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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Practical Considerations | 12% | - Test Environment Setup for AI - Documentation and Reporting - Test Data Quality and Preparation - Automation in AI Testing |
| Topic 2: Testing AI-Specific Quality Characteristics | 20% | - Robustness Testing - Fairness and Bias Detection - Accuracy, Precision, Recall, F1-Score - Explainability and Interpretability |
| Topic 3: Testing AI-Based Systems | 20% | - Testing AI-Specific Quality Characteristics - Bias and Fairness Considerations - Test Levels for AI-Based Systems - Challenges in Testing AI-Based Systems |
| Topic 4: Basics of AI | 8% | - Machine Learning Types (Supervised, Unsupervised, Reinforcement) - Data Concepts (Training, Validation, Test Data) - AI Types and Techniques - Definitions and Terminology |
| Topic 5: AI Test Methods and Techniques | 20% | - Adversarial Testing - Metamorphic Testing - Test Oracle Techniques for AI - Black-Box Testing for AI Systems |
| Topic 6: Neural Networks and Deep Learning | 20% | - Testing Deep Learning Models - Overfitting and Underfitting - Model Training and Evaluation Metrics - Neural Network Architecture Basics |
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NEW QUESTION # 52
Which ONE of the following characteristics is the least likely to cause safety related issues for an Al system?
Answer: A
Explanation:
Robustness refers to the ability of the system to handle errors, anomalies, and unexpected inputs gracefully. A robust system is less likely to cause safety issues because it can maintain functionality under varied conditions.
NEW QUESTION # 53
A robotic AI based system is being built by a logistics company to operate within its unmanned warehouses.
The warehouses can all be very different and new ones are being added each year. It is expected that the system will not require retraining for each warehouse, and will be able to learn the location of different items and move them to specified locations on request. Which ONE of the following attributes should be MOST carefully considered when specifying the objectives and acceptance criteria for the system?
Answer: B
Explanation:
The correct answer is B. Adaptability . The scenario emphasizes different warehouse layouts, new warehouses being added, and the expectation that the robotic system will operate without retraining for each environment. The CT-AI syllabus defines adaptability as the ease with which a system can be modified for new situations, including different hardware and changing operational environments. It states that flexibility and adaptability are useful where the operational environment is not fully known, the system must cope with new operational environments, the system is expected to adapt to new situations, or it must determine when to change behaviour.
In this logistics context, acceptance criteria should specify how the robot adapts to unfamiliar warehouse structures, how quickly it learns item locations, what resources it may consume while adapting, and whether it still meets functional and non-functional requirements after adaptation. Option A is less central because the example does not involve fairness across protected or stakeholder groups. Option C is always relevant but not the main technical acceptance attribute. Option D may matter, but the dominant requirement is adaptation to changing environments.
References/topics: CT-AI Syllabus Chapter 2, Section 2.1 "Flexibility and Adaptability"; Chapter 8, Section 8.8 "Test Objectives and Acceptance Criteria."
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NEW QUESTION # 54
Which ONE of the following tests is LEAST likely to be performed during the ML model testing phase?
SELECT ONE OPTION
Answer: B
Explanation:
The question asks which test is least likely to be performed during the ML model testing phase. Let's consider each option:
* Testing the accuracy of the classification model (A): Accuracy testing is a fundamental part of the ML model testing phase. It ensures that the model correctly classifies the data as intended and meets the required performance metrics.
* Testing the API of the service powered by the ML model (B): Testing the API is crucial, especially if the ML model is deployed as part of a service. This ensures that the service integrates well with other systems and that the API performs as expected.
* Testing the speed of the training of the model (C): This is least likely to be part of the ML model testing phase. The speed of training is more relevant during the development phase when optimizing and tuning the model. During testing, the focus is more on the model's performance and behavior rather than how quickly it was trained.
* Testing the speed of the prediction by the model (D): Testing the speed of prediction is important to ensure that the model meets performance requirements in a production environment, especially for real- time applications.
References:
* ISTQB CT-AI Syllabus Section 3.2 on ML Workflow and Section 5 on ML Functional Performance Metrics discuss the focus of testing during the model testing phase, which includes accuracy and prediction speed but not the training speed.
NEW QUESTION # 55
Which of the following is an example of overfitting?
Answer: A
Explanation:
Overfitting occurs when a machine learning (ML) model learns patterns that are too specific to the training data, leading to a lack of generalization for new, unseen data. This means the model performs exceptionally well on the training data but poorly on validation or test data because it has memorized the noise and minor details rather than learning the underlying patterns.
NEW QUESTION # 56
You are a test manager planning testing for an invoice financing company. The company buys unpaid invoices from companies and provides them with immediate cash.
The company is replacing their existing conventional system, which takes company accounting records as inputs, with an ML system that classifies each invoice for sales as something that should, or should not be bought. Significant historical production data is available. It is important that invoices are not bought incorrectly.
Which ONE of the following test techniques would be MOST appropriate for you to plan for system testing?
Answer: B
Explanation:
Back-to-back testing is the most appropriate technique in this scenario. It involves comparing the outputs of the new machine learning system with the outputs of the existing conventional system, using the same input data. This approach allows to verify that the ML system performs at least as well as the existing system, particularly in avoiding incorrect purchases of invoices, which is crucial for the company's operations.
NEW QUESTION # 57
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