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| Section | Weight | Objectives |
|---|---|---|
| Test Levels and Machine Learning Systems | 15% | - Test strategies for AI projects - Testing across ML lifecycle stages - Unit, integration, system, and acceptance testing for ML |
| Model Testing for Machine Learning Systems | 20% | - Drift detection and monitoring - Metamorphic and statistical testing - Adversarial and robustness testing - Model performance and accuracy testing - Explainability and transparency testing |
| Introduction to Testing AI-Based Systems | 10% | - Characteristics of AI and ML systems - AI-specific quality characteristics - Challenges in testing AI-based systems |
| Testing Generative AI and Large Language Models | 15% | - Evaluation of generative AI outputs - Generative AI fundamentals - Specific risks and test approaches for LLMs |
| Input Data Testing for Machine Learning Systems | 20% | - Validation of data pipelines and preprocessing - Data quality attributes - Testing for bias, representativeness, and completeness - Label verification and ground truth assessment |
| Test Approaches and Techniques for AI Systems | 10% | - Exploratory testing and red teaming - A/B testing and back-to-back testing - Test environment and tool considerations - Risk-based testing |
| Machine Learning Development Testing | 10% | - Testing ML development workflows - Testing MLOps and deployment pipelines - Regression testing for retrained models |
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NEW QUESTION # 44
Which statement about automation bias is correct?
Choose ONE option (1 out of 4)
Answer: D
Explanation:
Automation bias is defined in Section4.4 - Human Factors in AI Testingof the ISTQB CT-AI syllabus. It refers to the human tendency to overly trust, rely on, or defer to automated system outputs. The syllabus explains that this bias arises especially indecision-support systems, where humans may accept AI judgments without adequate verification. This aligns directly with Option B.
Option A is incorrect: automation biasdoesinfluence testing, especially when testers rely excessively on AI outputs. The syllabus cautions about testers adopting the same cognitive biases as end users. Option C is incorrect because autonomous systems are not the primary context; rather,systems supporting human decisionsare most impacted. Option D is incorrect because the quality of human inputmatters significantly, and poorly designed user studies can mask or distort automation bias.
Thus,Option Bis the syllabus-accurate description of automation bias.
NEW QUESTION # 45
You have access to the training data that was used to train an AI-based system. You can review this information and use it as a guideline when creating your tests. What type of characteristic is this?
Answer: B
Explanation:
The syllabus states:
"Transparency: This is considered to be the ease with which the algorithm and training data used to generate the model can be determined." Access to the training data is an example of transparency.
(Reference: ISTQB CT-AI Syllabus v1.0, Section 2.7, page 24 of 99)
NEW QUESTION # 46
Which ONE of the following options is a technology used to implement AI?
Answer: D
Explanation:
Genetic algorithms are a technology used in AI, particularly in optimization problems and machine learning models. They are inspired by the process of natural selection and evolve solutions over generations.
NEW QUESTION # 47
A wildlife conservation group would like to use a neural network to classify images of different animals. The algorithm is going to be used on a social media platform to automatically pick out pictures of the chosen animal of the month. This month's animal is set to be a wolf. The test team has already observed that the algorithm could classify a picture of a dog as being a wolf because of the similar characteristics between dogs and wolves. To handle such instances, the team is planning to train the model with additional images of wolves and dogs so that the model is able to better differentiate between the two. What test method should you use to verify that the model has improved after the additional training?
Answer: A
Explanation:
The syllabus defines back-to-back testing as a method to compare a modified AI system to the previous version, which is ideal in this scenario:
"Back-to-back testing is performed by comparing the outputs of two systems that are supposed to provide the same outputs, one being a known and trusted system and the other being the test system. This approach can be used to test ML systems after re-training to verify that improvements have not introduced regressions."
NEW QUESTION # 48
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?
Answer: D
Explanation:
The ML workflow typically involves iterative steps, beginning with data preparation once the model and framework are selected. The syllabus explains:
"The steps shown in Figure 1 (the ML workflow) do not include the integration of the ML model with the non- ML parts of the overall system. Typically, ML models cannot be deployed in isolation and need to be integrated with the non-ML parts... The next step would be data preparation as part of the ML workflow to provide input data to support training by an ML algorithm or prediction by an ML model." (Reference: ISTQB CT-AI Syllabus v1.0, Sections 3.2 & 4.1)
NEW QUESTION # 49
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