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NEW QUESTION # 10
A neural network has been designed and created to assist day-traders improve efficiency when buying and selling commodities in a rapidly changing market. Suppose the test team executes a test on the neural network where each neuron is examined. For this network the shortest path indicates a buy, and it will only occur when the one-day predicted value of the commodity is greater than the spot price by 0.75%. The neurons are stimulated by entering commodity prices and testers verify that they activate only when the future value exceeds the spot price by at least
0.75%. Which of the following statements BEST explains the type of coverage being tested on the neural network?
Answer: B
Explanation:
The syllabus details that threshold coverage requires each neuron to achieve an activation value greater than a specified threshold:
"Threshold coverage: Full threshold coverage requires that each neuron in the neural network achieves an activation value greater than a specified threshold."
NEW QUESTION # 11
You are developing a "flower" ML model... Which of the following describes an objection that you can NEGLECT in your risk assessment?
Choose ONE option (1 out of 4)
Answer: A
Explanation:
The ISTQB CT-AI syllabus explains that reusing pre-trained models is strongly related tosimilarity between the original task and the new task. Section1.8 - Pre-trained Models and Transfer Learningstates that reuse is effective when the new task is similar to the original one, such as adapting a cat-classifier to classify dog breeds. The syllabus warns about risks related toinput differences,data preparation inconsistencies, inherited shortcomings, andexplainability issues. These are legitimate objections (matching options A, B, and C) because large differences in image inputs or patterns can undermine transfer learning; misclassification risk can increase; and explainability often decreases when reusing pre-trained models .
However,output differences are NOT a valid concernhere. Both the leaf-based and flower-based ML models classifythe same plant species, meaning theiroutputs are identical. The syllabus does not identify output mismatch as a transfer-learning risk. Real risks concerninputs,bias inheritance,model transparency, andtraining differences-not output labels. Therefore, OptionDdescribes an objection that can be safely neglected, because output classes are the same and do not hinder reuse.
NEW QUESTION # 12
An engine manufacturing facility wants to apply machine learning to detect faulty bolts. Which of the following would result in bias in the model?
Answer: D
Explanation:
The syllabus defines bias as:
"Bias is the systematic difference in treatment of certain objects, people or groups in comparison to others." It also discusses:
"Sample bias can occur if the data used for training the model does not represent the operational environment, or if some relevant faulty conditions are excluded deliberately." (Reference: ISTQB CT-AI Syllabus v1.0, Section 7.6 and 8.3)
NEW QUESTION # 13
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: C
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 is B. Test the model during model evaluation for data bias.
Reference:
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 # 14
Which ONE of the below statements BEST describes why test environments for autonomous systems might need to be different to other test environments?
Answer: D
Explanation:
The correct answer is A . For autonomous systems, the distinguishing test-environment issue is that the system must respond to changes in its environment without human intervention and must also recognize situations where autonomy should be ceded back to human operators. The CT-AI syllabus states that, for some autonomous systems, identifying and mimicking the circumstances for ceding autonomy may require test environments to push the systems to extremes . It also notes that some autonomous systems operate in hazardous environments, making representative real-world testing difficult or unsafe.
Option B relates more specifically to multi-agent AI systems, where the environment may need non- determinism to mimic other interacting AI-based systems. Option C is a general AI-environment consideration where AI-specific processors may need inclusion. Option D is associated with explainability, where tools may be needed to understand decisions. These are valid AI test-environment factors, but they are not the best autonomy-specific reason. Virtual environments are particularly valuable because dangerous, unusual, and extreme scenarios can be tested safely and repeatedly.
References/topics: CT-AI Syllabus Chapter 10, Sections 10.1 and 10.2 "Test Environments for AI-Based Systems."
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NEW QUESTION # 15
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