実用的なCT-AI認定内容 |素晴らしい合格率のCT-AI: Certified Tester AI Testing Exam |効率的なCT-AI日本語受験攻略

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ISTQB CT-AI Exam Overview:

Certification Vendor:	ISTQB
Exam Name:	ISTQB Certified Tester - AI Testing
Exam Number:	CT-AI
Exam Duration:	60 minutes
Real Exam Qty:	40
Passing Score:	65%
Available Languages:	English
Certificate Validity Period:	Lifetime (no expiration)
Exam Format:	Multiple Choice
Exam Price:	EUR 250
Related Certifications:	ISTQB CTFL ISTQB CTAL-TTA
Sample Questions:	ISTQB CT-AI Sample Questions
Exam Way:	Online proctored or in-person at authorized testing centers
Pre Condition:	ISTQB CTFL (Certified Tester Foundation Level) certification is recommended but not mandatory
Official Syllabus URL:	https://www.istqb.org/certifications/artificial-intelligence-testing-certification

>> CT-AI認定内容 <<

更新するCT-AI認定内容 & 合格スムーズCT-AI日本語受験攻略 | 素敵なCT-AI関連日本語内容

IT職員としてのあなたは昇進したいのですか。プロなIT技術専門家になりたいのですか。速くISTQBのCT-AI認定試験「Certified Tester AI Testing Exam」を申し込みましょう。この認証がどんなに重要するかあなたもよく知っています。試験に合格できないなんて心配しないで、あなたの能力を疑わないでください。ISTQBのCT-AI認定試験「Certified Tester AI Testing Exam」を受けたいのなら、試験の準備に関する全ての質問がPass4Testは解決して差し上げます。Pass4TestはIT認証に対するプロなサイトです。Pass4Testがそばのいてあげたら、全ての難問が解決できます。Pass4Testに助けられた受験生は数え切れないです。Pass4Testをクロックしたら、１００パーセントの成功を差し上げます。

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 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 2	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 3	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 4	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 5	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。
トピック 6	従来のシステムに必要なものとは異なるシステムです。

ISTQB Certified Tester AI Testing Exam 認定 CT-AI 試験問題 (Q60-Q65):

質問 # 60
A tourist calls an airline to book a ticket and is connected with an automated system which is able to recognize speech, understand requests related to purchasing a ticket, and provide relevant travel options. When the tourist asks about the expected weather at the destination or potential impacts on operations because of the tight labor market the only response from the automated system is: "I don't understand your question." This AI system should be categorized as?

A. Conventional AI
B. General AI
C. Super AI
D. Narrow AI

正解：D

解説：
Narrow AI (also known as Weak AI) is designed to perform specific tasks or solve particular problems within a limited domain, such as booking a ticket or recognizing speech. In this case, the AI system is specialized in handling flight bookings and related tasks but is not capable of addressing broader or more complex questions, like weather conditions or labor market impacts.

質問 # 61
Which of the following neural network coverage criteria can be adapted for its application?

A. Threshold coverage
B. Sign-Change coverage
C. Sign-Sign coverage
D. Neuron coverage

正解：A

解説：
Section4.2 - Test Coverage Criteria for AI Modelsof the ISTQB CT-AI syllabus describes neural network-specific coverage methods. Among the techniques,threshold coverageis explicitly noted asadaptable, meaning testers may choose different thresholds to determine whether neuron activation is considered "covered." This flexibility makes threshold coverage adjustable to the model architecture, problem domain, and required test thoroughness.

質問 # 62
Which statement regarding data preparation in the ML workflow is correct?
Choose ONE option (1 out of 4)

A. A key challenge in data transformation is the removal or correction of erroneous data.
B. Since data preparation is time-consuming, all steps should be automated.
C. Sampling is so well researched that it is no longer considered risky.
D. One challenge of data gathering is obtaining high-quality data from multiple sources.

正解：D

解説：
The ISTQB CT-AI syllabus describes theML data preparation workflowin Section2.2 - Data Preparation.
Data preparation consists ofdata gathering,cleaning,transformation, andsampling. The syllabus emphasizes that one significant challenge duringdata gatheringis combining data frommultiple heterogeneous sources, which often differ in structure, quality, and format. Ensuring the resulting dataset is accurate, complete, and representative can be complex, making this a critical challenge in the ML workflow.
This aligns directly with OptionC.
Option A is incorrect because erroneous data correction is part ofcleaning, not transformation. Option B contradicts the syllabus: while automation can help,not all steps should be automateddue to the need for expert oversight, especially in detecting subtle data quality issues. Option D is incorrect because sampling continues to involve risk-particularly around representativeness-and the syllabus emphasizes caution, not complacency.
Thus, OptionCis the only statement that accurately reflects the syllabus.

質問 # 63
A bank wants to use an algorithm to determine which applicants should be given a loan. The bank hires a data scientist to construct a logistic regression model to predict whether the applicant will repay the loan or not.
The bank has enough data on past customers to randomly split the data into a training dataset and a test
/validation dataset. A logistic regression model is constructed on the training dataset using the following independent variables:
* Gender
* Marital status
* Number of dependents
* Education
* Income
* Loan amount
* Loan term
* Credit score
The model reveals that those with higher credit scores and larger total incomes are more likely to repay their loans. The data scientist has suggested that there might be bias present in the model based on previous models created for other banks.
Given this information, what is the best test approach to check for potential bias in the model?

A. A/B testing should be used to verify that the test data set does not detect any bias that might have been introduced by the original training data. If the two models significantly differ, it will indicate there is bias in the original model.
B. Acceptance testing should be used to make sure the algorithm is suitable for the customer. The team can re-work the acceptance criteria such that the algorithm is sure to correctly predict the remaining applicants that have been set aside for the validation dataset ensuring no bias is present.
C. Back-to-back testing should be used to compare the model created using the training data set to another model created using the test data set. If the two models significantly differ, it will indicate there is bias in the original model.
D. Experience-based testing should be used to confirm that the training data set is operationally relevant.
This can include applying exploratory data analysis (EDA) to check for bias within the training data set.

正解：D

解説：
The syllabus mentions that experience-based testing and EDA are effective for detecting biases:
"Experience-based testing can be used to verify that the training dataset is operationally relevant and identify potential sources of bias. EDA is also useful for exploring the data and understanding any relationships that might lead to bias in the model." (Reference: ISTQB CT-AI Syllabus v1.0, Section 8.3, page 58 of 99)

質問 # 64
A bank wants to use an algorithm to determine which applicants should be given a loan. The bank hires a data scientist to construct a logistic regression model to predict whether the applicant will repay the loan or not. The bank has enough data on past customers to randomly split the data into a training data set and a test/validation data set. A logistic regression model is constructed on the training data set using the following independent variables:
* Gender
* Marital status
* Number of dependents
* Education
* Income
* Loan amount
* Loan term
* Credit score
The model reveals that those with higher credit scores and larger total incomes are more likely to repay their loans. The data scientist has suggested that there might be bias present in the model based on previous models created for other banks.
Given this information, what is the best test approach to check for potential bias in the model?

A. Experienced-based testing should be used to confirm that the training data set is operationally relevant. This can include applying exploratory data analysis (EDA) to check for bias within the training data set.
B. A/B testing should be used to verify that the test data set does not detect any bias that might have been introduced by the original training data. If the two models significantly differ, it will indicate there is bias in the original model.
C. Acceptance testing should be used to make sure the algorithm is suitable for the customer. The team can re-work the acceptance criteria such that the algorithm is sure to correctly predict the remaining applicants that have been set aside for the validation data set ensuring no bias is present.
D. Back-to-back testing should be used to compare the model created using the training data set to another model created using the test data set, if the two models significantly differ, it will indicate there is bias in the original model.

正解：A

質問 # 65
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CT-AI日本語受験攻略: https://www.pass4test.jp/CT-AI.html

2026年Pass4Testの最新CT-AI PDFダンプおよびCT-AI試験エンジンの無料共有：https://drive.google.com/open?id=1CeGOGv1-FiuZ2P9hTq162C72WHxL_Lqq