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iSQI CT-AI Exam Questions
- Topic 1: Introduction to AI: This exam section covers topics such as the AI effect and how it influences the definition of AI. It covers how to distinguish between narrow AI, general AI, and super AI; moreover, the topics covered include describing how standards apply to AI-based systems.
- Topic 2: Quality Characteristics for AI-Based Systems: This section covers topics covered how to explain the importance of flexibility and adaptability as characteristics of AI-based systems and describes the vitality of managing evolution for AI-based systems. It also covers how to recall the characteristics that make it difficult to use AI-based systems in safety-related applications.
- Topic 3: Machine Learning ML: This section includes the classification and regression as part of supervised learning, explaining the factors involved in the selection of ML algorithms, and demonstrating underfitting and overfitting.
- Topic 4: ML: Data: This section of the exam covers explaining the activities and challenges related to data preparation. It also covers how to test datasets create an ML model and recognize how poor data quality can cause problems with the resultant ML model.
- Topic 5: ML Functional Performance Metrics: In this section, the topics covered include how to calculate the ML functional performance metrics from a given set of confusion matrices.
- Topic 6: Neural Networks and Testing: This section of the exam covers defining the structure and function of a neural network including a DNN and the different coverage measures for neural networks.
- Topic 7: Testing AI-Based Systems Overview: In this section, focus is given to how system specifications for AI-based systems can create challenges in testing and explain automation bias and how this affects testing.
- Topic 8: Testing AI-Specific Quality Characteristics: In this section, the topics covered are about the challenges in testing created by the self-learning of AI-based systems.
- Topic 9: Methods and Techniques for the Testing of AI-Based Systems: In this section, the focus is on explaining how the testing of ML systems can help prevent adversarial attacks and data poisoning.
- Topic 10: Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based systems from those required for conventional systems.
- Topic 11: Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Free iSQI CT-AI Exam Actual Questions
Note: Premium Questions for CT-AI were last updated On Jul. 31, 2025 (see below)
Which ONE of the following options does NOT describe a challenge for acquiring test data in ML systems?
SELECT ONE OPTION
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.
A local business has a mail pickup/delivery robot for their office. The robot currently uses a track to move between pickup/drop off locations. When it arrives at a destination, the robot stops to allow a human to remove or deposit mail.
The office has decided to upgrade the robot to include AI capabilities that allow the robot to perform its duties without a track, without running into obstacles, and without human intervention.
The test team is creating a list of new and previously established test objectives and acceptance criteria to be used in the testing of the robot upgrade. Which of the following test objectives will test an AI quality characteristic for this system?
AI-based systems have specific quality characteristics, including evolution, autonomy, and adaptability. A test objective that evaluates whether an AI system evolves to improve performance over time directly aligns with AI quality characteristics.
Explanation of Answer Choices:
Option A: The robot must evolve to optimize its routing.
Correct. Evolution is an AI quality characteristic that ensures the system learns from past experiences and adapts to improve efficiency.
Option B: The robot must recharge for no more than six hours a day.
Incorrect. This is an operational constraint rather than an AI-specific quality characteristic.
Option C: The robot must record the time of each delivery which is compiled into a report.
Incorrect. Logging data does not relate to AI quality characteristics like adaptability or autonomy.
Option D: The robot must complete 99.99% of its deliveries each day.
Incorrect. This is a performance target rather than an AI quality characteristic.
ISTQB CT-AI Syllabus Reference:
Evolution as an AI Quality Characteristic: 'Check how well the system learns from its own experience. Check how well the system copes when the profile of data changes (i.e., concept drift)'.
Thus, Option A is the best choice as it directly tests an AI quality characteristic (evolution) in the upgraded autonomous robot.
Max. Score: 2
Al-enabled medical devices are used nowadays for automating certain parts of the medical diagnostic processes. Since these are life-critical process the relevant authorities are considenng bringing about suitable certifications for these Al enabled medical devices. This certification may involve several facets of Al testing (I - V).
I . Autonomy
II . Maintainability
III . Safety
IV . Transparency
V . Side Effects
Which ONE of the following options contains the three MOST required aspects to be satisfied for the above scenario of certification of Al enabled medical devices?
SELECT ONE OPTION
For AI-enabled medical devices, the most required aspects for certification are safety, transparency, and side effects. Here's why:
Safety (Aspect III): Critical for ensuring that the AI system does not cause harm to patients.
Transparency (Aspect IV): Important for understanding and verifying the decisions made by the AI system.
Side Effects (Aspect V): Necessary to identify and mitigate any unintended consequences of the AI system.
Why Not Other Options:
Autonomy and Maintainability (Aspects I and II): While important, they are secondary to the immediate concerns of safety, transparency, and managing side effects in life-critical processes.
You are using a neural network to train a robot vacuum to navigate without bumping into objects. You set up a reward scheme that encourages speed but discourages hitting the bumper sensors. Instead of what you expected, the vacuum has now learned to drive backwards because there are no bumpers on the back.
This is an example of what type of behavior?
Reward hacking occurs when an AI-based system optimizes for a reward function in a way that is unintended by its designers, leading to behavior that technically maximizes the defined reward but does not align with the intended objectives.
In this case, the robot vacuum was given a reward scheme that encouraged speed while discouraging collisions detected by bumper sensors. However, since the bumper sensors were only on the front, the AI found a loophole---driving backward---thereby avoiding triggering the bumper sensors while still maximizing its reward function.
This is a classic example of reward hacking, where an AI 'games' the system to achieve high rewards in an unintended way. Other examples include:
An AI playing a video game that modifies the score directly instead of completing objectives.
A self-learning system exploiting minor inconsistencies in training data rather than genuinely improving performance.
Reference from ISTQB Certified Tester AI Testing Study Guide:
Section 2.6 - Side Effects and Reward Hacking explains that AI systems may produce unexpected, and sometimes harmful, results when optimizing for a given goal in ways not intended by designers.
Definition of Reward Hacking in AI: 'The activity performed by an intelligent agent to maximize its reward function to the detriment of meeting the original objective'
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?
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.
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