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ハイパスレートのCT-AI資料勉強一回合格-一番優秀なCT-AI試験合格攻略

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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

AI ベースシステムのテストの概要: このセクションでは、AI ベースシステムのシステム仕様がテストでどのような課題を生み出す可能性があるかに焦点を当て、自動化のバイアスとそれがテストにどのように影響するかについて説明します。

トピック 2

ニューラルネットワークとテスト: この試験のセクションでは、DNN を含むニューラルネットワークの構造と機能の定義、およびニューラルネットワークのさまざまなカバレッジ測定について説明します。

トピック 3

AI 入門: この試験セクションでは、AI 効果やそれが AI の定義に与える影響などのトピックを取り上げます。狭義の AI、汎用 AI、スーパー AI を区別する方法を取り上げます。さらに、AI ベースのシステムに標準がどのように適用されるかを説明するトピックも取り上げます。

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

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 6

従来のシステムに必要なものとは異なるシステムです。

トピック 7

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 8

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 9

AI ベースシステムのテストの方法とテクニック: このセクションでは、ML システムのテストが敵対的攻撃やデータ汚染の防止にどのように役立つかを説明することに重点を置いています。

トピック 10

AI 固有の品質特性のテスト: このセクションでは、AI ベースのシステムの自己学習によって生じるテストの課題について説明します。

>> CT-AI資料勉強 <<

効率的なCT-AI資料勉強 & 合格スムーズCT-AI試験合格攻略 | 認定するCT-AI最新試験情報

多くの会社はISTQB認証の有無によって社員の給料が違います。それに、CT-AI試験に参加したことがない人にとって、これはいい挑戦です。我々の更新された問題集は多くの受験者を助けました。あなたはCT-AI試験を準備しているなら、我々の最新の問題集を利用して復習することができます。

ISTQB Certified Tester AI Testing Exam 認定 CT-AI 試験問題 (Q81-Q86):

質問 # 81
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

A. Using a classification model to predict the presence of a defect by using code quality metrics as the input data.
B. Clustering of similar code modules to predict based on similarity.
C. Search of similar code based on natural language processing.
D. Identifying the relationship between developers and the modules developed by them.

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.
Reference:
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 # 82
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

A. Individual bias at the neuron level, and activation values of neurons in the previous layer.
B. Individual bias at the neuron level, and weights assigned to the connections between the neurons.
C. Individual bias at the neuron level, activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.
D. Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.

正解：C

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 # 83
Which ONE of the following options represents a technology MOST TYPICALLY used to implement Al?
SELECT ONE OPTION

A. Procedural programming
B. Search engines
C. Genetic algorithms
D. Case control structures

正解：C

解説：
* Technology Most Typically Used to Implement AI: Genetic algorithms are a well-known technique used in AI . They are inspired by the process of natural selection and are used to find approximate solutions to optimization and search problems. Unlike search engines, procedural programming, or case control structures, genetic algorithms are specifically designed for evolving solutions and are commonly employed in AI implementations.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 1.4 AI Technologies, which identifies different technologies used to implement AI.

質問 # 84
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 testing data from a different dataset than the training dataset
B. Selecting training data by purposely including all known faulty conditions
C. Selecting testing data from a boat manufacturer's bolt longevity data
D. Selecting training data by purposely excluding specific faulty conditions

正解：D

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 # 85
Which of the following is an example of overfitting?

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

正解：C

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 # 86
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2025年It-Passports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=1X0fpogKTtor61BiUsEfkuUkMG5yJ4oTV

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