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NEW QUESTION # 124
Which two test procedures are BEST suited for CleverPropose system testing?
Choose TWO options (2 out of 5)
Answer: D,E
Explanation:
The ISTQB CT-AI syllabus explains that AI-based decision-support systems benefit strongly fromback-to- back testingandmetamorphic testingwhen oracle problems exist or when limited regression tests are available. In this scenario, CleverPropose replaces an older advisory system.Back-to-back testing(Option A) is ideal because the outputs of the existing conventional system can serve as areference, enabling comparison against the new AI system. This is exactly what the syllabus recommends when AI is replacing a traditional deterministic system.
Metamorphic testing(Option C) is also appropriate, as stated in Section4.6 - Metamorphic Relations. With limited regression tests and complex decision logic, testers can define metamorphic relations such as "if customer income increases, risk rating should not worsen." These relations allow validation even when exact expected outputs are unavailable.
Exploratory data analysis (Option D) is not a system testing technique. Pairwise testing (Option E) is not well suited for complex AI-based financial advice systems. Adversarial testing (Option B) is more relevant for security-critical or robustness evaluation, not primary system testing for advisory tools.
Thus,A and Care the correct and syllabus-supported choices.
NEW QUESTION # 125
A facial recognition system is being deployed at airports in order to scan passengers' faces and compare them to a database of vaccinations, in order to identify unvaccinated passengers in a pandemic. There are a number of components involved including cameras, a model to segment the image, and a model to identify the face and match it against a known photograph. It is important that there are few false negatives, and that passengers cannot subvert the system. Which ONE of the following types of testing is the MOST appropriate options for the tests you would choose in system testing?
Answer: D
Explanation:
The correct answer is B. Adversarial testing . The key risk in this scenario is deliberate subversion of the facial recognition process. The syllabus defines adversarial attacks as situations where an attacker subtly perturbs valid inputs passed to a trained model so that it produces incorrect predictions. It specifically notes that image classifiers can be misled by changes that may be invisible to the human eye, causing the model to classify an image incorrectly with high confidence.
This airport system uses cameras, segmentation, and face-matching models, so adversarial inputs could include manipulated facial images, masks, presentation attacks, lighting manipulation, or other perturbations intended to create false negatives. The syllabus states that adversarial testing performs adversarial attacks to identify vulnerabilities so that preventive measures can be taken. It also identifies adversarial testing as appropriate where mishandling adversarial examples could have significant impact or where the system may be attacked.
References/topics: CT-AI Syllabus Chapter 9, Sections 9.1.1 and 9.7; Chapter 7, Section 7.2.5 "System Testing."
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NEW QUESTION # 126
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: A
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 # 127
Which ONE of the following tests is LEAST likely to be performed during the ML model testing phase?
Answer: B
Explanation:
This is least likely to be part of the ML model testing phase. The speed of training is more relevant during the development phase when optimizing and tuning the model. During testing, the focus is more on the model's performance and behavior rather than how quickly it was trained.
NEW QUESTION # 128
Which ONE of the below is NOT likely to cause a data quality issue affecting a single ML model?
Answer: D
Explanation:
The correct answer is C. Incorrect weights . The CT-AI syllabus lists typical dataset quality issues, including wrong data, incomplete data, mislabeled data, insufficient data, data not pre-processed, obsolete data, unbalanced data, unfair data, duplicate data, irrelevant data, privacy issues, and security issues. It specifically notes that wrong data may arise from a faulty sensor, and that incomplete data may be caused by security issues, hardware issues, or human error. Security issues may also involve fraudulent or misleading data deliberately inserted into training data.
Incorrect weights are different in nature. Weights are parameters learned within certain ML models, especially neural networks; they are part of the trained model, not a characteristic of the dataset itself. Incorrect or poorly optimized weights may produce poor predictions, but they do not constitute a data quality issue in the dataset. By contrast, faulty sensors can create wrong captured data, hardware issues can lead to missing data, and security issues can compromise or poison data.
References/topics: CT-AI Syllabus Chapter 4, Section 4.3 "Dataset Quality Issues"; Section 4.4 "Data Quality and its Effect on the ML Model."
NEW QUESTION # 129
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