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| Section | Weight | Objectives |
|---|---|---|
| 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 |
| Input Data Testing for Machine Learning Systems | 20% | - Data quality attributes - Label verification and ground truth assessment - Validation of data pipelines and preprocessing - Testing for bias, representativeness, and completeness |
| Model Testing for Machine Learning Systems | 20% | - Drift detection and monitoring - Adversarial and robustness testing - Model performance and accuracy testing - Metamorphic and statistical testing - Explainability and transparency testing |
| Testing Generative AI and Large Language Models | 15% | - Generative AI fundamentals - Evaluation of generative AI outputs - Specific risks and test approaches for LLMs |
| Introduction to Testing AI-Based Systems | 10% | - Challenges in testing AI-based systems - Characteristics of AI and ML systems - AI-specific quality characteristics |
| 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 |
>> Advanced CT-AI Testing Engine <<
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NEW QUESTION # 110
You are testing an autonomous vehicle which uses AI to determine proper driving actions and responses. You have evaluated the parameters and combinations to be tested and have determined that there are too many to test in the time allowed. It has been suggested that you use pairwise testing to limit the parameters. Given the complexity of the software under test, what is likely the outcome from using pairwise testing?
Answer: D
Explanation:
The syllabus states that while pairwise testing is effective at finding defects by reducing the number of test cases needed, the resulting test suite can still be extensive and require automation:
"Even the use of pairwise testing can result in extensive test suites... automation and virtual test environments often become necessary to allow the required tests to be run." (Reference: ISTQB CT-AI Syllabus v1.0, Section 9.2, Page 67 of 99)
NEW QUESTION # 111
Which statement about testing levels for AI-based systems is correct?
Choose ONE option (1 out of 4)
Answer: D
Explanation:
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.
NEW QUESTION # 112
A motorcycle engine repair shop owner wants to detect a leaking exhaust valve and fix it before it falls and causes catastrophic damage to the engine. The shop developed and trained a predictive model with historical data files from known health engines and ones which experienced a catastrophic fails due to exhaust valve failure. The shop evaluated 200 engines using this model and then disassembled the engines to assess the true state of the valves, recording the results in the confusion matrix below. What is the precision of this predictive model
Answer: A
Explanation:
The syllabus defines precision as:
"Precision = TP / (TP + FP) * 100%. Precision measures the proportion of positives that were correctly predicted." Using the confusion matrix:
TP = 90
FP = 10Thus: Precision = (90 / (90 + 10)) * 100% = 90 / 100 * 100% = 90%However, the confusion matrix totals suggest that the calculation should be done in the form:Precision = 90 / (90 + 10) * 100% = 90%Since the given answers do not include exactly 90%, the closest approximation and the correct answer, as described in the syllabus, would be 90%.
NEW QUESTION # 113
Which of the following statements about ML functional performance metrics is correct?
Answer: D
Explanation:
The ISTQB CT-AI syllabus explains ML performance metrics in Section3.2 - Evaluating ML Models. Forclustering, which is an unsupervised learning method, the syllabus lists metrics such asintra- cluster distance,inter-cluster distance, and coherence measures. Intra-cluster metrics evaluate how close data points are within a cluster, which directly corresponds to Option A.
NEW QUESTION # 114
Which ONE of the following tests is MOST likely to describe a useful test to help detect different kinds of biases in ML pipeline?
SELECT ONE OPTION
Answer: D
Explanation:
Detecting biases in the ML pipeline involves various tests to ensure fairness and accuracy throughout the ML process.
* Testing the distribution shift in the training data for inappropriate bias (A): This involves checking if there is any shift in the data distribution that could lead to bias in the model. It is an important test but not the most direct method for detecting biases.
* Test the model during model evaluation for data bias (B): This is a critical stage where the model is evaluated to detect any biases in the data it was trained on. It directly addresses potential data biases in the model.
* Testing the data pipeline for any sources for algorithmic bias (C): This test is crucial as it helps identify biases that may originate from the data processing and transformation stages within the pipeline. Detecting sources of algorithmic bias ensures that the model does not inherit biases from these processes.
* Check the input test data for potential sample bias (D): While this is an important step, it focuses more on the input data and less on the overall data pipeline.
Hence, the most likely useful test to help detect different kinds of biases in the ML pipeline isB. Test the model during model evaluation for data bias.
References:
* ISTQB CT-AI Syllabus Section 8.3 on Testing for Algorithmic, Sample, and Inappropriate Bias discusses various tests that can be performed to detect biases at different stages of the ML pipeline.
* Sample Exam Questions document, Question #32 highlights the importance of evaluating the model for biases.
NEW QUESTION # 115
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