select an optionIncorrectCorrect answer: D
* 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.
Was this answer correct?Question #8
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
Please select an optionIncorrectCorrect answer: B
• A. Feature selection • B. Data sampling • C. Data labeling • D. Data augmentation Therefore, the correct answer is B because data sampling is the most relevant activity when dealing with an excess amount of data for training.
Was this answer correct?Question #9
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
Please select an optionIncorrectCorrect answer: B
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.
Was this answer correct?Question #10
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
Please select an optionIncorrectCorrect answer: A
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: • Calculation: • Why Option A is Correct: • Eliminating Other Options: 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).
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Other ISTQB certifications
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