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| Section | Weight | Objectives |
|---|---|---|
| Model Testing for Machine Learning Systems | 20% | - Metamorphic and statistical testing - Model performance and accuracy testing - Adversarial and robustness testing - Drift detection and monitoring - Explainability and transparency testing |
| Introduction to Testing AI-Based Systems | 10% | - Challenges in testing AI-based systems - Characteristics of AI and ML systems - AI-specific quality characteristics |
| 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 |
| Test Approaches and Techniques for AI Systems | 10% | - Test environment and tool considerations - A/B testing and back-to-back testing - Risk-based testing - Exploratory testing and red teaming |
| 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 |
| Machine Learning Development Testing | 10% | - Testing ML development workflows - Testing MLOps and deployment pipelines - Regression testing for retrained models |
| Testing Generative AI and Large Language Models | 15% | - Generative AI fundamentals - Specific risks and test approaches for LLMs - Evaluation of generative AI outputs |
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NEW QUESTION # 103
Which ONE of the following models BEST describes a way to model defect prediction by looking at the history of bugs in modules by using code quality metrics of modules of historical versions as input?
SELECT ONE OPTION
Answer: A
Explanation:
Defect prediction models aim to identify parts of the software that are likely to contain defects by analyzing historical data and code quality metrics. The primary goal is to use this predictive information to allocate testing and maintenance resources effectively. Let's break down why option D is the correct choice:
Understanding Classification Models:
Classification models are a type of supervised learning algorithm used to categorize or classify data into predefined classes or labels. In the context of defect prediction, the classification model would classify parts of the code as either "defective" or "non-defective" based on the input features.
Input Data - Code Quality Metrics:
The input data for these classification models typically includes various code quality metrics such as cyclomatic complexity, lines of code, number of methods, depth of inheritance, coupling between objects, etc.
These metrics help the model learn patterns associated with defects.
Historical Data:
Historical versions of the code along with their defect records provide the labeled data needed for training the classification model. By analyzing this historical data, the model can learn which metrics are indicative of defects.
Why Option D is Correct:
Option D specifies using a classification model to predict the presence of defects by using code quality metrics as input data. This accurately describes the process of defect prediction using historical bug data and quality metrics.
Eliminating Other Options:
A). Identifying the relationship between developers and the modules developed by them: This does not directly involve predicting defects based on code quality metrics and historical data.
B). Search of similar code based on natural language processing: While useful for other purposes, this method does not describe defect prediction using classification models and code metrics.
C). Clustering of similar code modules to predict based on similarity: Clustering is an unsupervised learning technique and does not directly align with the supervised learning approach typically used in defect prediction models.
References:
ISTQB CT-AI Syllabus, Section 9.5, Metamorphic Testing (MT), describes various testing techniques including classification models for defect prediction.
"Using AI for Defect Prediction" (ISTQB CT-AI Syllabus, Section 11.5.1).
NEW QUESTION # 104
A system was developed for screening the X-rays of patients for potential malignancy detection (skin cancer). A workflow system has been developed to screen multiple cancers by using several individually trained ML models chained together in the workflow.
Testing the pipeline could involve multiple kind of tests (I - III):
I . Pairwise testing of combinations
II . Testing each individual model for accuracy
III . A/B testing of different sequences of models
Which ONE of the following options contains the kinds of tests that would be MOST APPROPRIATE to include in the strategy for optimal detection?
SELECT ONE OPTION
Answer: D
Explanation:
The question asks which combination of tests would be most appropriate to include in the strategy for optimal detection in a workflow system using multiple ML models.
Pairwise testing of combinations (I): This method is useful for testing interactions between different components in the workflow to ensure they work well together, identifying potential issues in the integration.
Testing each individual model for accuracy (II): Ensuring that each model in the workflow performs accurately on its own is crucial before integrating them into a combined workflow.
A/B testing of different sequences of models (III): This involves comparing different sequences to determine which configuration yields the best results. While useful, it might not be as fundamental as pairwise and individual accuracy testing in the initial stages.
Reference:
ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing and Section 9.3 on Testing ML Models emphasize the importance of testing interactions and individual model accuracy in complex ML workflows.
NEW QUESTION # 105
Which of the following are the three activities in the data acquisition activities for data preparation?
Answer: B
Explanation:
The syllabus defines data acquisition as consisting of three steps:
"Data acquisition: The activity of acquiring data relevant to the business problem to be solved by an ML model, typically involving the activities of identifying, gathering and labelling data." (Reference: ISTQB CT-AI Syllabus v1.0, Section 4.1, page 33 of 99)
NEW QUESTION # 106
An e-commerce developer built an application for automatic classification of online products in order to allow customers to select products faster. The goal is to provide more relevant products to the user based on prior purchases. Which of the following factors is necessary for a supervised machine learning algorithm to be successful?
Answer: D
Explanation:
The syllabus explains that supervised learning requires correctly labeled data so the algorithm can learn the relationship between input features and output labels:
"In supervised learning, the algorithm creates the ML model from labeled data during the training phase. The labeled data is used to infer the relationship between the input data and output labels."
NEW QUESTION # 107
Which ONE of the following statements about a system MOST describe an autonomous system?
Answer: A
Explanation:
The correct answer is A because it best matches the CT-AI concept of autonomy: the system can operate independently for a prolonged period, processing loan applications within predefined constraints until a human changes the available aggregate credit. The syllabus defines autonomy as the ability of a system to work independently of human oversight and control for prolonged periods, and emphasizes that testers should identify how long the system is expected to perform satisfactorily without human intervention and when control should be returned to humans.
Option B describes a safety or driver-monitoring function rather than sustained independent operation; the system reacts when the driver is unresponsive but still assumes human supervision. Option C describes evolution or self-learning, because the chatbot improves responses based on prior interactions, but self- learning alone does not make the system autonomous. Option D is explicitly human-in-the-loop: the fraud system only alerts operators and requires human input for the final decision, so it is less autonomous.
References/topics: CT-AI Syllabus Chapter 2, Section 2.2 "Autonomy"; Appendix B "Autonomous system" and "Autonomy."
NEW QUESTION # 108
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