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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Neural Networks and Deep Learning | 20% | - Model Training and Evaluation Metrics - Neural Network Architecture Basics - Overfitting and Underfitting - Testing Deep Learning Models |
| Topic 2: Practical Considerations | 12% | - Documentation and Reporting - Test Data Quality and Preparation - Automation in AI Testing - Test Environment Setup for AI |
| Topic 3: Testing AI-Specific Quality Characteristics | 20% | - Explainability and Interpretability - Robustness Testing - Accuracy, Precision, Recall, F1-Score - Fairness and Bias Detection |
| Topic 4: Testing AI-Based Systems | 20% | - Challenges in Testing AI-Based Systems - Bias and Fairness Considerations - Testing AI-Specific Quality Characteristics - Test Levels for AI-Based Systems |
| Topic 5: Basics of AI | 8% | - Data Concepts (Training, Validation, Test Data) - Machine Learning Types (Supervised, Unsupervised, Reinforcement) - Definitions and Terminology - AI Types and Techniques |
| Topic 6: AI Test Methods and Techniques | 20% | - Metamorphic Testing - Test Oracle Techniques for AI - Adversarial Testing - Black-Box Testing for AI Systems |
Viele Kandidaten, die sich auf die ISTQB CT-AI Zertifizierungsprüfung vorbereiten, haben auf anderen Websites auch die Online-Ressourcen zur ISTQB CT-AI Zertifizierungsprüfung gesehen. Aber unser EchteFrage ist eine einzige Website, die von den professionellen IT-Experten nach den Nachschlagen bearbeiteten ISTQB CT-AI Prüfungsfragen und Antworten bieten. Wir versprechen, das Sie mit unseren Schulungsunterlagen die ISTQB CT-AI Zertifizierungsprüfung beim ersten Versuch bestehen können.
47. Frage
An engine manufacturing facility wants to apply machine learning to detect faulty bolts. Which of the following would result in bias in the model?
Antwort: B
Begründung:
The syllabus defines bias as:
"Bias is the systematic difference in treatment of certain objects, people or groups in comparison to others." It also discusses:
"Sample bias can occur if the data used for training the model does not represent the operational environment, or if some relevant faulty conditions are excluded deliberately."
48. Frage
Which ONE of the below is NOT an input to an activation function in a neural network?
Antwort: C
Begründung:
The activation values from the current layer are the output of the activation function and are not used as an input. The input to the activation function consists of the bias, activation values from the previous layer, and the weights assigned to the connections.
49. Frage
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?
Antwort: A
Begründung:
Object recognition issues in test automation often arise whendevelopers frequently change object identifiers in the GUI. AI can enhance the stability of GUI automation by:
* Using multiple criteria for object identification
* AI cantrack UI elements using multiple attributessuch asXPath, label, ID, class, and screen coordinatesrather than relying on a single identifier that may change over time.
* This approach makes the automationless brittle and more adaptive to changes in the UI.
* Why other options are incorrect?
* B (Ignore unrecognizable objects to avoid failures): Ignoring objects instead of identifying them properly wouldlead to incomplete or incorrect test execution.
* C (Dynamically name objects and alter source code): AI-based testing tools donot modify application source code; they work byadjusting the recognition strategy.
* D (Anticipate developer changes and pre-alter automation code): While AI can adapt,it does not predict future changes to the GUI, making this option unrealistic.
Thus,Option A is the best answer, as AI tools enhance object recognitionby dynamically selecting the most stable and persistent identification methods, improving test automation reliability.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 11.6.1 (Using AI to Test Through the Graphical User Interface (GUI))
* ISTQB CT-AI Syllabus v1.0, Section 11.6.2 (Using AI to Test the GUI).
50. Frage
Which ONE of the following is a factor associated with the test data that can create challenges specific to testing AI based systems?
Antwort: A
Begründung:
The correct answer is B . The CT-AI syllabus identifies several challenges associated with test data for AI- based systems. One important challenge is that if testers use the same implementation as the data scientists for data acquisition and data pre-processing, defects in those steps may be masked.
Therefore, obtaining test data that has not already been pre-processed by the system under test is important when the purpose is to test the data pipeline and reveal possible defects in acquisition, transformation, cleaning, feature preparation, or formatting. Option A is not the best answer because public benchmark datasets can be useful for comparison or initial evaluation, but using them is not inherently a test- data challenge. Option C may be impractical for high-volume AI data, but it is not the most syllabus-specific risk stated here. Option D may raise privacy or representativeness concerns, but it is less precise than the data- pipeline masking issue. The core testing risk is that pre-processed data can bypass the very processing steps whose quality the test should evaluate.
References/topics: CT-AI Syllabus Chapter 7, Section 7.3 "Test Data for Testing AI-Based Systems"; Section 7.2.1 "Input Data Testing."
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51. Frage
"AllerEgo" is a product that uses sell-learning to predict the behavior of a pilot under combat situation for a variety of terrains and enemy aircraft formations. Post training the model was exposed to the real- world data and the model was found to be behaving poorly. A lot of data quality tests had been performed on the data to bring it into a shape fit for training and testing.
Which ONE of the following options is least likely to describes the possible reason for the fall in the performance, especially when considering the self-learning nature of the Al system?
SELECT ONE OPTION
* The difficulty of defining criteria for improvement before the model can be accepted.
* The fast pace of change did not allow sufficient time for testing.
* The unknown nature and insufficient specification of the operating environment might have caused the poor performance.
* There was an algorithmic bias in the Al system.
Antwort:
Begründung:
* A. The difficulty of defining criteria for improvement before the model can be accepted.
* Defining criteria for improvement is a challenge in the acceptance of AI models, but it is not directly related to the performance drop in real-world scenarios. It relates more to the evaluation and deployment phase rather than affecting the model's real-time performance post-deployment.
* B. The fast pace of change did not allow sufficient time for testing.
* This can significantly affect the model's performance. If the system is self-learning, it needs to adapt quickly, and insufficient testing time can lead to incomplete learning and poor performance.
* C. The unknown nature and insufficient specification of the operating environment might have caused the poor performance.
* This is highly likely to affect performance. Self-learning AI systems require detailed specifications of the operating environment to adapt and learn effectively. If the environment is insufficiently specified, the model may fail to perform accurately in real-world scenarios.
* D. There was an algorithmic bias in the AI system.
* Algorithmic bias can significantly impact the performance of AI systems. If the model has biases, it will not perform well across different scenarios and data distributions.
Given the context of the self-learning nature and the need for real-time adaptability, optionAis least likely to describe the fall in performance because it deals with acceptance criteria rather than real-time performance issues.
52. Frage
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