ちなみに、CertShiken 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=1cklwr1h5lvrPfms0n4drP_DUO0C5wGkq
IT技術人員にとって、両親にあなたの仕事などの問題を危ぶんでいきませんか？高い月給がある仕事に従事したいですか？美しい未来を有したいですか？だから、我々CertShikenのCT-AI問題集をご覧になってください。ここでは、あなたは一番質高い資料と行き届いたサービスを楽しみしています。あなたはCertShikenのISTQB CT-AI問題集を手に入れる前に、問題集の試用版を無料に使用できます。
| トピック | 出題範囲 |
|---|---|
| トピック 1 |
|
| トピック 2 |
|
| トピック 3 |
|
| トピック 4 |
|
| トピック 5 |
|
| トピック 6 |
|
| トピック 7 |
|
| トピック 8 |
|
| トピック 9 |
|
| トピック 10 |
|
CT-AI試験はIT業界でのあなたにとって重要です。あなたはCT-AI試験に悩んでいますか？試験に合格できないことを心配していますか？我々の提供した一番新しくて全面的なISTQBのCT-AI問題集はあなたのすべての需要を満たすことができます。資格をもらうのはあなたの発展の第一歩で、我々のCT-AI日本語対策はあなたを助けて試験に合格して資格をもらうことができます。
質問 # 138
Which ONE of the following is a factor associated with the test data that can create challenges specific to testing AI based systems?
正解：D
解説：
The correct answer is B . The CT-AI syllabus identifies several challenges associated with test data for AI- based systems. One important challenge is that if testers use the same implementation as the data scientists for data acquisition and data pre-processing, defects in those steps may be masked.
Therefore, obtaining test data that has not already been pre-processed by the system under test is important when the purpose is to test the data pipeline and reveal possible defects in acquisition, transformation, cleaning, feature preparation, or formatting. Option A is not the best answer because public benchmark datasets can be useful for comparison or initial evaluation, but using them is not inherently a test- data challenge. Option C may be impractical for high-volume AI data, but it is not the most syllabus-specific risk stated here. Option D may raise privacy or representativeness concerns, but it is less precise than the data- pipeline masking issue. The core testing risk is that pre-processed data can bypass the very processing steps whose quality the test should evaluate.
References/topics: CT-AI Syllabus Chapter 7, Section 7.3 "Test Data for Testing AI-Based Systems"; Section 7.2.1 "Input Data Testing."
=========
質問 # 139
A wildlife conservation group would like to use a neural network to classify images of different animals. The algorithm is going to be used on a social media platform to automatically pick out pictures of the chosen animal of the month. This month's animal is set to be a wolf. The test team has already observed that the algorithm could classify a picture of a dog as being a wolf because of the similar characteristics between dogs and wolves. To handle such instances, the team is planning to train the model with additional images of wolves and dogs so that the model is able to better differentiate between the two. What test method should you use to verify that the model has improved after the additional training?
正解：D
解説：
The syllabus defines back-to-back testing as a method to compare a modified AI system to the previous version, which is ideal in this scenario:
"Back-to-back testing is performed by comparing the outputs of two systems that are supposed to provide the same outputs, one being a known and trusted system and the other being the test system. This approach can be used to test ML systems after re-training to verify that improvements have not introduced regressions."
質問 # 140
Data used for an object detection ML system was found to have been labelled incorrectly in many cases.
Which ONE of the following options is most likely the reason for this problem?
正解：B
解説：
Accuracy Issues: The primary goal of labeling data in machine learning is to ensure that the model can accurately learn and make predictions based on the given labels. Incorrectly labeled data directly impacts the model's accuracy, leading to poor performance because the model learns incorrect patterns.
質問 # 141
Which ONE of the following models BEST describes a way to model defect prediction by looking at the history of bugs in modules by using code quality metrics of modules of historical versions as input?
SELECT ONE OPTION
正解：A
解説：
Defect prediction models aim to identify parts of the software that are likely to contain defects by analyzing historical data and code quality metrics. The primary goal is to use this predictive information to allocate testing and maintenance resources effectively. Let's break down why option D is the correct choice:
* Understanding Classification Models:
* Classification models are a type of supervised learning algorithm used to categorize or classify data into predefined classes or labels. In the context of defect prediction, the classification model would classify parts of the code as either "defective" or "non-defective" based on the input features.
* Input Data - Code Quality Metrics:
* The input data for these classification models typically includes various code quality metrics such as cyclomatic complexity, lines of code, number of methods, depth of inheritance, coupling between objects, etc. These metrics help the model learn patterns associated with defects.
* Historical Data:
* Historical versions of the code along with their defect records provide the labeled data needed for training the classification model. By analyzing this historical data, the model can learn which metrics are indicative of defects.
* Why Option D is Correct:
* Option D specifies using a classification model to predict the presence of defects by using code quality metrics as input data. This accurately describes the process of defect prediction using historical bug data and quality metrics.
* Eliminating Other Options:
* A. Identifying the relationship between developers and the modules developed by them:
This does not directly involve predicting defects based on code quality metrics and historical data.
* B. Search of similar code based on natural language processing: While useful for other purposes, this method does not describe defect prediction using classification models and code metrics.
* C. Clustering of similar code modules to predict based on similarity: Clustering is an unsupervised learning technique and does not directly align with the supervised learning approach typically used in defect prediction models.
References:
* ISTQB CT-AI Syllabus, Section 9.5, Metamorphic Testing (MT), describes various testing techniques including classification models for defect prediction.
* "Using AI for Defect Prediction" (ISTQB CT-AI Syllabus, Section 11.5.1).
質問 # 142
The activation value output for a neuron in a neural network is obtained by applying computation to the neuron.
Which ONE of the following options BEST describes the inputs used to compute the activation value?
SELECT ONE OPTION
正解：B
解説：
In a neural network, the activation value of a neuron is determined by a combination of inputs from the previous layer, the weights of the connections, and the bias at the neuron level. Here's a detailed breakdown:
Inputs for Activation Value:
Activation Values of Neurons in the Previous Layer: These are the outputs from neurons in the preceding layer that serve as inputs to the current neuron.
Weights Assigned to the Connections: Each connection between neurons has an associated weight, which determines the strength and direction of the input signal.
Individual Bias at the Neuron Level: Each neuron has a bias value that adjusts the input sum, allowing the activation function to be shifted.
Calculation:
The activation value is computed by summing the weighted inputs from the previous layer and adding the bias.
Formula: z=∑(wiai)+bz = \sum (w_i \cdot a_i) + bz=∑(wiai)+b, where wiw_iwi are the weights, aia_iai are the activation values from the previous layer, and bbb is the bias.
The activation function (e.g., sigmoid, ReLU) is then applied to this sum to get the final activation value.
Why Option A is Correct:
Option A correctly identifies all components involved in computing the activation value: the individual bias, the activation values of the previous layer, and the weights of the connections.
Eliminating Other Options:
B . Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons: This option misses the bias, which is crucial.
C . Individual bias at the neuron level, and weights assigned to the connections between the neurons: This option misses the activation values from the previous layer.
D . Individual bias at the neuron level, and activation values of neurons in the previous layer: This option misses the weights, which are essential.
Reference:
ISTQB CT-AI Syllabus, Section 6.1, Neural Networks, discusses the components and functioning of neurons in a neural network.
"Neural Network Activation Functions" (ISTQB CT-AI Syllabus, Section 6.1.1).
質問 # 143
......
CertShikenを選択したら100%CT-AI試験に合格することができます。試験科目の変化によって、最新のCT-AI試験の内容も更新いたします。CertShikenのインターネットであなたに年24時間のオンライン顧客サービスを無料で提供して、もしあなたはCertShikenに失敗したら、弊社が全額で返金いたします。
CT-AI勉強方法: https://www.certshiken.com/CT-AI-shiken.html
2026年CertShiken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=1cklwr1h5lvrPfms0n4drP_DUO0C5wGkq