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This is a printable CT-AI PDF dumps file. The CT-AI PDF dumps enables you to study without any device, as it is a portable and easily shareable format, thus you can study CT-AI dumps on your preferred smart device such as your smartphone or in hard copy format. Once downloaded from the website, you can easily study from the ISTQB CT-AI Exam Questions compiled by our highly experienced professionals as directed by the ISTQB exam syllabus.
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NEW QUESTION # 37
Which statement about testing levels for AI-based systems is correct?
Choose ONE option (1 out of 4)
Answer: A
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 # 38
A software component uses machine learning to recognize the digits from a scan of handwritten numbers. In the scenario above, which type of Machine Learning (ML) is this an example of?
SELECT ONE OPTION
Answer: A
Explanation:
Recognizing digits from a scan of handwritten numbers using machine learning is an example of classification. Here's a breakdown:
* Classification: This type of machine learning involves categorizing input data into predefined classes.
In this scenario, the input data (handwritten digits) are classified into one of the 10 digit classes (0-9).
* Why Not Other Options:
* Reinforcement Learning: This involves learning by interacting with an environment to achieve a goal, which does not fit the problem of recognizing digits.
* Regression: This is used for predicting continuous values, not discrete categories like digit recognition.
* Clustering: This involves grouping similar data points together without predefined classes, which is not the case here.
References:The explanation is based on the definitions of different machine learning types as outlined in the ISTQB CT-AI syllabus, specifically under supervised learning and classification.
NEW QUESTION # 39
Arihant Meditation is a startup using Al to aid people in deeper and better meditation based on analysis of various factors such as time and duration of the meditation, pulse and blood pressure, EEG patters etc. among others. Their model accuracy and other functional performance parameters have not yet reached their desired level.
Which ONE of the following factors is NOT a factor affecting the ML functional performance?
Answer: A
Explanation:
Factors Affecting ML Functional Performance: The data pipeline, quality of the labeling, and biased data are all factors that significantly affect the performance of machine learning models.
The number of classes, while relevant for the model structure, is not a direct factor affecting the performance metrics such as accuracy or bias.
NEW QUESTION # 40
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 # 41
Which ONE of the following models BEST describes a way to model defect prediction by looking at the history of bugs in modules by using code quality metrics of modules of historical versions as input?
SELECT ONE OPTION
Answer: A
Explanation:
Defect prediction models aim to identify parts of the software that are likely to contain defects by analyzing historical data and code quality metrics. The primary goal is to use this predictive information to allocate testing and maintenance resources effectively. Let's break down why option D is the correct choice:
Understanding Classification Models:
Classification models are a type of supervised learning algorithm used to categorize or classify data into predefined classes or labels. In the context of defect prediction, the classification model would classify parts of the code as either "defective" or "non-defective" based on the input features.
Input Data - Code Quality Metrics:
The input data for these classification models typically includes various code quality metrics such as cyclomatic complexity, lines of code, number of methods, depth of inheritance, coupling between objects, etc.
These metrics help the model learn patterns associated with defects.
Historical Data:
Historical versions of the code along with their defect records provide the labeled data needed for training the classification model. By analyzing this historical data, the model can learn which metrics are indicative of defects.
Why Option D is Correct:
Option D specifies using a classification model to predict the presence of defects by using code quality metrics as input data. This accurately describes the process of defect prediction using historical bug data and quality metrics.
Eliminating Other Options:
A). Identifying the relationship between developers and the modules developed by them: This does not directly involve predicting defects based on code quality metrics and historical data.
B). Search of similar code based on natural language processing: While useful for other purposes, this method does not describe defect prediction using classification models and code metrics.
C). Clustering of similar code modules to predict based on similarity: Clustering is an unsupervised learning technique and does not directly align with the supervised learning approach typically used in defect prediction models.
References:
ISTQB CT-AI Syllabus, Section 9.5, Metamorphic Testing (MT), describes various testing techniques including classification models for defect prediction.
"Using AI for Defect Prediction" (ISTQB CT-AI Syllabus, Section 11.5.1).
NEW QUESTION # 42
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