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ISQI CT-AI_v1.0_World Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: AI System Quality Risks | - Ethical and legal considerations - Model drift and performance degradation |
| Topic 2: AI in Test Automation | - AI-supported test generation - AI-based defect prediction and analytics |
| Topic 3: Machine Learning Fundamentals for Testers | - Supervised and unsupervised learning concepts - Model training and evaluation basics |
| Topic 4: Testing AI-Based Systems | - Bias, fairness, and explainability testing - Testing ML models - Data quality and dataset validation |
| Topic 5: Introduction to AI in Software Testing | - AI systems overview - Role of AI in testing |
ISQI ISTQB Certified Tester AI Testing (v1.0) Sample Questions:
Question 1
"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 bebehaving 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
A. The unknown nature and insufficient specification of the operating environment might have caused the poor performance.
B. There was an algorithmic bias in the Al system.
C. The difficulty of defining criteria for improvement before the model can be accepted.
D. The fast pace of change did not allow sufficient time for testing.
Question 2
Which ONE of the following describes a situation of back-to-back testing the LEAST?
SELECT ONE OPTION
A. Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.
B. Comparison of the results of the current neural network ML model on the current data set with a slightly modified data set.
C. Comparison of the results of a home-grown neural network model ML model with results in a neural network model implemented in a standard implementation (for example Pytorch) for same data
D. Comparison of the results of a current neural network model ML model implemented in platform A (for example Pytorch) with a similar neural network model ML model implemented in platform B (for example Tensorflow), for the same data.
Question 3
The activation value output for a neuron in a neural network is obtained by applying computation to the neuron.
Which ONE of the following options BEST describes the inputs used to compute the activation value?
SELECT ONE OPTION
A. Individual bias at the neuron level, and activation values of neurons in the previous layer.
B. Individual bias at the neuron level, and weights assigned to the connections between theneurons.
C. Individual bias at the neuron level, activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.
D. Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.
Question 4
Which ONE of the following statements is a CORRECT adversarial example in the context of machine learning systems that are working on image classifiers.
SELECT ONE OPTION
A. These examples are model specific and are not likely to cause another model trained on same task to fail.
B. These attack examples cause a model to predict the correct class with slightly less accuracy even though they look like the original image.
C. Black box attacks based on adversarial examples create an exact duplicate model of the original.
D. These attacks can't be prevented by retraining the model with these examples augmented tothe training data.
Solutions:
| Question 1 Answer: C | Question 2 Answer: A | Question 3 Answer: C | Question 4 Answer: A |
