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| Section | Weight | Objectives |
|---|---|---|
| Basics of AI | 8% | - Definitions and Terminology - Data Concepts (Training, Validation, Test Data) - Machine Learning Types (Supervised, Unsupervised, Reinforcement) - AI Types and Techniques |
| Practical Considerations | 12% | - Automation in AI Testing - Test Environment Setup for AI - Test Data Quality and Preparation - Documentation and Reporting |
| Testing AI-Specific Quality Characteristics | 20% | - Accuracy, Precision, Recall, F1-Score - Fairness and Bias Detection - Robustness Testing - Explainability and Interpretability |
| AI Test Methods and Techniques | 20% | - Test Oracle Techniques for AI - Metamorphic Testing - Black-Box Testing for AI Systems - Adversarial Testing |
| Neural Networks and Deep Learning | 20% | - Overfitting and Underfitting - Neural Network Architecture Basics - Testing Deep Learning Models - Model Training and Evaluation Metrics |
| Testing AI-Based Systems | 20% | - Testing AI-Specific Quality Characteristics - Bias and Fairness Considerations - Challenges in Testing AI-Based Systems - Test Levels for AI-Based Systems |
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NEW QUESTION # 28
A tourist calls an airline to book a ticket and is connected with an automated system which is able to recognize speech, understand requests related to purchasing a ticket, and provide relevant travel options.
When the tourist asks about the expected weather at the destination or potential impacts on operations because of the tight labor market, the only response from the automated system is, "I don't understand your question." This AI system should be categorized as?
Answer: C
Explanation:
According to the syllabus,conventional AIsystems are limited to specific, pre-defined tasks and do not have generalized intelligence:
"Conventional AI systems are limited in their scope and typically only perform specific tasks within the domain for which they have been designed. They do not exhibit general AI behavior." (Reference: ISTQB CT-AI Syllabus v1.0, Section 1.2)
NEW QUESTION # 29
Before deployment of an AI based system, a developer is expected to demonstrate in a test environment how decisions are made. Which of the following characteristics does decision making fall under?
Answer: B
Explanation:
The syllabus definesexplainabilityas the ability to understand how the AI-based system comes up with a particular result:
"Explainability is considered to be the ease with which users can determine how the AI-based system comes up with a particular result."
NEW QUESTION # 30
A test engineer is planning the best functional performance metrics to evaluate an unsupervised learning model. The model groups data points based on their similarity. The test engineer wants to measure how similar the data points in each group actually are.
Which is the MOST likely metric they should use?
Answer: A
Explanation:
The most appropriate metric for evaluating the similarity of data points within each group in an unsupervised learning model is intra-cluster. This metric measures how similar the data points within each cluster are to one another. The goal is to have high intra-cluster similarity, meaning the data points within a group should be similar to each other.
NEW QUESTION # 31
Consider a natural language processing (NLP) algorithm that attempts to predict the next word that you would like to type in a text message. An update to the algorithm has been created that should increase the accuracy of the predictions based on user typing patterns. The old algorithm was rated for accuracy by the users. Then, after the new update was released, the users rated the updated algorithm. A statistical test was used to compare between the two versions of the algorithm to see whether or not the update should remain in place.
This is an example of what type of testing?
Answer: B
Explanation:
A/B testing is a statistical testing method that compares two different versions of a system to determine which one performs better. In this scenario, theold NLP algorithmwas rated for accuracy, and after the update, the new algorithmwas also rated by users. A statistical test was performed to compare the two versions, which is the fundamental approach ofA/B testing.
A/B testing is commonly used in:
* User experience testing(e.g., comparing different versions of a website).
* ML model evaluation(e.g., comparing two AI-based classifiers).
* Performance assessment(e.g., determining if a new recommendation algorithm is more effective).
This approach allows for data-driven decisions, ensuring that any changes to the system result in meaningful improvements.
* Section 9.4 - A/B Testingstates that A/B testing is used to compare updates in AI-based systems to determine if the newer version is better.
Reference from ISTQB Certified Tester AI Testing Study Guide:
NEW QUESTION # 32
Which ONE of the following statements is a CORRECT adversarial example in the context of machine learning systems that are working on image classifiers?
Answer: C
Explanation:
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.
NEW QUESTION # 33
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