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In this way, the ISTQB CT-AI certified professionals can not only validate their skills and knowledge level but also put their careers on the right track. By doing this you can achieve your career objectives. To avail of all these benefits you need to pass the Certified Tester AI Testing Exam (CT-AI) exam which is a difficult exam that demands firm commitment and complete ISTQB CT-AI exam questions preparation.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Testing AI-Based Systems | 20% | - Challenges in Testing AI-Based Systems - Test Levels for AI-Based Systems - Bias and Fairness Considerations - Testing AI-Specific Quality Characteristics |
| Topic 2: Testing AI-Specific Quality Characteristics | 20% | - Fairness and Bias Detection - Robustness Testing - Accuracy, Precision, Recall, F1-Score - Explainability and Interpretability |
| Topic 3: AI Test Methods and Techniques | 20% | - Black-Box Testing for AI Systems - Test Oracle Techniques for AI - Adversarial Testing - Metamorphic Testing |
| Topic 4: Basics of AI | 8% | - Definitions and Terminology - Data Concepts (Training, Validation, Test Data) - AI Types and Techniques - Machine Learning Types (Supervised, Unsupervised, Reinforcement) |
| Topic 5: Practical Considerations | 12% | - Test Environment Setup for AI - Documentation and Reporting - Test Data Quality and Preparation - Automation in AI Testing |
| Topic 6: Neural Networks and Deep Learning | 20% | - Neural Network Architecture Basics - Model Training and Evaluation Metrics - Overfitting and Underfitting - Testing Deep Learning Models |
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NEW QUESTION # 153
Which ONE of the following options is the MOST APPROPRIATE stage of the ML workflow to set model and algorithm hyperparameters?
SELECT ONE OPTION
Answer: D
Explanation:
Setting model and algorithm hyperparameters is an essential step in the machine learning workflow, primarily occurring during the tuning phase.
Evaluating the model (A): This stage involves assessing the model's performance using metrics and does not typically include the setting of hyperparameters.
Deploying the model (B): Deployment is the stage where the model is put into production and used in real-world applications. Hyperparameters should already be set before this stage.
Tuning the model (C): This is the correct stage where hyperparameters are set. Tuning involves adjusting the hyperparameters to optimize the model's performance.
Data testing (D): Data testing involves ensuring the quality and integrity of the data used for training and testing the model. It does not include setting hyperparameters.
Hence, the most appropriate stage of the ML workflow to set model and algorithm hyperparameters is C. Tuning the model.
Reference:
ISTQB CT-AI Syllabus Section 3.2 on the ML Workflow outlines the different stages of the ML process, including the tuning phase where hyperparameters are set.
Sample Exam Questions document, Question #31 specifically addresses the stage in the ML workflow where hyperparameters are configured.
NEW QUESTION # 154
A local business has a mail pickup/delivery robot for their office. The robot currently uses a track to move between pickup/drop off locations. When it arrives at a destination, the robot stops to allow a human to remove or deposit mail.
The office has decided to upgrade the robot to include AI capabilities that allow the robot to perform its duties without a track, without running into obstacles, and without human intervention.
The test team is creating a list of new and previously established test objectives and acceptance criteria to be used in the testing of the robot upgrade. Which of the following test objectives will test an AI quality characteristic for this system?
Answer: A
Explanation:
In the syllabus, theevolutioncharacteristic for AI-based systems means the ability of the system to evolve and adapt its behavior in response to changes in the environment or in its own performance:
"Evolution is the system's ability to change itself to adapt to new situations, different hardware, or a changing operational environment."
NEW QUESTION # 155
A team of software testers is attempting to create an AI algorithm to assist in software testing. This particular team has gone through over 40 iterations of testing and cannot afford to spend as much time as it takes to run the full regression test suite. They are hoping to have the algorithm reduce the amount of testing required, thus reducing the time needed for each testing cycle.
How can an AI-based tool be expected to assist in this reduction?
Answer: C
Explanation:
The syllabus mentions that AI can help optimize regression test suites:
"An AI-based tool can perform optimization of the regression test suite by analyzing... the information from previous test results, associated defects, and the latest changes that have been made, such as features which are broken more frequently and which tests exercise code impacted by recent changes." (Reference: ISTQB CT-AI Syllabus v1.0, Section 11.4, page 79 of 99)
NEW QUESTION # 156
Which ONE of the following options is an example that BEST describes a system with Al-based autonomous functions?
SELECT ONE OPTION
Answer: C
Explanation:
* AI-Based Autonomous Functions: An AI-based autonomous system is one that can respond to its environment without human intervention. The other options either involve human decisions or do not use AI at all.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Autonomy and Testing Autonomous AI-Based Systems.
NEW QUESTION # 157
You have been developing test automation for an e-commerce system. One of the problems you are seeing is that object recognition in the GUI is having frequent failures. You have determined this is because the developers are changing the identifiers when they make code updates. How could AI help make the automation more reliable?
Answer: B
Explanation:
The syllabus discusses using AI-based tools to reduce GUI test brittleness:
"AI can be used to reduce the brittleness of this approach, by employing AI-based tools to identify the correct objects using various criteria (e.g., XPath, label, id, class, X/Y coordinates), and to choose the historically most stable identification criteria." (Reference: ISTQB CT-AI Syllabus v1.0, Section 11.6.1)
NEW QUESTION # 158
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