最新の更新CT-AI試験問題集 &検証するISTQB認定トレーニング -素敵なISTQB Certified Tester AI Testing Exam

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CT-AI試験問題集, CT-AI技術試験, CT-AI日本語版トレーリング, CT-AI資格トレーリング, CT-AI模試エンジン

さらに、Jpexam CT-AIダンプの一部が現在無料で提供されています：https://drive.google.com/open?id=1MFBZAjJt_1F5d7YRy6imBAgpAe24fR9C

もし、あなたもCT-AI試験に合格したいです。しかし、どんな資料を選択したらいいですか？お勧めしたいのはCT-AI試験問題集です。購入する前に、ISTQBのウエブサイトでCT-AI試験問題集のデモをダウンロードしてみると、あなたはきっとCT-AI試験問題集に魅了されます。

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: 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 2
  • 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 3
  • 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 4
  • 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 5
  • 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 6
  • 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。

>> CT-AI試験問題集 <<

CT-AI技術試験、CT-AI日本語版トレーリング

CT-AI認定を迅速に取得するために、Jpexam人々は多くのCT-AI学習教材を購入しましたが、これらの教材は適切ではなく、助けにもならないこともわかっています。 適切なCT-AIテストガイドも見つからない場合は、CT-AI学習資料を使用することをお勧めします。 当社の製品は問題の解決に役立つため、CT-AIの最新の質問を購入して実践することを決定しても、決して失望させません。 また、CT-AI試験問題のCertified Tester AI Testing Exam合格率は99％〜100％です。

ISTQB Certified Tester AI Testing Exam 認定 CT-AI 試験問題 (Q158-Q163):

質問 # 158
You are using a neural network to train a robot vacuum to navigate without bumping into objects. You set up a reward scheme that encourages speed but discourages hitting the bumper sensors. Instead of what you expected, the vacuum has now learned to drive backwards because there are no bumpers on the back.
This is an example of what type of behavior?

  • A. Interpretability
  • B. Transparency
  • C. Reward-hacking
  • D. Error-shortcircuiting

正解：C

解説：
The syllabus defines reward hacking as:
"Reward hacking can result from an AI-based system achieving a specified goal by using a 'clever' or 'easy' solution that perverts the spirit of the designer's intent." In this case, the vacuum found a loophole in the reward function-driving backwards to avoid bumper triggers while maximizing reward for speed.
(Reference: ISTQB CT-AI Syllabus v1.0, Section 2.6, page 24 of 99)


質問 # 159
Which statement regarding data preparation in the ML workflow is correct?
Choose ONE option (1 out of 4)

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

正解：C

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 # 160
Which of the following statements about the structure and function of neural networks is true?
Choose ONE option (1 out of 4)

  • A. A single-layer perceptron is NOT a neural network
  • B. The input layer of a deep neural network must have at least as many neurons as its output layer
  • C. Training a neural network only changes the values of the weights at the connections between neurons
  • D. The bias of a neuron is determined by the activation values of the neurons in the previous layer

正解：C

解説：
Section1.7 - Neural Networksof the ISTQB CT-AI syllabus explains that neural networks consist of neurons connected by weighted links. During training,learning occurs by adjusting the weights on these connections.
This is the essence of gradient descent and backpropagation. Option B correctly states this behavior: only theweightsare modified, not the activation functions, neuron counts, or architectural structure.
Option A is incorrect because a neuron'sbiasis not determined by previous activations; it is an independent trainable parameter added to the weighted input sum. Option C is incorrect because the syllabus states that asingle-layer perceptron is a valid type of neural network, although limited to linearly separable problems.
Option D is incorrect because no rule requires the number of input neurons to exceed or equal the number of output neurons. Instead, input neurons correspond to thenumber of features, while output neurons correspond totasks or classes.
Therefore,Option Bprecisely reflects the syllabus definition of what changes during neural network training.


質問 # 161
You are conducting a user acceptance test of a decision support recommendation system used in a data processing business. In addition to testing the functional performance of the recommendation systems, what else would you be MOST likely to test?

  • A. The amount of system resources consumed by each recommendation
  • B. The number of accurate recommendations per day
  • C. The number of inaccurate recommendations per day
  • D. The accuracy of the human decision when presented with an inaccurate recommendation

正解：D

解説：
The correct answer is D . A decision support recommendation system does not merely produce predictions; it influences human decision-making. The CT-AI syllabus explains that automation bias, also called complacency bias, occurs when humans become too trusting of automated recommendations. One form occurs when a human accepts system recommendations and fails to consider other sources, including their own judgment.
For that reason, testing should cover both the quality of the system's recommendations and the quality of the corresponding human input provided by representative users. The syllabus explicitly states that testers should understand how human decision-making may be compromised and should test both the system recommendations and the human input.
Options A and C are still functional performance measures of the recommendation system. Option B is a performance efficiency concern and may be relevant at system testing, but it does not address the decision- support risk. Option D directly targets the critical acceptance concern: whether users can detect and appropriately handle inaccurate AI recommendations rather than blindly following them.
References/topics: CT-AI Syllabus Chapter 7, Section 7.4 "Testing for Automation Bias in AI-Based Systems"; Section 7.2.6 "Acceptance Testing."


質問 # 162
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?

  • A. Value-change coverage
  • B. Neuron coverage
  • C. Sign-change coverage
  • D. Threshold coverage

正解：D

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 # 163
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CT-AI技術試験: https://www.jpexam.com/CT-AI_exam.html

ちなみに、Jpexam CT-AIの一部をクラウドストレージからダウンロードできます：https://drive.google.com/open?id=1MFBZAjJt_1F5d7YRy6imBAgpAe24fR9C

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