完璧なCT-AI試験復習 &合格スムーズCT-AI模試エンジン |一生懸命にCT-AI日本語独学書籍

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ISTQB CT-AI Exam Syllabus Topics:

Section	Weight	Objectives
Neural Networks and Deep Learning	20%	- Model Training and Evaluation Metrics - Testing Deep Learning Models - Neural Network Architecture Basics - Overfitting and Underfitting
Practical Considerations	12%	- Automation in AI Testing - Documentation and Reporting - Test Data Quality and Preparation - Test Environment Setup for AI
Testing AI-Based Systems	20%	- Testing AI-Specific Quality Characteristics - Challenges in Testing AI-Based Systems - Test Levels for AI-Based Systems - Bias and Fairness Considerations
AI Test Methods and Techniques	20%	- Metamorphic Testing - Black-Box Testing for AI Systems - Test Oracle Techniques for AI - Adversarial Testing
Testing AI-Specific Quality Characteristics	20%	- Explainability and Interpretability - Accuracy, Precision, Recall, F1-Score - Robustness Testing - Fairness and Bias Detection
Basics of AI	8%	- Data Concepts (Training, Validation, Test Data) - AI Types and Techniques - Definitions and Terminology - Machine Learning Types (Supervised, Unsupervised, Reinforcement)

>> CT-AI試験復習 <<

CT-AI模試エンジン & CT-AI日本語独学書籍

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ISTQB Certified Tester AI Testing Exam 認定 CT-AI 試験問題 (Q58-Q63):

質問 # 58
A beer company is trying to understand how much recognition its logo has in the market. It plans to do that by monitoring images on various social media platforms using a pre-trained neural network for logo detection. This particular model has been trained by looking for words, as well as matching colors on social media images. The company logo has a big word across the middle with a bold blue and magenta border. Which associated risk is most likely to occur when using this pre-trained model?

A. There is no risk, as the model has already been trained
B. Insufficient function; the model was not trained to check for colors or words
C. Inherited bias: the model could have inherited unknown defects
D. Improper data preparation

正解：C

解説：
According to the syllabus, pre-trained models often inherit biases and limitations from the data and processes used in their original training, which may not align with the new use case.
Specifically, the syllabus states:
"When using a pre-trained model, the training data and process cannot be fully controlled or known by the user of the model. As a result, the model can inherit biases or inaccuracies that were part of its original development and training process."

質問 # 59
Which machine learning approach is most suitable for predicting customer purchase probability?

A. Supervised learning (regression)
B. Reinforcement learning
C. Unsupervised learning
D. Supervised learning (classification)

正解：D

解説：
The ISTQB CT-AI syllabus explains in Section1.6 - Machine Learning Approachesthatsupervised learningis appropriate when labeled data exists and the goal is to predict an output based on known historical examples. Predicting a customer'spurchase probabilityis aclassificationtask when the output corresponds to discrete categories such as"likely to purchase"vs."not likely to purchase."The syllabus gives similar examples in describing classification as the process of assigning instances to predefined classes based on learned patterns in labeled data. Because the retail company wants to determine whether a customer will make a purchase based on marketing actions, classification is the most appropriate choice .

質問 # 60
Which TWO of the below examples of systems BEST describe regression?
I. Predicting the age of a person
II. Predicting if someone is aged over 18
III. Predicting how much fuel is required for a journey
IV. Predicting whether a release will pass all required tests

A. I, II
B. I, IV
C. I, III
D. II, III

正解：C

解説：
Regression is used for predicting continuous values.
I (Predicting the age of a person) and III (Predicting how much fuel is required for a journey) both involve continuous numerical predictions, which are characteristic of regression models.
II (Predicting if someone is aged over 18) and IV (Predicting whether a release will pass all required tests) are classification problems as they involve categorical outcomes (yes/no).

質問 # 61
You are testing an autonomous vehicle which uses AI to determine proper driving actions and responses. You have evaluated the parameters and combinations to be tested and have determined that there are too many to test in the time allowed. It has been suggested that you use pairwise testing to limit the parameters. Given the complexity of the software under test, what is likely the outcome from using pairwise testing?

A. The number of parameters to test can be reduced to less than a dozen.
B. All high priority defects will be identified using this method.
C. While the number of tests needed can be reduced, there may still be a large enough set of tests that automation will be required to execute all of them.
D. Pairwise cannot be applied to this problem because there is AI involved and the evolving values may result in unexpected results that cannot be verified.

正解：C

解説：
The syllabus states that while pairwise testing is effective at finding defects by reducing the number of test cases needed, the resulting test suite can still be extensive and require automation:
"Even the use of pairwise testing can result in extensive test suites... automation and virtual test environments often become necessary to allow the required tests to be run."

質問 # 62
Which ONE of the following would be the MOST effective input to an AI-based defect prediction tool?

A. Developers associated with previous code changes
B. Lines of code changed
C. Cyclomatic complexity
D. Commit size

正解：C

解説：
Cyclomatic complexity would be the most effective input to an AI-based defect prediction tool.
Cyclomatic complexity is a software metric that measures the complexity of a program's control flow, which is closely related to the likelihood of defects. Higher complexity generally indicates a higher probability of defects. This makes it a strong predictor of potential issues in the code, and thus a valuable input for defect prediction.

質問 # 63
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