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| Section | Weight | Objectives |
|---|---|---|
| Introduction to Testing AI-Based Systems | 10% | - Challenges in testing AI-based systems - Characteristics of AI and ML systems - AI-specific quality characteristics |
| Model Testing for Machine Learning Systems | 20% | - Model performance and accuracy testing - Drift detection and monitoring - Metamorphic and statistical testing - Explainability and transparency testing - Adversarial and robustness testing |
| Testing Generative AI and Large Language Models | 15% | - Evaluation of generative AI outputs - Generative AI fundamentals - Specific risks and test approaches for LLMs |
| 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 |
| Machine Learning Development Testing | 10% | - Regression testing for retrained models - Testing ML development workflows - Testing MLOps and deployment pipelines |
| Test Approaches and Techniques for AI Systems | 10% | - Risk-based testing - A/B testing and back-to-back testing - Test environment and tool considerations - Exploratory testing and red teaming |
| Input Data Testing for Machine Learning Systems | 20% | - Data quality attributes - Testing for bias, representativeness, and completeness - Validation of data pipelines and preprocessing - Label verification and ground truth assessment |
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NEW QUESTION # 23
Which statement about the property of the test environment for an AI-based system is correct?
Choose ONE option (1 out of 4)
Answer: D
Explanation:
The ISTQB CT-AI syllabus (Section4.3 - Test Environments for AI Systems) describes that, unlike conventional software testing, testing AI systems may requirespecialized toolsfor analyzing and explaining the decisions of ML models. This includes visualization tools, explainability frameworks, and diagnostic utilities to understand why the AI made a certain prediction. Since AI decisions may be non-transparent, the test environment must supportexplainability, making OptionBcorrect.
Option A is incorrect: the syllabus does not state that an autonomous AI system requires an autonomous test environment. Option C is incorrect because test environments mustnot learn; they must remain stable to avoid unpredictable testing conditions. Option D is incorrect because multi-agent systems often involve stochastic interactions, and determinism is neither required nor realistic.
Thus,Option Bis the syllabus-accurate choice.
NEW QUESTION # 24
Which of the following statements about bias in AI based systems is MOST correct?
Answer: C
Explanation:
The correct answer is D . The CT-AI syllabus explains that ML systems make decisions and predictions using algorithms and collected data , and that both can introduce bias into the results. Algorithmic bias can occur when the learning algorithm is incorrectly configured, for example where it overvalues some data compared with other data. Sample bias can occur when training data is not fully representative of the data space to which ML is applied. The syllabus further states that inappropriate bias is often caused by sample bias, but can also be caused by algorithmic bias.
Option A is too narrow because it describes only one algorithmic mechanism. Option B is also incomplete because it describes sample bias only. Option C is incorrect because the syllabus states that bias can be introduced into many types of AI-based systems, not only ML systems involving personal data. Therefore, the most complete and syllabus-aligned statement is that inappropriate bias can arise from either the algorithm or the data.
References/topics: CT-AI Syllabus Chapter 2, Section 2.4 "Bias."
=========
NEW QUESTION # 25
A mobile app start-up company is implementing an AI-based chat assistant for e-commerce customers. In the process of planning the testing, the team realizes that the specifications are insufficient.
Which testing approach should be used to test this system?
Answer: C
NEW QUESTION # 26
A company is using a spam filter to attempt to identify which emails should be marked as spam. Detection rules are created by the filter that causes a message to be classified as spam. An attacker wishes to have all messages internal to the company be classified as spam. So, the attacker sends messages with obvious red flags in the body of the email and modifies the "from" portion of the email to make it appear that the emails have been sent by company members. The testers plan to use exploratory data analysis (EDA) to detect the attack and use this information to prevent future adversarial attacks.
How could EDA be used to detect this attack?
Answer: A
Explanation:
The syllabus explains that EDA can be used to analyze data to identify outliers and unusual patterns, which can indicate adversarial attacks like data poisoning:
"Testing to detect data poisoning is possible using EDA, as poisoned data may show up as outliers." (Reference: ISTQB CT-AI Syllabus v1.0, Section 9.1.2, page 67 of 99)
NEW QUESTION # 27
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: C
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 isC.
Tuning the model.
:
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 # 28
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