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CT-AI日本語版試験解答、CT-AI日本語復習赤本

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難しいCT-AI認定試験に合格したいなら、試験の準備をするときに関連する参考書を使わないとダメです。自分に合っている優秀な参考資料がほしいとしたら、一番来るべき場所はJPNTestです。JPNTestの知名度が高くて、IT認定試験に関連するいろいろな優秀な問題集を持っています。それに、すべてのCT-AI試験問題集に対する無料なdemoがあります。JPNTestのCT-AI問題集があなたに適するかどうかを確認したいなら、まず問題集のデモをダウンロードして体験してください。

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

Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based

トピック 2

Machine Learning ML: This section includes the classification and regression as part of supervised learning, explaining the factors involved in the selection of ML algorithms, and demonstrating underfitting and overfitting.

トピック 3

ML Functional Performance Metrics: In this section, the topics covered include how to calculate the ML functional performance metrics from a given set of confusion matrices.

トピック 4

Testing AI-Specific Quality Characteristics: In this section, the topics covered are about the challenges in testing created by the self-learning of AI-based systems.

トピック 5

Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.

トピック 6

Methods and Techniques for the Testing of AI-Based Systems: In this section, the focus is on explaining how the testing of ML systems can help prevent adversarial attacks and data poisoning.

>> CT-AI日本語版試験解答 <<

CT-AI日本語復習赤本 & CT-AIテスト難易度

ローマは一日に建てられませんでした。多くの人にとって、短い時間でCT-AI試験に合格できることは難しいです。しかし、幸いにして、CT-AIの練習問題の専門会社として、弊社の最も正確な質問と回答を含むCT-AI試験の資料は、CT-AI試験対する問題を効果的に解決できます。CT-AI練習問題をちゃんと覚えると、CT-AIに合格できます。あなたはCT-AI練習問題を選ばれば、試験に合格できますよ！

ISTQB Certified Tester AI Testing Exam 認定 CT-AI 試験問題 (Q78-Q83):

質問 # 78
In a certain coffee producing region of Colombia, there have been some severe weather storms, resulting in massive losses in production. This caused a massive drop in stock price of coffee.
Which ONE of the following types of testing SHOULD be performed for a machine learning model for stock-price prediction to detect influence of such phenomenon as above on price of coffee stock.
SELECT ONE OPTION

A. Testing for accuracy
B. Testing for security
C. Testing for bias
D. Testing for concept drift

正解：D

解説：
* Type of Testing for Stock-Price Prediction Models: Concept drift refers to the change in the statistical properties of the target variable over time. Severe weather storms causing massive losses in coffee production and affecting stock prices would require testing for concept drift to ensure that the model adapts to new patterns in data over time.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 7.6 Testing for Concept Drift, which explains the need to test for concept drift in models that might be affected by changing external factors.

質問 # 79
Which of the following is an example of a clustering problem that can be resolved by unsupervised learning?

A. Classifying muffin purchases based on the perceived attractiveness of their packaging
B. Grouping individual fish together based on their types of fins
C. Associating shoppers with their shopping tendencies
D. Estimating the expected purchase of cat food after a particularly successful ad campaign

正解：C

解説：
Clustering is a form ofunsupervised learning, which groups data points based onsimilarities without predefined labels. According toISTQB CT-AI Syllabus, clustering is used in scenarios where:
* The objective is to find natural groupings in data.
* The dataset does not have labeled outputs.
* Patterns and structures need to be identified automatically.
Analyzing the answer choices:
* A. Associating shoppers with their shopping tendencies # Correct
* Shoppers can be grouped based on purchasing behaviors(e.g., luxury shoppers vs. budget- conscious shoppers), which is a typical clustering application in market segmentation.
* B. Grouping individual fish together based on their types of fins # Incorrect
* If thetypes of fins are labeled, it becomes aclassification problem, which requires supervised learning.
* C. Classifying muffin purchases based on packaging attractiveness # Incorrect
* Classification, not clustering, because attractiveness scores or labels must be predefined.
* D. Estimating the expected purchase of cat food after an ad campaign # Incorrect
* This is a prediction task, best suited forregression models, which are part of supervised learning.
Thus,Option A is the best answer, asclusteringis used togroup shoppers based on tendencies without predefined labels.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 3.1.2 (Unsupervised Learning - Clustering and Association)
* ISTQB CT-AI Syllabus v1.0, Section 3.3 (Selecting a Form of ML - Clustering).

質問 # 80
Which ONE of the following options does NOT describe a challenge for acquiring test data in ML systems?
SELECT ONE OPTION

A. Data for the use case is being generated at a fast pace.
B. Nature of data constantly changes with lime.
C. Test data being sourced from public sources.
D. Compliance needs require proper care to be taken of input personal data.

正解：A

解説：
* Challenges for Acquiring Test Data in ML Systems: Compliance needs, the changing nature of data over time, and sourcing data from public sources are significant challenges. Data being generated quickly is generally not a challenge; it can actually be beneficial as it provides more data for training and testing.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Data Preparation and Data Quality Issues.

質問 # 81
An engine manufacturing facility wants to apply machine learning to detect faulty bolts. Which of the following would result in bias in the model?

A. Selecting training data by purposely including all known faulty conditions
B. Selecting training data by purposely excluding specific faulty conditions
C. Selecting testing data from a boat manufacturer's bolt longevity data
D. Selecting testing data from a different dataset than the training dataset

正解：B

解説：
Bias in AI models often originates fromincomplete or non-representative training data. In this case, if the training datasetpurposely excludes specific faulty conditions, the machine learning model willfail to learn and detectthese conditions in real-world scenarios.
This results in:
* Sample bias, where the training data is not fully representative of all possible faulty conditions.
* Algorithmic bias, where the model prioritizes certain defect types while ignoring others.
* B. Selecting training data by purposely including all known faulty conditions# This would help reduce bias by improving model generalization.
* C. Selecting testing data from a different dataset than the training dataset# This is a good practice to evaluate model generalization but does not inherently introduce bias.
* D. Selecting testing data from a boat manufacturer's bolt longevity data# While using unrelated data can createpoor model accuracy, it does not directly introduce bias unless systematic patterns in the incorrect dataset lead to unfair decision-making.
* Section 8.3 - Testing for Algorithmic, Sample, and Inappropriate Biasstates thatsample bias can occur if the training dataset is not fully representative of the expected data space, leading to biased predictions.
Why are the other options incorrect?Reference from ISTQB Certified Tester AI Testing Study Guide:

質問 # 82
Which of the following is correct regarding the layers of a deep neural network?

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

正解：B

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 # 83
......

私たち全員が知っているように、試験の準備プロセスは非常に面倒で時間がかかります。 CT-AI試験の準備のために他のことをするために時間を割く必要があり、多くの重要なことが遅れました。この問題に直面した場合は、CT-AIの実際の試験を選択してください。教材を使用すると、試験に参加できるのは準備に約20〜30時間かかる場合のみです。残りの時間は、やりたいことを何でもできます。これにより、レビューのプレッシャーを完全に軽減できます。

CT-AI日本語復習赤本: https://www.jpntest.com/shiken/CT-AI-mondaishu

P.S. JPNTestがGoogle Driveで共有している無料かつ新しいCT-AIダンプ：https://drive.google.com/open?id=1CYgyFQKXjawx1NvKLSiaxipgqh1QtYrY

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