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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 |
| Testing Generative AI and Large Language Models | 15% | - Generative AI fundamentals - Specific risks and test approaches for LLMs - Evaluation of generative AI outputs |
| Model Testing for Machine Learning Systems | 20% | - Model performance and accuracy testing - Adversarial and robustness testing - Drift detection and monitoring - Explainability and transparency testing - Metamorphic and statistical testing |
| Test Approaches and Techniques for AI Systems | 10% | - Exploratory testing and red teaming - A/B testing and back-to-back testing - Risk-based testing - Test environment and tool considerations |
| Machine Learning Development Testing | 10% | - Regression testing for retrained models - Testing ML development workflows - Testing MLOps and deployment pipelines |
| Input Data Testing for Machine Learning Systems | 20% | - Label verification and ground truth assessment - Data quality attributes - Testing for bias, representativeness, and completeness - Validation of data pipelines and preprocessing |
| Test Levels and Machine Learning Systems | 15% | - Testing across ML lifecycle stages - Unit, integration, system, and acceptance testing for ML - Test strategies for AI projects |
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NEW QUESTION # 47
Which ONE of the following statements about the hardware used to implement ML systems is MOST likely to be correct?
Answer: A
Explanation:
The correct answer is B. Less bits are required for hardware supporting ML . The CT-AI syllabus explains that ML typically benefits from hardware supporting low-precision arithmetic , which uses fewer bits for computation, for example 8 bits instead of 32 bits, and states that this is usually sufficient for ML. It also identifies two other beneficial hardware attributes: the ability to work with large data structures and massively parallel processing.
Option A is too absolute. Specialist hardware may be useful, and AI-specific processors exist, but the syllabus states that a model may run on a low-end smartphone and that cloud training with later deployment to a host device is common. Option C is incorrect because general-purpose CPUs support complex operations that are not typically required for ML applications. Option D is incorrect because higher clock speed alone is not the deciding factor; the syllabus notes that GPUs may outperform CPUs for ML despite CPUs often having faster clock speeds.
References/topics: CT-AI Syllabus Chapter 1, Section 1.6 "Hardware for AI-Based Systems."
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NEW QUESTION # 48
An ML engineer performing supervised learning needs to label images of football games based on the location of the football in the image. Which ONE of the below labelling approaches can be used?
Answer: A
Explanation:
The correct answer is B. Annotation . The CT-AI syllabus explains that data labelling enriches unlabeled or poorly labelled data by adding labels so that it becomes suitable for supervised learning. For image-based supervised learning, the syllabus specifically describes labelling objects in images by drawing rectangles around them as a common labelling technique, known as annotation . It also notes that more complex annotation may be required for 3D objects or irregular bounding boxes, and that data labelling and annotation are typically supported by tools.
This directly matches the football-game scenario: the ML engineer must identify the location of the football in each image, which requires object localization rather than merely assigning the whole image to a class.
Option A, "internal," describes who performs labelling within an organization, not the image-labelling technique itself. Option C, augmentation, creates additional data points from an existing dataset, such as transformed images, but it does not identify the football's location. Option D, benchmarking, is unrelated to labelling and is used for comparative evaluation.
References/topics: CT-AI Syllabus Chapter 4, Section 4.5 "Data Labelling for Supervised Learning"; Appendix B "Annotation."
NEW QUESTION # 49
Which of the following is one of the reasons for data mislabelling?
Answer: A
Explanation:
The syllabus lists multiple reasons for mislabelled data, including the lack of domain knowledge:
"Lack of required domain knowledge may lead to incorrect labelling."
NEW QUESTION # 50
You are a test manager planning testing for an invoice financing company. The company buy unpaid invoices from companies and provide them with immediate cash. The company is replacing their existing conventional system, which takes company accounting records as inputs, with an ML system that classifies each invoice for sales as something that should, or should not be bought. Significant historical production data is available. It is important that invoices are not bought incorrectly. Which ONE of the following test techniques would be MOST appropriate for you to plan for system testing?
Answer: A
Explanation:
The correct answer is B. Back-to-back testing . The scenario contains the key conditions for this technique:
an existing conventional system with similar functionality, a new ML-based replacement, and significant historical production data. The CT-AI syllabus explains that back-to-back testing uses an alternative version of the system as a pseudo-oracle , comparing its outputs with those produced by the system under test. The pseudo-oracle may be an existing system or even conventional non-AI software.
This is stronger than A/B testing because the objective is not primarily to compare two production variants statistically to determine which is better; it is to detect classification defects in a replacement system. It is also stronger than metamorphic testing because an existing oracle-like comparator is available, reducing the need to rely only on relative output relations. Experience-based testing is useful as a supplement, but it would not be the primary systematic technique where historical production inputs and an existing system are available.
References/topics: CT-AI Syllabus Chapter 9, Section 9.3 "Back-to-Back Testing"; Section 9.7
"Selecting Test Techniques for AI-Based Systems."
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NEW QUESTION # 51
Which of the following options is an example of the concept of overfitting?
Answer: C
Explanation:
The ISTQB CT-AI syllabus defines overfitting in Section3.2 - ML Model Evaluationas a condition where an ML model learns the training data too precisely--including noise and irrelevant detail-- resulting in poor performance on unseen data. Overfitting is characterized byhigh accuracy on training data but low accuracy on validation or real-world data. Option A perfectly matches this definition: a model trained only on one university's student data generalizes poorly to students from other universities.
This is a textbook example of overfitting because the model has essentially memorized patterns unique to a narrow dataset, instead of learning generalizable relationships applicable across environments .
NEW QUESTION # 52
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