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| Section | Weight | Objectives |
|---|---|---|
| Testing AI-Based Systems | 20% | - Testing AI-Specific Quality Characteristics - Test Levels for AI-Based Systems - Bias and Fairness Considerations - Challenges in Testing AI-Based Systems |
| Basics of AI | 8% | - Definitions and Terminology - Machine Learning Types (Supervised, Unsupervised, Reinforcement) - AI Types and Techniques - Data Concepts (Training, Validation, Test Data) |
| Neural Networks and Deep Learning | 20% | - Model Training and Evaluation Metrics - Testing Deep Learning Models - Neural Network Architecture Basics - Overfitting and Underfitting |
| AI Test Methods and Techniques | 20% | - Black-Box Testing for AI Systems - Metamorphic Testing - Adversarial Testing - Test Oracle Techniques for AI |
| Practical Considerations | 12% | - Test Environment Setup for AI - Automation in AI Testing - Test Data Quality and Preparation - Documentation and Reporting |
| Testing AI-Specific Quality Characteristics | 20% | - Fairness and Bias Detection - Explainability and Interpretability - Accuracy, Precision, Recall, F1-Score - Robustness Testing |
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NEW QUESTION # 79
When verifying that an autonomous AI-based system is acting appropriately, which of the following are MOST important to include?
Answer: D
Explanation:
The syllabus highlights that testing for unnecessary human intervention is a key focus for autonomous AI- based systems:
"For autonomous AI-based systems, testers must ensure that the system does not prompt for unnecessary human intervention, as this contradicts the autonomy concept." (Reference: ISTQB CT-AI Syllabus v1.0, Section 8.2, page 59 of 99)
NEW QUESTION # 80
A wildlife conservation group would like to use a neural network to classify images of different animals. The algorithm is going to be used on a social media platform to automatically pick out pictures of the chosen animal of the month. This month's animal is set to be a wolf. The test team has already observed that the algorithm could classify a picture of a dog as being a wolf because of the similar characteristics between dogs and wolves. To handle such instances, the team is planning to train the model with additional images of wolves and dogs so that the model is able to better differentiate between the two. What test method should you use to verify that the model has improved after the additional training?
Answer: B
Explanation:
The syllabus defines back-to-back testing as a method to compare a modified AI system to the previous version, which is ideal in this scenario:
"Back-to-back testing is performed by comparing the outputs of two systems that are supposed to provide the same outputs, one being a known and trusted system and the other being the test system. This approach can be used to test ML systems after re-training to verify that improvements have not introduced regressions."
NEW QUESTION # 81
Which statement about testing to prevent data poisoning and adversarial attacks is correct?
Choose ONE option (1 out of 4)
Answer: A
Explanation:
The ISTQB CT-AI syllabus explains inSection 4.5 - Testing AI-Specific Risksthat adversarial testing is a structured test activity in which testers applyadversarial attacks-crafted or perturbed inputs-to intentionally expose weaknesses in the ML model. The purpose is to identify vulnerabilities that could be exploited throughdata poisoning,evasion attacks, orinput manipulation. OptionCcorrectly reflects this syllabus definition: adversarial testing is aboutusing attacks to locate weaknesses so they can be removed or mitigated.
Option A is incorrect because regression testing does not verify data sourcing policies; it verifies unchanged functionality after modifications. Option B is incorrect because adversarial examples are oftenaddedto training datasets to improverobustness(a practice called adversarial training), not excluded. Option D is incorrect because AIB testing is not described as superior to exploratory data analysis in outlier detection; both have different purposes, and EDA remains essential for data quality assessment.
Thus,Option Cis consistent with syllabus-defined adversarial testing.
NEW QUESTION # 82
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: B
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 # 83
Which statement regarding flexibility and adaptability of AI-based systems is correct?
Answer: C
Explanation:
The ISTQB CT-AI syllabus defines these two concepts clearly inSection 2.1 - Flexibility and Adaptability. Flexibility is described as the ability of a system to operate in situationsnot explicitly covered in its original requirements, while adaptability refers to how easily the system can be modified to handle new environments or conditions. The syllabus stresses that both flexibility and adaptability are crucial, particularly inself-learning AI systemsthat may need to respond to changes in their environment and adjust their behavior accordingly. It states that systems must be capable of determining when and how to adjust behavior in evolving situations, especially when the operational environment is not fully known at deployment time . This directly aligns with Option A.
NEW QUESTION # 84
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