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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Test Methods and Techniques | 20% | - Black-Box Testing for AI Systems - Adversarial Testing - Test Oracle Techniques for AI - Metamorphic Testing |
| Topic 2: Practical Considerations | 12% | - Test Environment Setup for AI - Test Data Quality and Preparation - Documentation and Reporting - Automation in AI Testing |
| Topic 3: Testing AI-Based Systems | 20% | - Challenges in Testing AI-Based Systems - Testing AI-Specific Quality Characteristics - Bias and Fairness Considerations - Test Levels for AI-Based Systems |
| Topic 4: Neural Networks and Deep Learning | 20% | - Model Training and Evaluation Metrics - Overfitting and Underfitting - Testing Deep Learning Models - Neural Network Architecture Basics |
| Topic 5: Basics of AI | 8% | - Data Concepts (Training, Validation, Test Data) - Definitions and Terminology - AI Types and Techniques - Machine Learning Types (Supervised, Unsupervised, Reinforcement) |
| Topic 6: Testing AI-Specific Quality Characteristics | 20% | - Fairness and Bias Detection - Robustness Testing - Explainability and Interpretability - Accuracy, Precision, Recall, F1-Score |
Therefore, make the most of this opportunity of getting these superb exam questions for the ISTQB CT-AI certification exam. We guarantee you that our top-rated Certified Tester AI Testing Exam practice exam (PDF, desktop practice test software, and web-based practice exam) will enable you to pass the ISTQB CT-AI Certification Exam on the very first go.
NEW QUESTION # 36
A car insurance company is using a new AI service to reward defensive driving behavior among its policyholders. The driving behavior is recorded in a rating number (score).
The AI service determines this score from the following input values:
Reference speed v_max in km/h
Average speed v_mean in km/h
Average acceleration a_pos in m/s2
Average braking deceleration a_neg in m/s2
The more defensive the driving behavior is (slow driving, low acceleration, low braking deceleration), the higher is the score.
Three initial test cases (Test 1 to Test 3) are used for testing the AI service. In addition, new test cases A-D are proposed.
Which of the new tests is NOT a follow-up test case for metamorphic testing?
Answer: B
Explanation:
According to the ISTQB CT-AI syllabus,metamorphic testingworks by applyingmetamorphic relations (MRs): predictable input transformations that should lead to predictable output changes.
From the initial test data, clear relations emerge for defensive driving scoring. The score increases when:
v_mean decreases,
a_pos decreases,
a_neg becomes less negative,and decreases when the opposite occurs.
A valid metamorphic follow-up test must modify inputs in a direction consistent with at least one MR while keeping the expected output direction predictable.
Test Alowers v_mean compared to Test 1, with similar acceleration values. This directly satisfies the MR that lower speed -> higher score.
Test Cincreases both acceleration and braking intensity compared to Test 2, making the reduced score range (30-70) consistent with more aggressive driving.
Test Dmodifies acceleration and braking magnitudes in ways consistent with Test 3's defensive- driving scoring boundaries.
Test B, however, changes multiple variables in contradictory directions:
v_mean increases (worse)
a_pos increases (worse)
a_neg becomes less negative (better)
Because these changes conflict,the expected score trend becomes unpredictable, violating the premise of a metamorphic follow-up test.
Thus Test B cannot be considered a metamorphic follow-up, which makes Option C correct.
NEW QUESTION # 37
Which of the following statements about reinforcement learning is correct?
Choose ONE option (1 out of 4)
Answer: B
Explanation:
Section1.6.3 - Reinforcement Learningof the ISTQB CT-AI syllabus states that reinforcement learning (RL) is based on anagent interacting with an environment, performing actions, and receivingrewards or penalties. The core concept is thereward function, which guides the agent's learning process. The syllabus emphasizes that training in RL isdriven by rewards, and the agent aims to maximize cumulative reward over time. Therefore, OptionCdirectly reflects the correct description: the agent learns by being rewarded for successful actions .
Option A is incorrect because RL doesnotuse labeled data; that applies to supervised learning. Option B contradicts the syllabus definition: RL fundamentallyrequiresinteraction with the environment. Option D is incorrect because the reward function isdefined by humans, not learned by the agent; the agent learns apolicy
, not the reward function itself.
Thus, OptionCis the only statement consistent with RL as defined in the syllabus.
NEW QUESTION # 38
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?
Answer: D
Explanation:
According to the syllabus, pre-trained models often inherit biases and limitations from the data and processes used in their original training, which may not align with the new use case.
Specifically, the syllabus states:
"When using a pre-trained model, the training data and process cannot be fully controlled or known by the user of the model. As a result, the model can inherit biases or inaccuracies that were part of its original development and training process."
NEW QUESTION # 39
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
Answer: D
Explanation:
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 is B because data sampling is the most relevant activity when dealing with an excess amount of data for training.
NEW QUESTION # 40
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
Answer: D
Explanation:
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 is D because adversarial examples are typically model-specific and may not cause another model trained on the same task to fail.
NEW QUESTION # 41
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