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| Section | Weight | Objectives |
|---|---|---|
| Input Data Testing for Machine Learning Systems | 20% | - Validation of data pipelines and preprocessing - Data quality attributes - Label verification and ground truth assessment - Testing for bias, representativeness, and completeness |
| 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 |
| Introduction to Testing AI-Based Systems | 10% | - AI-specific quality characteristics - Challenges in testing AI-based systems - Characteristics of AI and ML systems |
| Testing Generative AI and Large Language Models | 15% | - Generative AI fundamentals - Specific risks and test approaches for LLMs - Evaluation of generative AI outputs |
| Test Levels and Machine Learning Systems | 15% | - Unit, integration, system, and acceptance testing for ML - Test strategies for AI projects - Testing across ML lifecycle stages |
| Model Testing for Machine Learning Systems | 20% | - Model performance and accuracy testing - Adversarial and robustness testing - Explainability and transparency testing - Drift detection and monitoring - Metamorphic and statistical testing |
| Machine Learning Development Testing | 10% | - Regression testing for retrained models - Testing MLOps and deployment pipelines - Testing ML development workflows |
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NEW QUESTION # 80
The training of an ML model... What type of bias is LEAST important to look for when testing the model?
Choose ONE option (1 out of 4)
Answer: C
Explanation:
The ISTQB CT-AI syllabus distinguishes between several types of bias relevant in AI testing, including sample bias,algorithmic bias, andinappropriate bias. In Section3.3 - Bias in AI-Based Systems, the syllabus stresses the importance of identifying biases that originate fromtraining data,model development, anddecision logic. Sample bias occurs when the training data does not adequately represent the population; algorithmic bias arises when the model produces systematically skewed results due to learned patterns; inappropriate bias involves ethically or socially problematic distortions in the outcomes. All three of these bias types directly affect theoutputs of the AI modeland are therefore highly relevant when testing an industrial inspection system intended to reliably detect defects. These biases can lead to defective items being missed or false alarms being raised, which impacts quality assurance significantly .
Automation bias, however, is fundamentally different. It refers to ahuman cognitive bias, where users (e.g., inspectors) overly trust or rely on the AI system's output. While important in user-interaction testing, it isnota biaswithin the ML model itself. Since the question asks which bias isleast important when testing the model, automation bias can be legitimately deprioritized duringmodel-level testing. Therefore, OptionBis correct.
NEW QUESTION # 81
Which challenge to testing self-learning systems puts you at risk of a data attack?
Answer: C
Explanation:
The ISTQB CT-AI syllabus describes thatself-learning systems continuously adjust their behaviorduring operation as new data arrives. Section4.1 - Challenges of Testing AI-Based Systemshighlights that such systems are vulnerable todata attacks, particularly through adversarial inputs, poisoning, or malicious drift. The risk arises because unexpected changes in the input distribution may alter the learned model in harmful ways. Option D - Unexpected changes corresponds directly to this syllabus-defined risk.
NEW QUESTION # 82
"AllerEgo" is a product that uses sell-learning to predict the behavior of a pilot under combat situation for a variety of terrains and enemy aircraft formations. Post training the model was exposed to the real- world data and the model was found to be behaving poorly. A lot of data quality tests had been performed on the data to bring it into a shape fit for training and testing.
Which ONE of the following options is least likely to describes the possible reason for the fall in the performance, especially when considering the self-learning nature of the Al system?
SELECT ONE OPTION
The difficulty of defining criteria for improvement before the model can be accepted.
The fast pace of change did not allow sufficient time for testing.
The unknown nature and insufficient specification of the operating environment might have caused the poor performance.
There was an algorithmic bias in the Al system.
Answer: C
Explanation:
Given the context of the self-learning nature and the need for real-time adaptability, option A is least likely to describe the fall in performance because it deals with acceptance criteria rather than real-time performance issues.
NEW QUESTION # 83
You are a test manager planning testing for an invoice financing company. The company buys unpaid invoices from companies and provides 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: B
Explanation:
Back-to-back testing is the most appropriate technique in this scenario. It involves comparing the outputs of the new machine learning system with the outputs of the existing conventional system, using the same input data. This approach allows to verify that the ML system performs at least as well as the existing system, particularly in avoiding incorrect purchases of invoices, which is crucial for the company's operations.
NEW QUESTION # 84
While measuring the test coverage of a neural network, a test engineer wants to measure the number of neurons that have each output two activation function results with a minimum difference between the two results of 0.5.
Which ONE of the below coverage measures would achieve that goal?
Answer: C
Explanation:
Value-change coverage is a coverage measure that focuses on tracking the changes in the values of a neuron's output. In this case, the test engineer is interested in measuring the number of neurons that exhibit a difference of 0.5 between two activation function results, which is effectively a value change. This coverage measure ensures that the neuron undergoes a significant change in its output, fulfilling the engineer's goal.
NEW QUESTION # 85
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