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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Testing Generative AI and Large Language Models | 15% | - Generative AI fundamentals - Evaluation of generative AI outputs - Specific risks and test approaches for LLMs |
| Topic 2: Input Data Testing for Machine Learning Systems | 20% | - Data quality attributes - Label verification and ground truth assessment - Testing for bias, representativeness, and completeness - Validation of data pipelines and preprocessing |
| Topic 3: Model Testing for Machine Learning Systems | 20% | - Drift detection and monitoring - Adversarial and robustness testing - Metamorphic and statistical testing - Model performance and accuracy testing - Explainability and transparency testing |
| Topic 4: Introduction to Testing AI-Based Systems | 10% | - Characteristics of AI and ML systems - AI-specific quality characteristics - Challenges in testing AI-based systems |
| Topic 5: Test Levels and Machine Learning Systems | 15% | - Test strategies for AI projects - Unit, integration, system, and acceptance testing for ML - Testing across ML lifecycle stages |
| Topic 6: Test Approaches and Techniques for AI Systems | 10% | - A/B testing and back-to-back testing - Risk-based testing - Test environment and tool considerations - Exploratory testing and red teaming |
| Topic 7: Machine Learning Development Testing | 10% | - Testing ML development workflows - Testing MLOps and deployment pipelines - Regression testing for retrained models |
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31. Frage
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?
Antwort: D
Begründung:
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."
32. Frage
Which ONE of the following would be the LEAST effective input to an AI-based test optimization process?
Antwort: C
Begründung:
Test environment downtime would be the least effective input to an AI-based test optimization process. While downtime may affect the ability to run tests, it does not provide direct insight into the quality of the tests or the effectiveness of the AI model being tested. In contrast, previously failing tests, defect reports, and source control data are much more relevant as they provide information about areas where the system may have issues or require further optimization.
33. Frage
Which statement about testing levels for AI-based systems is correct?
Choose ONE option (1 out of 4)
Antwort: A
Begründung:
Section4.3 - Test Levels for AI Systemsclearly defines ML model testing as the level at which testers evaluate whether an ML model fulfills itsfunctional performance criteria, including accuracy, precision, recall, F1, robustness, stability, and fairness. Therefore, Option C is the correct and syllabus-aligned statement.
Option A is incorrect because input data testing focuses onvalidity and correctness of data entering the model, not interactions with all system components. Option B is incorrect: acceptance testing in the syllabus focuses primarily onbusiness and stakeholder requirements, not specifically explainability. Explainability testing may occur at multiple levels depending on context. Option D is also incorrect because API testing belongs tointegration testing, not system testing, even when AI is consumed as a service.
Thus,Option Cis the only statement that precisely matches syllabus definitions.
34. Frage
The activation value output for a neuron in a neural network is obtained by applying computation to the neuron.
Which ONE of the following options BEST describes the inputs used to compute the activation value?
Antwort: A
35. Frage
The stakeholders of a machine learning model have confirmed that they understand the objective and purpose of the model, and ensured that the proposed model aligns with their business priorities. They have also selected a framework and a machine learning model that they will be using.
What should be the next step to progress along the machine learning workflow?
Antwort: D
Begründung:
Themachine learning (ML) workflowfollows a structured sequence of steps. Once stakeholders have agreed on theobjectives, business priorities, and the framework/model selection, the next logical step is to prepare and pre-process the databefore training the model.
* Data Preparationis crucial becausemachine learning models rely heavily on the quality of input data. Poor data can result in biased, inaccurate, or unreliable models.
* The process involvesdata acquisition, cleaning, transformation, augmentation, and feature engineering.
* Preparing the dataensures it is in the right format, free from errors, and representative of the problem domain, leading to better generalization in training.
* A (Tune the ML Algorithm):Hyperparameter tuning occursafter the model has been trainedand evaluated.
* C (Agree on Acceptance Criteria):Acceptance criteria should already have been defined in theinitial objective-setting phasebefore framework and model selection.
* D (Evaluate the Framework and Model):The selection of the framework and ML model has already been completed. The next step isdata preparation, not reevaluation.
* ISTQB CT-AI Syllabus (Section 3.2: ML Workflow - Data Preparation Phase)
* "Data preparation comprises data acquisition, pre-processing, and feature engineering.
Exploratory data analysis (EDA) may be performed alongside these activities".
* "The data used to train, tune, and test the model must be representative of the operational data that will be used by the model".
Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Since the model selection is complete, thenext step in the ML workflow is to prepare and pre-process the datato ensure it is ready for training and testing. Thus, thecorrect answer is B.
36. Frage
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