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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 Mar. 23, 2026 (see below)
Which of the following approaches would help overcome testing challenges associated with probabilistic and non-deterministic AI-based systems?
The syllabus states:
''When testing probabilistic and non-deterministic systems, the same input may produce different outputs. Tests need to be run several times to produce statistically valid test results, ensuring that an appropriate number of answers are accurate.''
(Reference: ISTQB CT-AI Syllabus v1.0, Section 8.4, page 58 of 99)
Which of the following statements regarding experience-based testing for AI-based systems is correct?
Choose ONE option (1 out of 4)
The ISTQB CT-AI syllabus explains inSection 4.4 -- Experience-Based Testing for AI Systemsthat AI-based systems frequently suffer frominsufficient specifications, unpredictable model behavior, andtest oracle problems, especially when outputs depend on probabilistic or learned patterns. The syllabus explicitly states thatexploratory testingis especially valuable in such contexts because it allows testers to investigate the system interactively, observe unexpected behavior, and evaluate system responses that cannot be fully predicted beforehand. Thus, OptionCaccurately reflects the role and justification of exploratory testing for AI systems.
Option A describes data analysis rather than intuitive test design. Option B is incorrect because checklist-based testing does not dynamically adapt test cases; instead, it follows predetermined checklists. Option D incorrectly defines ''tour-based testing''; tours refer to structured exploratory approaches, not biased datasets.
Therefore,Option Cis the syllabus-aligned correct statement.
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.
A mobile app start-up company is implementing an AI-based chat assistant for e-commerce customers. In the process of planning the testing, the team realizes that the specifications are insufficient.
Which testing approach should be used to test this system?
When testing an AI-based chat assistant for e-commerce customers, the lack of sufficient specifications makes it difficult to use structured test techniques. The ISTQB CT-AI Syllabus recommends exploratory testing in such cases:
Why Exploratory Testing?
Exploratory testing is useful when specifications are incomplete or unclear.
AI-based systems, particularly those using natural language processing (NLP), may not behave deterministically, making scripted test cases ineffective.
The tester interacts dynamically with the system, identifying unexpected behaviors not documented in the specification.
Analysis of Answer Choices:
A (Exploratory testing) Correct, as it is the best approach when specifications are incomplete.
B (Static analysis) Incorrect, as static analysis checks code without execution, which is not helpful for AI chatbots.
C (Equivalence partitioning) Incorrect, as this technique requires well-defined inputs and outputs, which are missing due to insufficient specifications.
D (State transition testing) Incorrect, as state-based testing requires knowledge of valid and invalid transitions, which is difficult with a chatbot lacking a clear specification.
Thus, Option A is the correct answer, as exploratory testing is the best approach when dealing with insufficient specifications in AI-based systems.
Certified Tester AI Testing Study Guide Reference:
ISTQB CT-AI Syllabus v1.0, Section 7.7 (Selecting a Test Approach for an ML System)
ISTQB CT-AI Syllabus v1.0, Section 9.6 (Experience-Based Testing of AI-Based Systems).
An airline has created a ML model to project fuel requirements for future flights. The model imports weather data such as wind speeds and temperatures, calculates flight routes based on historical routings from air traffic control, and estimates loads from average passenger and baggage weights. The model performed within an acceptable standard for the airline throughout the summer but as winter set in the load weights became less accurate. After some exploratory data analysis it became apparent that luggage weights were higher in the winter than in summer.
Which of the following statements BEST describes the problem and how it could have been prevented?
The problem described in the question is a classic case of concept drift. Concept drift occurs when the relationship between input variables and the output variable changes over time, leading to a decline in model accuracy.
In this scenario, the average passenger and baggage weights used in the model changed due to seasonal variations, but the model was not updated accordingly. This resulted in inaccurate predictions for fuel requirements in the winter season. This is an example of seasonal drift, where model behavior changes periodically due to recurring trends (e.g., higher luggage weights in winter compared to summer).
To prevent such problems:
The model should be regularly tested for concept drift against agreed ML functional performance criteria.
Exploratory Data Analysis (EDA) should be performed periodically to detect gradual changes in input distributions.
Retraining of the model with updated training data should be done to maintain accuracy.
If drift is detected, mitigation techniques such as incremental learning, retraining with new data, or adjusting model parameters should be employed.
Why Other Options Are Incorrect:
Option B (Easing the performance standard instead of addressing drift): Lowering the performance standard is not a solution; it only masks the problem without fixing it. Instead, regular testing and retraining should be used to handle drift properly.
Option C (Corruption and reloading the model): Model corruption is unrelated to this issue. Corruption refers to accidental or malicious damage to the model or data, whereas this case is due to a changing data environment.
Option D (Lack of transparency): Transparency refers to how understandable the model's decisions are, but the problem here is a change in data distributions, making drift the primary concern.
Supporting Reference from ISTQB Certified Tester AI Testing Study Guide:
ISTQB CT-AI Syllabus (Section 7.6: Testing for Concept Drift)
'The operational environment can change over time without the trained model changing correspondingly. This phenomenon is known as concept drift and typically causes the outputs of the model to become increasingly less accurate and less useful.'
'Systems that may be prone to concept drift should be regularly tested against their agreed ML functional performance criteria to ensure that any occurrences of concept drift are detected soon enough for the problem to be mitigated.'
ISTQB CT-AI Syllabus (Section 7.7: Selecting a Test Approach for an ML System)
'If concept drift is detected, it may be mitigated by retraining the system with up-to-date training data followed by confirmation testing, regression testing, and possibly A/B testing where the updated system must outperform the original system.'
Conclusion:
Since the question describes a situation where seasonal variations affected input data distributions, the correct answer is A: The model suffers from drift and therefore should be regularly tested to ensure that any occurrences of drift are detected soon enough for the problem to be mitigated.
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