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CT-AI最新の試験トレントは、資格試験ごとに分類が異なるため、ユーザーはユーザーの実際のニーズに応じて独自の学習モードを選択できます。 CT-AI試験の質問は、ユーザーが選択できるさまざまな学習モードを提供します。これは、コンピューターや携帯電話の複数のクライアントがオンラインで勉強したり、オフライン統合のためにデータを印刷したりするために使用できます。手頃な価格と実践を完璧にサポートする最新のCT-AI試験のトレントは、CT-AI試験の質問のみを気に入っています。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Practical Considerations | 12% | - Documentation and Reporting - Test Environment Setup for AI - Test Data Quality and Preparation - Automation in AI Testing |
| Topic 2: AI Test Methods and Techniques | 20% | - Test Oracle Techniques for AI - Metamorphic Testing - Adversarial Testing - Black-Box Testing for AI Systems |
| Topic 3: Basics of AI | 8% | - Machine Learning Types (Supervised, Unsupervised, Reinforcement) - Data Concepts (Training, Validation, Test Data) - Definitions and Terminology - AI Types and Techniques |
| Topic 4: Neural Networks and Deep Learning | 20% | - Model Training and Evaluation Metrics - Neural Network Architecture Basics - Overfitting and Underfitting - Testing Deep Learning Models |
| Topic 5: Testing AI-Based Systems | 20% | - Challenges in Testing AI-Based Systems - Test Levels for AI-Based Systems - Testing AI-Specific Quality Characteristics - Bias and Fairness Considerations |
| Topic 6: Testing AI-Specific Quality Characteristics | 20% | - Fairness and Bias Detection - Robustness Testing - Explainability and Interpretability - Accuracy, Precision, Recall, F1-Score |
人生はさまざまな試しがある、人生の頂点にかからないけど、刺激のない生活に変化をもたらします。あなたは我々社の提供する質高いISTQB CT-AI問題集を使用して、試験に参加します。もし無事にCT-AI試験に合格したら、あなたはもっと自信になって、更なる勇気でやりたいことをしています。
質問 # 150
Which ONE of the following options is a technology used to implement AI?
正解：D
解説：
The correct answer is A. Genetic algorithms . In the CT-AI syllabus, Chapter 1, Section 1.4 "AI Technologies" lists multiple technologies used to implement AI, including fuzzy logic, search algorithms, reasoning techniques, neural networks, Bayesian models, decision trees, random forest, linear regression, logistic regression, clustering algorithms, genetic algorithms , and support vector machines. The syllabus also states that AI-based systems typically implement one or more of these technologies.
Option B, autonomy , is not an AI implementation technology; it is a quality characteristic describing the ability of a system to work without human oversight or intervention for sustained periods. Option C, classification , is a supervised learning problem type in which inputs are assigned to predefined classes, not itself an implementation technology in the syllabus taxonomy. Option D, reinforcement learning , is a form of machine learning, but the option that directly appears in the AI technology list is genetic algorithms .
References/topics: CT-AI Syllabus Chapter 1, Section 1.4 "AI Technologies"; Chapter 3, Section 3.1
"Forms of ML."
=========
質問 # 151
Which ONE of the following types of coverage SHOULD be used if test cases need to cause each neuron to achieve both positive and negative activation values?
SELECT ONE OPTION
正解：B
解説：
* Coverage for Neuron Activation Values: Sign change coverage is used to ensure that test cases cause each neuron to achieve both positive and negative activation values. This type of coverage ensures that the neurons are thoroughly tested under different activation states.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 6.2 Coverage Measures for Neural Networks, which details different types of coverage measures, including sign change coverage.
質問 # 152
"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
正解：A
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 # 153
A company is using a spam filter to attempt to identify which emails should be marked as spam. Detection rules are created by the filter that causes a message to be classified as spam. An attacker wishes to have all messages internal to the company be classified as spam. So, the attacker sends messages with obvious red flags in the body of the email and modifies the "from" portion of the email to make it appear that the emails have been sent by company members. The testers plan to use exploratory data analysis (EDA) to detect the attack and use this information to prevent future adversarial attacks.
How could EDA be used to detect this attack?
正解：D
解説：
The syllabus explains that EDA can be used to analyze data to identify outliers and unusual patterns, which can indicate adversarial attacks like data poisoning:
"Testing to detect data poisoning is possible using EDA, as poisoned data may show up as outliers." (Reference: ISTQB CT-AI Syllabus v1.0, Section 9.1.2, page 67 of 99)
質問 # 154
A neural network has been designed and created to assist day-traders improve efficiency when buying and selling commodities in a rapidly changing market. Suppose the test team executes a test on the neural network where each neuron is examined. For this network the shortest path indicates a buy, and it will only occur when the one-day predicted value of the commodity is greater than the spot price by 0.75%. The neurons are stimulated by entering commodity prices and testers verify that they activate only when the future value exceeds the spot price by at least
0.75%. Which of the following statements BEST explains the type of coverage being tested on the neural network?
正解：B
解説：
The syllabus details that threshold coverage requires each neuron to achieve an activation value greater than a specified threshold:
"Threshold coverage: Full threshold coverage requires that each neuron in the neural network achieves an activation value greater than a specified threshold."
質問 # 155
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