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CT-AI復習過去問、CT-AI的中率

CT-AIクイズガイドは、毎年の質問の調査と分析を通じて、多くの隠れたルールを調査する価値があることがわかりました。さらに、強力な専門家チームがあるため、ルールを要約して使用できます。 CT-AIトレントの準備は、毎年の質問の分析に基づいて行うことができ、近年の関連知識と組み合わせて、資格試験に関連する一連の重要な結論が結論付けられます。 CT-AIテスト資料は、今年のトピックと提案の傾向を正確に予測する能力を向上させ、CT-AI試験に合格するのに役立ちます。

ISTQB CT-AI 認定試験の出題範囲：

トピック
出題範囲

トピック 1

ML: Data: This section of the exam covers explaining the activities and challenges related to data preparation. It also covers how to test datasets create an ML model and recognize how poor data quality can cause problems with the resultant ML model.

トピック 2

Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based

トピック 3

Testing AI-Based Systems Overview: In this section, focus is given to how system specifications for AI-based systems can create challenges in testing and explain automation bias and how this affects testing.

トピック 4

Methods and Techniques for the Testing of AI-Based Systems: In this section, the focus is on explaining how the testing of ML systems can help prevent adversarial attacks and data poisoning.

トピック 5

ML Functional Performance Metrics: In this section, the topics covered include how to calculate the ML functional performance metrics from a given set of confusion matrices.

トピック 6

Machine Learning ML: This section includes the classification and regression as part of supervised learning, explaining the factors involved in the selection of ML algorithms, and demonstrating underfitting and overfitting.

トピック 7

Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.

トピック 8

systems from those required for conventional systems.

>> CT-AI復習過去問 <<

CT-AI的中率 & CT-AI受験料

ISTQBのCT-AI認定試験は現在で本当に人気がある試験ですね。まだこの試験の認定資格を取っていないあなたも試験を受ける予定があるのでしょうか。確かに、これは困難な試験です。しかし、難しいといっても、高い点数を取って楽に試験に合格できないというわけではないです。では、まだ試験に合格するショートカットがわからないあなたは、受験のテクニックを知りたいですか。今教えてあげますよ。TopexamのCT-AI問題集を利用することです。

ISTQB Certified Tester AI Testing Exam 認定 CT-AI 試験問題 (Q47-Q52):

質問 # 47
Which of the following is an example of overfitting?

A. The model is not able to generalize to accommodate new types of data
B. The model is too simplistic for the data
C. The model is missing relationships between the inputs and outputs
D. The model discards data it considers to be noise or outliers

正解：A

解説：
The syllabus defines overfitting as:
"Overfitting is when the ML model learns the training data so well that it is unable to generalize to accommodate new data." This occurs when the model memorizes the training data, including noise, instead of learning the general patterns.
(Reference: ISTQB CT-AI Syllabus v1.0, Section 3.5.1, page 31 of 99)

質問 # 48
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 an ML system on two different input datasets.
B. A comparison of two different websites for the same company to observe from a user acceptance perspective.
C. A comparison of the performance of two different ML implementations on the same input data.
D. A comparison of two different offers in a recommendation system to decide on the more effective offer for same users.

正解：A

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 # 49
Which of the following approaches would help overcome testing challenges associated with probabilistic and non-deterministic AI-based systems?

A. Run the test several times to generate a statistically valid test result to ensure that an appropriate number of answers are accurate
B. Run the test several times to ensure that the AI always returns the same correct test result
C. Decompose the system test into multiple data ingestion tests to determine if the AI system is getting precise and accurate input data
D. Decompose the system test into multiple data ingestion tests to determine if the AI system is getting a sufficient volume of input data

正解：A

解説：
The syllabus states:
"When testing probabilistic and non-deterministic systems, the same input may produce different outputs.
Tests need to be run several times to produce statistically valid test results, ensuring that an appropriate number of answers are accurate." (Reference: ISTQB CT-AI Syllabus v1.0, Section 8.4, page 58 of 99)

質問 # 50
A startup company has implemented a new facial recognition system for a banking application for mobile devices. The application is intended to learn at run-time on the device to determine if the user should be granted access. It also sends feedback over the Internet to the application developers. The application deployment resulted in continuous restarts of the mobile devices.
Which of the following is the most likely cause of the failure?

A. The feedback requires a physical connection and cannot be sent over the Internet.
B. Mobile operating systems cannot process machine learning algorithms.
C. The size of the application is consuming too much of the phone's storage capacity.
D. The training, processing, and diagnostic generation are too computationally intensive for the mobile device hardware to handle.

正解：D

解説：

Facial recognition applications involvecomplex computational tasks, including:
* Feature Extraction- Identifying unique facial landmarks.
* Model Training and Updates- Continuous learning and adaptation of user data.
* Image Processing- Handling real-time image recognition under various lighting and angles.
In this scenario, themobile device is experiencing continuous restarts, which suggestsa resource overloadcaused by excessive processing demands.
* Mobile devices have limited computational power.
* Unlike servers, mobile devices lack powerful GPUs/TPUs required for deep learning models.
* On-device learning is computationally expensive.
* The model is likely performingreal-time learning, which can overwhelm the CPU and RAM.
* Continuous feedback transmission may cause overheating.
* If the system is running multiple processes-training, inference, and network communication-it can overload system resources and cause crashes.
* (A) The feedback requires a physical connection and cannot be sent over the Internet.#(Incorrect)
* Feedback transmission over the internet is common for cloud-based AI services.This is not the cause of the issue.
* (B) Mobile operating systems cannot process machine learning algorithms.#(Incorrect)
* Many mobile applications use ML models efficiently. The problem here is thehigh computational intensity, not the OS's ability to run ML algorithms.
* (C) The size of the application is consuming too much of the phone's storage capacity.#(Incorrect)
* Storage issues typically result in installation failures or lag,not device restarts.The issue here isprocessing overload, not storage space.
* AI-based applications require significant computational power."The computational intensity of AI- based applications can pose a challenge when deployed on resource-limited devices."
* Edge devices may struggle with processing complex ML workloads."Deploying AI models on mobile or edge devices requires optimization, as these devices have limited processing capabilities compared to cloud environments." Why is Option D Correct?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option D is the correct answer, as thecomputational demands of the facial recognition system are too high for the mobile hardware to handle, causing continuous restarts.

質問 # 51
Which of the following is an example of a clustering problem that can be resolved by unsupervised learning?

A. Grouping individual fish together based on their types of fins
B. Associating shoppers with their shopping tendencies
C. Classifying muffin purchases based on the perceived attractiveness of their packaging
D. Estimating the expected purchase of cat food after a particularly successful ad campaign

正解：B

解説：
The syllabus defines clustering as:
"Clustering: This is when the problem requires the identification of similarities in input data points that allows them to be grouped based on common characteristics or attributes. For example, clustering is used to categorize different types of customers for the purpose of marketing." (Reference: ISTQB CT-AI Syllabus v1.0, Section 3.1.2, page 26 of 99)

質問 # 52
......

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CT-AI的中率: https://www.topexam.jp/CT-AI_shiken.html

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