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第一に、当社は常に優秀なCT-AI学習ガイドと卓越した技術で受験者にフィードバックし、最も専門的な試験教材を継続的に開発しています。第二に、当社のCT-AI学習資料は、最新のサービス指向システムの作成に固執し、お客様の便宜のためにより優先的な活動を提供するよう努めています。最後になりましたが、以下のように、無料のデモがあります。次のように、どのCT-AI試験資料デモをダウンロードして選択することができます。したがって、あなたは私たちのCT-AI学習資料を愛するでしょう！
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Test Methods and Techniques | 20% | - Test Oracle Techniques for AI - Black-Box Testing for AI Systems - Adversarial Testing - Metamorphic Testing |
| Topic 2: Practical Considerations | 12% | - Test Environment Setup for AI - Test Data Quality and Preparation - Automation in AI Testing - Documentation and Reporting |
| Topic 3: Basics of AI | 8% | - Definitions and Terminology - AI Types and Techniques - Data Concepts (Training, Validation, Test Data) - Machine Learning Types (Supervised, Unsupervised, Reinforcement) |
| Topic 4: Neural Networks and Deep Learning | 20% | - Model Training and Evaluation Metrics - Neural Network Architecture Basics - Testing Deep Learning Models - Overfitting and Underfitting |
| Topic 5: Testing AI-Specific Quality Characteristics | 20% | - Robustness Testing - Accuracy, Precision, Recall, F1-Score - Explainability and Interpretability - Fairness and Bias Detection |
| Topic 6: Testing AI-Based Systems | 20% | - Challenges in Testing AI-Based Systems - Test Levels for AI-Based Systems - Bias and Fairness Considerations - Testing AI-Specific Quality Characteristics |
良い仕事を見つけることを選択した場合、できる限りCT-AI認定を取得することが重要です。効率化を促すすばらしい製品があります。したがって、テストの準備をするためのすべての効果的かつ中心的なプラクティスがあります。専門的な能力を備えているため、CT-AI試験問題を編集するために必要なテストポイントに合わせることができます。あなたの難しさを解決するために、試験の中心を指し示します。したがって、高品質の資料を使用すると、試験に効果的に合格し、安心して目標を達成できます。
質問 # 150
Which ONE of the following tests is LEAST likely to be performed during the ML model testing phase?
SELECT ONE OPTION
正解：D
解説：
The question asks which test is least likely to be performed during the ML model testing phase. Let's consider each option:
* Testing the accuracy of the classification model (A): Accuracy testing is a fundamental part of the ML model testing phase. It ensures that the model correctly classifies the data as intended and meets the required performance metrics.
* Testing the API of the service powered by the ML model (B): Testing the API is crucial, especially if the ML model is deployed as part of a service. This ensures that the service integrates well with other systems and that the API performs as expected.
* Testing the speed of the training of the model (C): This is least likely to be part of the ML model testing phase. The speed of training is more relevant during the development phase when optimizing and tuning the model. During testing, the focus is more on the model's performance and behavior rather than how quickly it was trained.
* Testing the speed of the prediction by the model (D): Testing the speed of prediction is important to ensure that the model meets performance requirements in a production environment, especially for real- time applications.
:
ISTQB CT-AI Syllabus Section 3.2 on ML Workflow and Section 5 on ML Functional Performance Metrics discuss the focus of testing during the model testing phase, which includes accuracy and prediction speed but not the training speed.
質問 # 151
An airline has created a ML model to project fuel requirements for future flights. The model imports weather data such as wind speeds and temperatures, calculates flight routes based on historical routings from air traffic control, and estimates loads from average passenger and baggage weights. The model performed within an acceptable standard for the airline throughout the summer but as winter set in the load weights became less accurate. After some exploratory data analysis it became apparent that luggage weights were higher in the winter than in summer.
Which of the following statements BEST describes the problem and how it could have been prevented?
正解：C
解説：
The syllabus states:
"Concept drift occurs when the operational environment changes without the trained model changing correspondingly. The outputs of the model become less accurate and less useful.
Therefore, the operational model should be regularly evaluated against its acceptance criteria."
質問 # 152
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?
SELECT ONE OPTION
正解：A
解説：
* 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.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Data Quality and its Effect on the ML Model and ML Functional Performance Metrics.
質問 # 153
Which ONE of the following statements about a system MOST describes an autonomous system?
正解：A
解説：
An autonomous system is capable of performing tasks independently, without requiring human intervention. The self-driving car in option B is an example of an autonomous system because it automatically takes action (stopping the car) based on its internal decision-making, in response to the driver's behavior.
質問 # 154
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
正解：A
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, optionCis 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 # 155
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成功したいですか？ 目立ちたいですか？ 当社の製品を選択してください。 数年間、優れたCT-AI模擬試験教材を提供するように最善を尽くしています。 当社の製品を選択した場合は、試験を受験して有効な認定資格を取得し、ISTQB CT-AI模擬試験教材で大きなメリットを得ることができます。 適切なポジションに応募する場合、Certified Tester AI Testing Examが役立ちます。 よろしければ、CT-AI模擬テストファイルを使用して、新しい一歩を踏み出し、より良い未来を実現してください。
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