CT-AIファンデーション & CT-AI勉強方法

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CT-AI勉強方法、CT-AI勉強資料

CT-AIテストガイドの言語は理解しやすいため、学習障害のない学習者は、学生であろうと現職のスタッフであろうと、初心者であれ、多くの経験豊富な経験豊富なスタッフであれ、年。 CT-AI試験問題は、教育レベルに依存しないすべての分野のすべての人に適用されます。したがって、困難なテストを通過するためにCT-AIガイドトレントを選択して合格することは素晴らしい素晴らしいアイデアです。

ISTQB Certified Tester AI Testing Exam 認定 CT-AI 試験問題 (Q42-Q47):

質問 # 42
In a conference on artificial intelligence (Al), a speaker made the statement, "The current implementation of Al using models which do NOT change by themselves is NOT true Al*. Based on your understanding of Al, is this above statement CORRECT or INCORRECT and why?
SELECT ONE OPTION

A. This statement is incorrect. Current Al is true Al and there is no reason to believe that this fact will change over time.
B. This statement is incorrect. What is considered Al today will continue to be Al even as technology evolves and changes.
C. This statement is correct. In general, today the term Al is utilized incorrectly.
D. This statement is correct. In general, what is considered Al today may change over time.

正解：D

解説：
* A. This statement is incorrect. Current AI is true AI and there is no reason to believe that this fact will change over time.
AI is an evolving field, and the definition of what constitutes AI can change as technology advances.
* B. This statement is correct. In general, what is considered AI today may change over time.
The term AI is dynamic and has evolved over the years. What is considered AI today might be viewed as standard computing in the future. Historically, as technologies become mainstream, they often cease to be considered "AI".
* C. This statement is incorrect. What is considered AI today will continue to be AI even as technology evolves and changes.
This perspective does not account for the historical evolution of the definition of AI . As new technologies emerge, the boundaries of AI shift.
* D. This statement is correct. In general, today the term AI is utilized incorrectly.
While some may argue this, it is not a universal truth. The term AI encompasses a broad range of technologies and applications, and its usage is generally consistent with current technological capabilities.

質問 # 43
Which ONE of the following options describes a scenario of A/B testing the LEAST?
SELECT ONE OPTION

A. A comparison of the performance of two different ML implementations on the same input data.
B. A comparison of two different websites for the same company to observe from a user acceptance perspective.
C. A comparison of two different offers in a recommendation system to decide on the more effective offer for same users.
D. A comparison of the performance of an ML system on two different input datasets.

正解：D

解説：
A/B testing, also known as split testing, is a method used to compare two versions of a product or system to determine which one performs better. It is widely used in web development, marketing, and machine learning to optimize user experiences and model performance. Here's why option C is the least descriptive of an A/B testing scenario:
* Understanding A/B Testing:
* In A/B testing, two versions (A and B) of a system or feature are tested against each other. The objective is to measure which version performs better based on predefined metrics such as user engagement, conversion rates, or other performance indicators.
* Application in Machine Learning:
* In ML systems, A/B testing might involve comparing two different models, algorithms, or system configurations on the same set of data to observe which yields better results.
* Why Option C is the Least Descriptive:
* Option C describes comparing the performance of an ML system on two different input datasets.
This scenario focuses on the input data variation rather than the comparison of system versions or features, which is the essence of A/B testing. A/B testing typically involves a controlled experiment with two versions being tested under the same conditions, not different datasets.
* Clarifying the Other Options:
* A. A comparison of two different websites for the same company to observe from a user acceptance perspective: This is a classic example of A/B testing where two versions of a website are compared.
* B. A comparison of two different offers in a recommendation system to decide on the more effective offer for the same users: This is another example of A/B testing in a recommendation system.
* D. A comparison of the performance of two different ML implementations on the same input data: This fits the A/B testing model where two implementations are compared under the same conditions.
References:
* ISTQB CT-AI Syllabus, Section 9.4, A/B Testing, explains the methodology and application of A/B testing in various contexts.
* "Understanding A/B Testing" (ISTQB CT-AI Syllabus).

質問 # 44
In a certain coffee producing region of Colombia, there have been some severe weather storms, resulting in massive losses in production. This caused a massive drop in stock price of coffee.
Which ONE of the following types of testing SHOULD be performed for a machine learning model for stock-price prediction to detect influence of such phenomenon as above on price of coffee stock.
SELECT ONE OPTION

A. Testing for concept drift
B. Testing for accuracy
C. Testing for security
D. Testing for bias

正解：A

解説：
* Type of Testing for Stock-Price Prediction Models: Concept drift refers to the change in the statistical properties of the target variable over time. Severe weather storms causing massive losses in coffee production and affecting stock prices would require testing for concept drift to ensure that the model adapts to new patterns in data over time.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 7.6 Testing for Concept Drift, which explains the need to test for concept drift in models that might be affected by changing external factors.

質問 # 45
A ML engineer is trying to determine the correctness of the new open-source implementation *X", of a supervised regression algorithm implementation. R-Square is one of the functional performance metrics used to determine the quality of the model.
Which ONE of the following would be an APPROPRIATE strategy to achieve this goal?
SELECT ONE OPTION

A. Add 10% of the rows randomly and create another model and compare the R-Square scores of both the model.
B. Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.
C. Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.
D. Compare the R-Square score of the model obtained using two different implementations that utilize two different programming languages while using the same algorithm and the same training and testing data.

正解：D

解説：
A . Add 10% of the rows randomly and create another model and compare the R-Square scores of both the models.
Adding more data to the training set can affect the R-Square score, but it does not directly verify the correctness of the implementation.
B . Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.
Changing the order of input features should not significantly affect the R-Square score if the implementation is correct, but this approach is more about testing model robustness rather than correctness of the implementation.
C . Compare the R-Square score of the model obtained using two different implementations that utilize two different programming languages while using the same algorithm and the same training and testing data.
This approach directly compares the performance of two implementations of the same algorithm. If both implementations produce similar R-Square scores on the same training and testing data, it suggests that the new implementation "X" is correct.
D . Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.
Dropping data can lead to variations in the R-Square score but does not directly verify the correctness of the implementation.
Therefore, option C is the most appropriate strategy because it directly compares the performance of the new implementation "X" with another implementation using the same algorithm and datasets, which helps in verifying the correctness of the implementation.

質問 # 46
Which ONE of the following activities is MOST relevant when addressing the scenario where you have more than the required amount of data available for the training?
SELECT ONE OPTION

A. Feature selection
B. Data sampling
C. Data augmentation
D. Data labeling

正解：B

解説：
A . Feature selection
Feature selection is the process of selecting the most relevant features from the data. While important, it is not directly about handling excess data.
B . Data sampling
Data sampling involves selecting a representative subset of the data for training. When there is more data than needed, sampling can be used to create a manageable dataset that maintains the statistical properties of the full dataset.
C . Data labeling
Data labeling involves annotating data for supervised learning. It is necessary for training models but does not address the issue of having excess data.
D . Data augmentation
Data augmentation is used to increase the size of the training dataset by creating modified versions of existing data. It is useful when there is insufficient data, not when there is excess data.
Therefore, the correct answer is B because data sampling is the most relevant activity when dealing with an excess amount of data for training.

質問 # 47
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CT-AIファンデーション & CT-AI勉強方法

CT-AI勉強方法、CT-AI勉強資料

ISTQB Certified Tester AI Testing Exam 認定 CT-AI 試験問題 (Q42-Q47):

最新の更新CT-AIファンデーション & 資格試験におけるリーダーオファー & 効率的なCT-AI勉強方法