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実用的CT-AI｜最新のCT-AI認定資格試験問題集試験｜試験の準備方法Certified Tester AI Testing Exam資格取得講座

顧客様と販売者の間での信頼性は苦労かつ大切なことだと良く知られます。誠意をみなぎるISTQB　CT-AI試験備考資料は我々チームの専業化を展示されるし、最完全の質問と再詳細の解説でもって試験に合格するのを助けるます。同時的に、皆様の認可は我々仕事の一番良い評価です。

我々はISTQBのCT-AI試験問題と解答また試験シミュレータを最初に提供し始めたとき、私達が評判を取ることを夢にも思わなかった。我々が今行っている保証は私たちが信じられないほどのフォームです。ISTQBのCT-AI試験はJapancertの保証を検証することができ、１００パーセントの合格率に達することができます。

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ISTQB Certified Tester AI Testing Exam 認定 CT-AI 試験問題 (Q27-Q32):

質問 # 27
A word processing company is developing an automatic text correction tool. A machine learning algorithm was used to develop the auto text correction feature. The testers have discovered when they start typing "Isle of Wight" it fills in "Isle of Eight". Several UAT testers have accepted this change without noticing. What type of bias is this?

A. Ignorance/Cognitive
B. Complacency/Disregard
C. Automation/Complacency
D. Geographical/Locality

正解：C

解説：
Automation bias, also known as complacency bias, occurs when humans over-rely on automated systems and fail to question or validate the system's output. In this scenario, the auto-text correction feature of the word processing tool incorrectly suggests "Isle of Eight" instead of "Isle of Wight." The issue arises because multiple UAT testers accept the incorrect suggestion without noticing it, demonstrating a reliance on the AI- based system rather than their own judgment.
Automation bias is commonly seen in:
* Text correction systems, where users accept incorrect suggestions without verifying them.
* Medical diagnosis AI tools, where doctors may rely too much on AI recommendations.
* Autonomous driving systems, where drivers become overly dependent on automation and fail to react in critical situations.
* Section 7.4 - Testing for Automation Bias in AI-Based Systemsexplains that automation bias occurs when people accept AI-generated outputs without verifying them, often leading to incorrect decisions.
Reference from ISTQB Certified Tester AI Testing Study Guide:

質問 # 28
Which of the following is a dataset issue that can be resolved using pre-processing?

A. Insufficient data
B. Wanted outliers
C. Numbers stored as strings
D. Invalid data

正解：C

解説：
Pre-processing is an essential step in data preparation that ensures data is clean, formatted correctly, and structured for effective machine learning (ML) model training. One common issue that can be resolved during pre-processing isnumbers stored as strings.
Explanation of Answer Choices:
* Option A: Insufficient data
* Incorrect. Pre-processing cannot resolve insufficient data. If data is lacking, techniques like data augmentation or external data collection are needed.
* Option B: Invalid data
* Incorrect. While pre-processing can identify and handle some forms of invalid data (e.g., missing values, duplicate entries), it does not resolve all invalid data issues. Some cases may require domain expertise to determine validity.
* Option C: Wanted outliers
* Incorrect. Pre-processing usually focuses on handling unwanted outliers. Wanted outliers may need to be preserved, which is more of a data selection decision rather than pre-processing.
* Option D: Numbers stored as strings
* Correct. One of the key functions of data pre-processing isdata transformation, which includes converting incorrectly formatted data types, such as numbers stored as strings, into their correct numerical format.
ISTQB CT-AI Syllabus References:
* Data Pre-Processing Steps:"Transformation: The format of the given data is changed (e.g., breaking an address held as a string into its constituent parts, dropping a field holding a random identifier, converting categorical data into numerical data, changing image formats)".

質問 # 29
A mobile app start-up company is implementing an AI-based chat assistant for e-commerce customers. In the process of planning the testing, the team realizes that the specifications are insufficient.
Which testing approach should be used to test this system?

A. State transition testing
B. Equivalence partitioning
C. Exploratory testing
D. Static analysis

正解：C

質問 # 30
Which of the following is correct regarding the layers of a deep neural network?

A. There is only an input and output layer
B. The output layer is not connected with the other layers to maintain integrity
C. There is at least one internal hidden layer
D. There must be a minimum of five total layers to be considered deep

正解：C

解説：
Adeep neural network (DNN)is a type of artificial neural network that consists of multiple layers between the input and output layers. TheISTQB Certified Tester AI Testing (CT-AI) Syllabusoutlines the following characteristics of a DNN:
* Structure of a Deep Neural Network:
* ADNN comprises at least three types of layers:
* Input layer: Receives the input data.
* Hidden layers: Perform complex feature extraction and transformations.
* Output layer: Produces the final prediction or classification.
* Analysis of Answer Choices:
* A (Only input and output layers)# Incorrect, as a DNN must haveat least one hidden layer.
* B (At least one internal hidden layer)# Correct, as a neural network must havehidden layersto be considered deep.
* C (Minimum of five layers required)# Incorrect, asthere is no strict definitionthat requires at least five layers.
* D (Output layer is not connected to other layers)# Incorrect, asthe output layer must be connectedto the hidden layers.
Thus,Option B is the correct answer, asa deep neural network must have at least one hidden layer.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 6.1 (Neural Networks and Deep Neural Networks)
* ISTQB CT-AI Syllabus v1.0, Section 6.2 (Structure of Deep Neural Networks).

質問 # 31
Upon testing a model used to detect rotten tomatoes, the following data was observed by the test engineer, based on certain number of tomato images.

For this confusion matrix which combinations of values of accuracy, recall, and specificity respectively is CORRECT?
SELECT ONE OPTION

A. 1,0.87,0.84
B. 0.84.1,0.9
C. 0.87.0.9. 0.84
D. 1,0.9, 0.8

正解：C

解説：
To calculate the accuracy, recall, and specificity from the confusion matrix provided, we use the following formulas:
* Confusion Matrix:
* Actually Rotten: 45 (True Positive), 8 (False Positive)
* Actually Fresh: 5 (False Negative), 42 (True Negative)
* Accuracy:
* Accuracy is the proportion of true results (both true positives and true negatives) in the total population.
* Formula: Accuracy=TP+TNTP+TN+FP+FN ext{Accuracy} = rac{TP + TN}{TP + TN + FP + FN}Accuracy=TP+TN+FP+FNTP+TN
* Calculation: Accuracy=45+4245+42+8+5=87100=0.87 ext{Accuracy} = rac{45 + 42}{45 + 42
+ 8 + 5} = rac{87}{100} = 0.87Accuracy=45+42+8+545+42=10087=0.87
* Recall (Sensitivity):
* Recall is the proportion of true positive results in the total actual positives.
* Formula: Recall=TPTP+FN ext{Recall} = rac{TP}{TP + FN}Recall=TP+FNTP
* Calculation: Recall=4545+5=4550=0.9 ext{Recall} = rac{45}{45 + 5} = rac{45}{50} = 0.9 Recall=45+545=5045=0.9
* Specificity:
* Specificity is the proportion of true negative results in the total actual negatives.
* Formula: Specificity=TNTN+FP ext{Specificity} = rac{TN}{TN + FP}Specificity=TN+FPTN
* Calculation: Specificity=4242+8=4250=0.84 ext{Specificity} = rac{42}{42 + 8} = rac{42}{50} = 0.84Specificity=42+842=5042=0.84 Therefore, the correct combinations of accuracy, recall, and specificity are 0.87, 0.9, and 0.84 respectively.
References:
* ISTQB CT-AI Syllabus, Section 5.1, Confusion Matrix, provides detailed formulas and explanations for calculating various metrics including accuracy, recall, and specificity.
* "ML Functional Performance Metrics" (ISTQB CT-AI Syllabus, Section 5).

質問 # 32
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CT-AI試験のブレーンダンプは、より大きな会社に注目させる能力を証明できます。 CT-AI試験ガイドは、良い仕事を得るのに役立ちます。 CT-AIのテスト準備は、ごく短時間で完全かつ効率的に自分自身を証明するのに役立ちます。何万人ものお客様が、CT-AI試験の質問で20〜30時間勉強すれば、CT-AI試験に合格し、それに応じて資格を取得できることを証明しました。

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CT-AI試験材料は多くのお客様に評価されています、ISTQB CT-AI認定資格試験問題集それで、あなたは私たちを完全に信じられます、ISTQB 学習教材としてCT-AI準備トレントを使用する場合、試験の確認と準備に必要な時間と労力はほとんど必要ありません、ISTQB CT-AI認定資格試験問題集試験に受かるために大量の時間とトレーニング費用を費やした受験生がたくさんいます、Japancert CT-AI資格取得講座が提供しておりますのは専門家チームの研究した問題と真題で弊社の高い名誉はたぶり信頼をうけられます、社会は想像以上に速く変化しているために、我々の更新されたCT-AI資格取得講座 - Certified Tester AI Testing Exam学習資料はあなたが必要なスキルを磨くのに役立ちます。

お母様の留守の間は私が代理になって、どんな用の時にも私はこちらへ来るつもCT-AI技術問題りなのだが、まだ親と認めないお扱いを受けるのに悲観されますなどと、御簾（みす）の前にすわって言っている時、姫君はほのかに返辞くらいはしていた。

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久し振りだな、元気にしているか、CT-AI試験材料は多くのお客様に評価されています、それで、あなたは私たちを完全に信じられます、ISTQB 学習教材としてCT-AI準備トレントを使用する場合、試験の確認と準備に必要な時間と労力はほとんど必要ありません。

試験に受かるために大量の時間とトレーニング費用を費やした受験生がたCT-AIくさんいます、Japancertが提供しておりますのは専門家チームの研究した問題と真題で弊社の高い名誉はたぶり信頼をうけられます。

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