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CT-AI復習問題集 &資格試験におけるリーダーオファー & CT-AI: Certified Tester AI Testing Exam

P.S.Xhs1991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=1r1kTZogpNJejo6DbF_CEyHbP1CjpR0CW

CT-AI認定を取得することは、学生、教師、主婦など、さまざまな分野の多くの人々にますます一般的になっていることがわかっています。全員がCT-AI認定を取得することが望まれます。私たちのCT-AI試験ダンプ問題は、短時間で認定を取得するために最善を尽くすために非常に必要です。 CT-AI Exam Braindumpsは、試験に合格する手を差し伸べます。 CT-AI Exam Torrentは、認定を取得するための最良の学習ツールです。

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

AI ベースシステムのテストの方法とテクニック: このセクションでは、ML システムのテストが敵対的攻撃やデータ汚染の防止にどのように役立つかを説明することに重点を置いています。

トピック 2

AIベースのシステムのテスト環境：このセクションでは、AIベースのシステムのテスト環境を区別する要因について説明します。

トピック 3

従来のシステムに必要なものとは異なるシステムです。

トピック 4

AI ベースシステムのテストの概要: このセクションでは、AI ベースシステムのシステム仕様がテストでどのような課題を生み出す可能性があるかに焦点を当て、自動化のバイアスとそれがテストにどのように影響するかについて説明します。

トピック 5

ML 機能パフォーマンスメトリック: このセクションでは、指定された混同行列のセットから ML 機能パフォーマンスメトリックを計算する方法などのトピックについて説明します。

トピック 6

ニューラルネットワークとテスト: この試験のセクションでは、DNN を含むニューラルネットワークの構造と機能の定義、およびニューラルネットワークのさまざまなカバレッジ測定について説明します。

トピック 7

テストでの AI の使用: このセクションの試験トピックでは、ソフトウェアテストで使用される AI テクノロジーの分類について説明します。

トピック 8

AI ベースシステムの品質特性: このセクションでは、AI ベースシステムの特性としての柔軟性と適応性の重要性を説明する方法と、AI ベースシステムの進化を管理することの重要性について説明します。また、安全関連のアプリケーションで AI ベースシステムを使用することを困難にする特性を思い出す方法についても説明します。

トピック 9

機械学習 ML: このセクションには、教師あり学習の一部としての分類と回帰が含まれており、ML アルゴリズムの選択に関係する要因が説明され、アンダーフィッティングとオーバーフィッティングが示されます。

トピック 10

AI 入門: この試験セクションでは、AI 効果やそれが AI の定義に与える影響などのトピックを取り上げます。狭義の AI、汎用 AI、スーパー AI を区別する方法を取り上げます。さらに、AI ベースのシステムに標準がどのように適用されるかを説明するトピックも取り上げます。

トピック 11

ML: データ: この試験のセクションでは、データ準備に関連するアクティビティと課題について説明します。また、データセットをテストして ML モデルを作成する方法や、データ品質が低いと結果として得られる ML モデルに問題が発生する可能性があることを認識する方法についても説明します。

>> CT-AI復習問題集 <<

ISTQB CT-AI Exam | CT-AI復習問題集 - 効果的な会社 CT-AI: Certified Tester AI Testing Exam 試験

研究により、学習への関心を刺激することが最善の解決策であることがわかっています。したがって、CT-AI準備ガイドの焦点は、CT-AI試験の準備方法を変更することにより、厳格で無駄なメモリモードを改革することです。 CT-AI実践教材のソフトバージョンは、知識と最新テクノロジーを組み合わせて学習力を大幅に刺激します。楽しい学習シーンと鮮明な説明をシミュレートすることにより、ユーザーは資格のあるCT-AI試験に合格する自信が大きくなります。

ISTQB Certified Tester AI Testing Exam 認定 CT-AI 試験問題 (Q15-Q20):

質問 # 15
A local business has a mail pickup/delivery robot for their office. The robot currently uses a track to move between pickup/drop off locations. When it arrives at a destination, the robot stops to allow a human to remove or deposit mail.
The office has decided to upgrade the robot to include AI capabilities that allow the robot to perform its duties without a track, without running into obstacles, and without human intervention.
The test team is creating a list of new and previously established test objectives and acceptance criteria to be used in the testing of the robot upgrade. Which of the following test objectives will test an AI quality characteristic for this system?

A. The robot must evolve to optimize its routing
B. The robot must recharge for no more than six hours a day
C. The robot must record the time of each delivery which is compiled into a report
D. The robot must complete 99.99% of its deliveries each day

正解：A

解説：
AI-based systems have specific quality characteristics, includingevolution,autonomy, andadaptability. A test objective that evaluates whether an AI systemevolvesto improve performance over time directly aligns with AI quality characteristics.
Explanation of Answer Choices:
* Option A: The robot must evolve to optimize its routing.
* Correct.Evolution is an AI quality characteristic that ensures the systemlearns from past experiencesand adapts to improve efficiency.
* Option B: The robot must recharge for no more than six hours a day.
* Incorrect.This is an operational constraint rather than an AI-specific quality characteristic.
* Option C: The robot must record the time of each delivery which is compiled into a report.
* Incorrect.Logging data does not relate to AI quality characteristics likeadaptability or autonomy.
* Option D: The robot must complete 99.99% of its deliveries each day.
* Incorrect.This is a performance target rather than an AI quality characteristic.
ISTQB CT-AI Syllabus References:
* Evolution as an AI Quality Characteristic:"Check how well the system learns from its own experience. Check how well the system copes when the profile of data changes (i.e., concept drift)".
Thus,Option A is the best choice as it directly tests an AI quality characteristic (evolution) in the upgraded autonomous robot.

質問 # 16
An image classification system is being trained for classifying faces of humans. The distribution of the data is
70% ethnicity A and 30% for ethnicities B, C and D. Based ONLY on the above information, which of the following options BEST describes the situation of this image classification system?
SELECT ONE OPTION

A. This is an example of hyperparameter bias.
B. This is an example of algorithmic bias.
C. This is an example of sample bias.
D. This is an example of expert system bias.

正解：C

解説：
* A. This is an example of expert system bias.
* Expert system bias refers to bias introduced by the rules or logic defined by experts in the system, not by the data distribution.
* B. This is an example of sample bias.
* Sample bias occurs when the training data is not representative of the overall population that the model will encounter in practice. In this case, the over-representation of ethnicity A (70%) compared to B, C, and D (30%) creates a sample bias, as the model may become biased towards better performance on ethnicity A.
* C. This is an example of hyperparameter bias.
* Hyperparameter bias relates to the settings and configurations used during the training process, not the data distribution itself.
* D. This is an example of algorithmic bias.
* Algorithmic bias refers to biases introduced by the algorithmic processes and decision-making rules, not directly by the distribution of training data.
Based on the provided information, optionB(sample bias) best describes the situation because the training data is skewed towards ethnicity A, potentially leading to biased model performance.

質問 # 17
Which of the following approaches would help overcome testing challenges associated with probabilistic and non-deterministic AI-based systems?

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

正解：B

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 # 18
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?

A. EDA cannot be used to detect the attack
B. EDA can detect and remove the false emails
C. EDA can restrict how many inputs can be provided by unique users
D. EDA can help detect the outlier emails from the real emails

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 # 19
"Splendid Healthcare" has started developing a cancer detection system based on ML. The type of cancer they plan on detecting has 2% prevalence rate in the population of a particular geography. It is required that the model performs well for both normal and cancer patients.
Which ONE of the following combinations requires MAXIMIZATION?
SELECT ONE OPTION

A. Maximize specificity number of classes
B. Maximize precision and accuracy
C. Maximize accuracy and recall
D. Maximize recall and precision

正解：D

解説：
Prevalence Rate and Model Performance:
The cancer detection system being developed by "Splendid Healthcare" needs to account for the fact that the type of cancer has a 2% prevalence rate in the population. This indicates that the dataset is highly imbalanced with far fewer positive (cancer) cases compared to negative (normal) cases.
Importance of Recall:
Recall, also known as sensitivity or true positive rate, measures the proportion of actual positive cases that are correctly identified by the model. In medical diagnosis, especially cancer detection, recall is critical because missing a positive case (false negative) could have severe consequences for the patient. Therefore, maximizing recall ensures that most, if not all, cancer cases are detected.
Importance of Precision:
Precision measures the proportion of predicted positive cases that are actually positive. High precision reduces the number of false positives, meaning fewer people will be incorrectly diagnosed with cancer. This is also important to avoid unnecessary anxiety and further invasive testing for those who do not have the disease.
Balancing Recall and Precision:
In scenarios where both false negatives and false positives have significant consequences, it is crucial to balance recall and precision. This balance ensures that the model is not only good at detecting positive cases but also accurate in its predictions, reducing both types of errors.
Accuracy and Specificity:
While accuracy (the proportion of total correct predictions) is important, it can be misleading in imbalanced datasets. In this case, high accuracy could simply result from the model predicting the majority class (normal) correctly. Specificity (true negative rate) is also important, but for a cancer detection system, recall and precision take precedence to ensure positive cases are correctly and accurately identified.
Conclusion:
Therefore, for a cancer detection system with a low prevalence rate, maximizing both recall and precision is crucial to ensure effective and accurate detection of cancer cases.

質問 # 20
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