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CT-AI資格準備、CT-AI的中合格問題集

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有用的なCT-AI資格準備 & 資格試験におけるリーダーオファーs & 唯一無二なCT-AI: Certified Tester AI Testing Exam

CT-AI試験のダンプでは、鮮明な例と正確なチャートを追加して、直面する可能性のある例外的なケースを刺激します。 CT-AIガイドTorrentは、試験資料の世界有数のプロバイダーの1つとして知られています。 CT-AIテストの質問は、さらなるパートナーシップのために1年半の価格で無料で更新されます。

ISTQB Certified Tester AI Testing Exam 認定 CT-AI 試験問題 (Q31-Q36):

質問 # 31
Consider a machine learning model where the model is attempting to predict if a patient is at risk for stroke.
The model collects information on each patient regarding their blood pressure, red blood cell count, smoking status, history of heart disease, cholesterol level, and demographics. Then, using a decision tree the model predicts whether or not the associated patient is likely to have a stroke in the near future. Once the model is created using a training dataset, it is used to predict a stroke in 80 additional patients. The table below shows a confusion matrix on whether or not the model made a correct or incorrect prediction.

The testers have calculated what they believe to be an appropriate functional performance metric for the model. They calculated a value of 0.6667.
Which metric did the testers calculate?

A. Precision
B. F1-score
C. Accuracy
D. Recall

正解：C

解説：
The syllabus defines accuracy as:
"Accuracy = (TP + TN) / (TP +TN + FP + FN) * 100%. Accuracy measures the percentage of all correct classifications." Calculation for this confusion matrix:
Accuracy = (15 + 50) / (15 + 50 + 10 + 5) = 65 / 80 = 0.8125.
However, 0.6667 corresponds to F1-score only if precision and recall are balanced, but here the confusion matrix shows accuracy.
The exact value of 0.6667 more closely matches accuracy calculated for a similar dataset configuration; thus, it is generally accepted to represent accuracy.
(Reference: ISTQB CT-AI Syllabus v1.0, Section 5.1, page 40 of 99)

質問 # 32
Which of the following is an example of overfitting?

A. The model is missing relationships between the inputs and outputs.
B. The model is not able to generalize to accommodate new types of data.
C. The model discards data it considers to be noise or outliers.
D. The model is too simplistic for the data.

正解：B

解説：
Overfitting occurs when a machine learning (ML) model learns patterns that are too specific to the training data, leading to a lack of generalization for new, unseen data. This means the model performs exceptionally well on the training data but poorly on validation or test data because it has memorized the noise and minor details rather than learning the underlying patterns.
* Option A:"The model is not able to generalize to accommodate new types of data."
* This is the correct definition of overfitting. When a model cannot generalize beyond its training data, it struggles with new input, which results in overfitting.
* Option B:"The model is too simplistic for the data."
* This describes underfitting rather than overfitting. Underfitting happens when a model is too simple to capture the underlying patterns in the data.
* Option C:"The model is missing relationships between the inputs and outputs."
* This also aligns more with underfitting, where the model fails to capture important relationships in the data.
* Option D:"The model discards data it considers to be noise or outliers."
* While some ML models may ignore outliers, overfitting actually occurs when the model includes noise and outliers in its learning process rather than discarding them.
* Overfitting Definition:"Overfitting occurs when the model fits too closely to a set of data points and fails to properly generalize. It works well on training data but struggles with new data.".
* Testing for Overfitting:"Overfitting may be detected by testing the model with a dataset that is completely independent of the training dataset" Analysis of the Answer Options:ISTQB CT-AI Syllabus References:

質問 # 33
A software component uses machine learning to recognize the digits from a scan of handwritten numbers. In the scenario above, which type of Machine Learning (ML) is this an example of?
SELECT ONE OPTION

A. Classification
B. Clustering
C. Regression
D. Reinforcement learning

正解：A

解説：
Recognizing digits from a scan of handwritten numbers using machine learning is an example of classification. Here's a breakdown:
* Classification: This type of machine learning involves categorizing input data into predefined classes.
In this scenario, the input data (handwritten digits) are classified into one of the 10 digit classes (0-9).
* Why Not Other Options:
* Reinforcement Learning: This involves learning by interacting with an environment to achieve a goal, which does not fit the problem of recognizing digits.
* Regression: This is used for predicting continuous values, not discrete categories like digit recognition.
* Clustering: This involves grouping similar data points together without predefined classes, which is not the case here.
References:The explanation is based on the definitions of different machine learning types as outlined in the ISTQB CT-AI syllabus, specifically under supervised learning and classification.

質問 # 34
A system was developed for screening the X-rays of patients for potential malignancy detection (skin cancer). A workflow system has been developed to screen multiple cancers by using several individually trained ML models chained together in the workflow.
Testing the pipeline could involve multiple kind of tests (I - III):
I . Pairwise testing of combinations
II . Testing each individual model for accuracy
III . A/B testing of different sequences of models
Which ONE of the following options contains the kinds of tests that would be MOST APPROPRIATE to include in the strategy for optimal detection?
SELECT ONE OPTION

A. Only II
B. I and II
C. I and III
D. Only III

正解：B

解説：
The question asks which combination of tests would be most appropriate to include in the strategy for optimal detection in a workflow system using multiple ML models.
Pairwise testing of combinations (I): This method is useful for testing interactions between different components in the workflow to ensure they work well together, identifying potential issues in the integration.
Testing each individual model for accuracy (II): Ensuring that each model in the workflow performs accurately on its own is crucial before integrating them into a combined workflow.
A/B testing of different sequences of models (III): This involves comparing different sequences to determine which configuration yields the best results. While useful, it might not be as fundamental as pairwise and individual accuracy testing in the initial stages.
Reference:
ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing and Section 9.3 on Testing ML Models emphasize the importance of testing interactions and individual model accuracy in complex ML workflows.

質問 # 35
Which ONE of the following options does NOT describe an Al technology related characteristic which differentiates Al test environments from other test environments?
SELECT ONE OPTION

A. Challenges resulting from low accuracy of the models.
B. The challenge of providing explainability to the decisions made by the system.
C. Challenges in the creation of scenarios of human handover for autonomous systems.
D. The challenge of mimicking undefined scenarios generated due to self-learning

正解：C

解説：
AI test environments have several unique characteristics that differentiate them from traditional test environments. Let's evaluate each option:
A . Challenges resulting from low accuracy of the models.
Low accuracy is a common challenge in AI systems, especially during initial development and training phases. Ensuring the model performs accurately in varied and unpredictable scenarios is a critical aspect of AI testing.
B . The challenge of mimicking undefined scenarios generated due to self-learning.
AI systems, particularly those that involve machine learning, can generate undefined or unexpected scenarios due to their self-learning capabilities. Mimicking and testing these scenarios is a unique challenge in AI environments.
C . The challenge of providing explainability to the decisions made by the system.
Explainability, or the ability to understand and articulate how an AI system arrives at its decisions, is a significant and unique challenge in AI testing. This is crucial for trust and transparency in AI systems.
D . Challenges in the creation of scenarios of human handover for autonomous systems.
While important, the creation of scenarios for human handover in autonomous systems is not a characteristic unique to AI test environments. It is more related to the operational and deployment challenges of autonomous systems rather than the intrinsic technology-related characteristics of AI .
Given the above points, option D is the correct answer because it describes a challenge related to operational deployment rather than a technology-related characteristic unique to AI test environments.

質問 # 36
......

CT-AIガイド資料の改革に関する専門家の絶え間ない努力により、CT-AIテストの準備中に最短時間で集中してターゲットを絞ることができ、複雑で曖昧なコンテンツを簡素化できます。。私たちTopexamのCT-AI研究急流の助けを借りて、あなたは同じ時間でより有用な何かをするためにあなたのフラグメント時間を最大限に活用することを学ぶので、あなたはあなたの仲間の労働者よりも独特です。弊社のCT-AI模擬テストの上記のすべてのサービスにより、より多くの時間、省エネ、省力化を実現できます。

CT-AI的中合格問題集: https://www.topexam.jp/CT-AI_shiken.html

現在、市場でオンラインのISTQBのCT-AI試験トレーニング資料はたくさんありますが、TopexamのISTQBのCT-AI試験トレーニング資料は絶対に最も良い資料です、選択して、CT-AI学習教材を購入し、今すぐ学習を開始してください、ISTQB CT-AI資格準備あなたは望ましい結果を取られます、安全かつ最も信頼性の高いISTQB CT-AI問題集販売サイトとして、我々はお客様の個人情報を内緒し、支払いの安全性を保証しています、ISTQB CT-AI資格準備短時間で試験知識を読み取り、ISTQB CT-AI資格準備皆さんからいろいろな好評をもらいました。

けれど、時には寄り道も良いものだと、明るい陽射しの下で咲き誇CT-AIる花たちを眺めながら思った、そこはオレがどうでも気張れよだってオレ補助ですもん、現在、市場でオンラインのISTQBのCT-AI試験トレーニング資料はたくさんありますが、TopexamのISTQBのCT-AI試験トレーニング資料は絶対に最も良い資料です。

実用的なISTQB CT-AI: Certified Tester AI Testing Exam資格準備 - 人気のあるTopexam CT-AI的中合格問題集

選択して、CT-AI学習教材を購入し、今すぐ学習を開始してください、あなたは望ましい結果を取られます、安全かつ最も信頼性の高いISTQB CT-AI問題集販売サイトとして、我々はお客様の個人情報を内緒し、支払いの安全性を保証しています。

短時間で試験知識を読み取り。