Ray Cole Ray Cole's Profile Page

Ray Cole Ray Cole

0 Course Enrolled • 0 Course Completed

Biography

CT-AI資格関連題、CT-AI勉強方法

P.S.JapancertがGoogle Driveで共有している無料の2025 ISTQB CT-AIダンプ：https://drive.google.com/open?id=1lk2DPQZjJG-HiN9nd66aJwH2nouxoqEa

CT-AI認定試験は専門知識と情報技術を検査する試験で、Japancertが一日早くISTQBのCT-AI認定試験「Certified Tester AI Testing Exam」に合格させるのサイトで試験の前に弊社が提供する訓練練習問題をテストして、短い時間であなたの収穫が大きいです。

業種別の人々は自分が将来何か成績を作るようにずっと努力しています。IT業種で勤めているあなたもきっとずっと努力して自分の技能を向上させているでしょう。では、最近最も人気があるISTQBのCT-AI認定試験の認証資格を既に取りましたか。CT-AI試験に対して、あなたはいくらぐらい分かっていますか。もしこの試験に関連する知識が非常に不足であると同時にこの試験に合格したい場合、あなたはどうするつもりですか。そうですか。どうするか全然分からないですか。そうしても焦らないでください。Japancertはあなたに援助を提供します。

>> CT-AI資格関連題 <<

試験の準備方法-信頼的なCT-AI資格関連題試験-更新するCT-AI勉強方法

何事でもはじめが一番難しいです。ISTQBのCT-AI試験への復習に悩んでいますか。弊社の試験のためのソフトを買うのはあなたの必要の第一歩です。弊社の提供したのはあなたがほしいのだけではなく、試験のためにあなたの必要があるのです。あなたは決められないかもしれませんが、ISTQBのCT-AIのデモをダウンロードしてください。やってみて第一歩を進める勇気があります。

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

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 3

Neural Networks and Testing: This section of the exam covers defining the structure and function of a neural network including a DNN and the different coverage measures for neural networks.

トピック 4

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 5

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 6

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

トピック 7

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

ISTQB Certified Tester AI Testing Exam 認定 CT-AI 試験問題 (Q38-Q43):

質問 # 38
Which of the following are the three activities in the data acquisition activities for data preparation?

A. Building, approving, deploying
B. Identifying, gathering, labelling
C. Cleaning, transforming, augmenting
D. Feature selecting, feature growing, feature augmenting

正解：B

解説：
The syllabus defines data acquisition as consisting of three steps:
"Data acquisition: The activity of acquiring data relevant to the business problem to be solved by an ML model, typically involving the activities of identifying, gathering and labelling data." (Reference: ISTQB CT-AI Syllabus v1.0, Section 4.1, page 33 of 99)

質問 # 39
Which ONE of the following options describes the LEAST LIKELY usage of Al for detection of GUI changes due to changes in test objects?
SELECT ONE OPTION

A. Using a computer vision to compare the GUI before and after the test object changes.
B. Using a ML-based classifier to flag if changes in GUI are to be flagged for humans.
C. Using a vision-based detection of the GUI layout changes before and after test object changes.
D. Using a pixel comparison of the GUI before and after the change to check the differences.

正解：D

解説：
* A. Using a pixel comparison of the GUI before and after the change to check the differences.
Pixel comparison is a traditional method and does not involve AI . It compares images at the pixel level, which can be effective but is not an intelligent approach. It is not considered an AI usage and is the least likely usage of AI for detecting GUI changes.
* B. Using computer vision to compare the GUI before and after the test object changes.
Computer vision involves using AI techniques to interpret and process images. It is a likely usage of AI for detecting changes in the GUI .
* C. Using vision-based detection of the GUI layout changes before and after test object changes.
Vision-based detection is another AI technique where the layout and structure of the GUI are analyzed to detect changes. This is a typical application of AI .
* D. Using a ML-based classifier to flag if changes in GUI are to be flagged for humans.
An ML-based classifier can intelligently determine significant changes and decide if they need human review, which is a sophisticated AI application.

質問 # 40
"AllerEgo" is a product that uses sell-learning to predict the behavior of a pilot under combat situation for a variety of terrains and enemy aircraft formations. Post training the model was exposed to the real- world data and the model was found to be behaving poorly. A lot of data quality tests had been performed on the data to bring it into a shape fit for training and testing.
Which ONE of the following options is least likely to describes the possible reason for the fall in the performance, especially when considering the self-learning nature of the Al system?
SELECT ONE OPTION
* The difficulty of defining criteria for improvement before the model can be accepted.
* The fast pace of change did not allow sufficient time for testing.
* The unknown nature and insufficient specification of the operating environment might have caused the poor performance.
* There was an algorithmic bias in the Al system.

正解：

解説：
* A. The difficulty of defining criteria for improvement before the model can be accepted.
* Defining criteria for improvement is a challenge in the acceptance of AI models, but it is not directly related to the performance drop in real-world scenarios. It relates more to the evaluation and deployment phase rather than affecting the model's real-time performance post-deployment.
* B. The fast pace of change did not allow sufficient time for testing.
* This can significantly affect the model's performance. If the system is self-learning, it needs to adapt quickly, and insufficient testing time can lead to incomplete learning and poor performance.
* C. The unknown nature and insufficient specification of the operating environment might have caused the poor performance.
* This is highly likely to affect performance. Self-learning AI systems require detailed specifications of the operating environment to adapt and learn effectively. If the environment is insufficiently specified, the model may fail to perform accurately in real-world scenarios.
* D. There was an algorithmic bias in the AI system.
* Algorithmic bias can significantly impact the performance of AI systems. If the model has biases, it will not perform well across different scenarios and data distributions.
Given the context of the self-learning nature and the need for real-time adaptability, optionAis least likely to describe the fall in performance because it deals with acceptance criteria rather than real-time performance issues.

質問 # 41
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. Mobile operating systems cannot process machine learning algorithms.
B. The feedback requires a physical connection and cannot be sent over the Internet.
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 # 42
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 determinedthat 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. All high priority defects will be identified using this method.
B. 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.
C. 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.
D. The number of parameters to test can be reduced to less than a dozen.

正解：B

解説：
Pairwise testing is a combinatorial testing technique that reduces the number of test cases by focusing on testing interactions between pairs of parameters rather than all possible combinations. It is widely used in AI- based systems, including autonomous vehicles, where the number of possible input parameter combinations can be extremely high.
* Option A:"The number of parameters to test can be reduced to less than a dozen."
* This is incorrect. While pairwise testing significantly reduces the number of test cases, it does not necessarily limit them to a fixed number like a dozen. The final number of tests depends on the number of parameters and their possible values.
* Option B:"All high priority defects will be identified using this method."
* This is incorrect. While pairwise testing is effective in detecting defects caused by interactions between two parameters, it may not uncover defects resulting from more complex interactions involving three or more parameters.
* Option 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."
* This is the correct answer. Even though pairwise testing reduces the number of test cases, AI- based systems such as autonomous vehicles still have a large number of test scenarios. Therefore, automation is often necessary to execute all test cases within the available time.
* Option 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."
* This is incorrect. Pairwise testing can still be applied to AI-based systems, including those that evolve over time. However, additional testing techniques may be required to verify evolving behavior.
* Pairwise Testing for AI Systems:"Pairwise testing is widely used because it effectively reduces the number of test cases while maintaining defect detection capability".
* Automation Requirement:"In practice, even with pairwise testing, extensive test suites may still require automation".
Analysis of the Answer Options:ISTQB CT-AI Syllabus References:

質問 # 43
......

ISTQBのCT-AIのオンラインサービスのスタディガイドを買いたかったら、Japancertを買うのを薦めています。Japancertは同じ作用がある多くのサイトでリーダーとしているサイトで、最も良い品質と最新のトレーニング資料を提供しています。弊社が提供したすべての勉強資料と他のトレーニング資料はコスト効率の良い製品で、サイトが一年間の無料更新サービスを提供します。ですから、弊社のトレーニング製品はあなたが試験に合格することを助けにならなかったら、全額で返金することを保証します。

CT-AI勉強方法: https://www.japancert.com/CT-AI.html

BONUS！！！ Japancert CT-AIダンプの一部を無料でダウンロード：https://drive.google.com/open?id=1lk2DPQZjJG-HiN9nd66aJwH2nouxoqEa

Ray Cole Ray Cole

Biography

Quick Links

Resources

Support

Support