さらに、CertShiken CT-AIダンプの一部が現在無料で提供されています：https://drive.google.com/open?id=1eAyNKMR_CGbyfvyW15K4q-EwRed2EO_R
あなたはまだ試験について心配していますか？ 心配しないで！CertShiken CT-AI試験トレントは、作業または学習プロセス中にこの障害を克服するのに役立ちます。 CT-AIテスト準備の指示の下で、非常に短時間でタスクを完了し、間違いなく試験に合格してCT-AI証明書を取得できます。 ISTQBサービスをさまざまな個人に合わせて調整し、わずか20〜30時間の練習とトレーニングの後、目的の試験に参加できるようにします。 さらに、理論と内容に関してCertified Tester AI Testing Examクイズトレントを毎日更新する専門家がいます。
| Section | Weight | Objectives |
|---|---|---|
| Model Testing for Machine Learning Systems | 20% | - Metamorphic and statistical testing - Model performance and accuracy testing - Adversarial and robustness testing - Drift detection and monitoring - Explainability and transparency testing |
| Test Approaches and Techniques for AI Systems | 10% | - Test environment and tool considerations - Risk-based testing - A/B testing and back-to-back testing - Exploratory testing and red teaming |
| Testing Generative AI and Large Language Models | 15% | - Evaluation of generative AI outputs - Generative AI fundamentals - Specific risks and test approaches for LLMs |
| Test Levels and Machine Learning Systems | 15% | - Unit, integration, system, and acceptance testing for ML - Testing across ML lifecycle stages - Test strategies for AI projects |
| Introduction to Testing AI-Based Systems | 10% | - Characteristics of AI and ML systems - AI-specific quality characteristics - Challenges in testing AI-based systems |
| Machine Learning Development Testing | 10% | - Regression testing for retrained models - Testing MLOps and deployment pipelines - Testing ML development workflows |
| Input Data Testing for Machine Learning Systems | 20% | - Label verification and ground truth assessment - Validation of data pipelines and preprocessing - Testing for bias, representativeness, and completeness - Data quality attributes |
もちろん、資格試験を審査するとき、非公開にすることはできません。テストCT-AI認定に関連する新しいポリシーと情報に注意する必要があります。ユーザーの便宜を図るため、ホームページでCT-AIテスト資料を更新し、資格試験に関連する情報をタイムリーに更新します。年次認定試験は、内容はほぼ同じですが、各年のポリシーとして、対応する試験パターンのグレーディング基準とホットスポットが変更されます。CT-AIテスト準備は、ユーザーが最短時間で合格するのに役立ちます。試験。
質問 # 76
You are testing an autonomous vehicle which uses AI to determine proper driving actions and responses. You have evaluated the parameters and combinations to be tested and have determined that there are too many to test in the time allowed. It has been suggested that you use pairwise testing to limit the parameters. Given the complexity of the software under test, what is likely the outcome from using pairwise testing?
正解：B
解説：
The syllabus states that while pairwise testing is effective at finding defects by reducing the number of test cases needed, the resulting test suite can still be extensive and require automation:
"Even the use of pairwise testing can result in extensive test suites... automation and virtual test environments often become necessary to allow the required tests to be run."
質問 # 77
Which ONE of the following hardware is MOST suitable for implementing Al when using ML?
SELECT ONE OPTION
正解：D
解説：
A . 64-bit CPUs.
While 64-bit CPUs are essential for handling large amounts of memory and performing complex computations, they are not specifically optimized for the types of operations commonly used in machine learning.
B . Hardware supporting fast matrix multiplication.
Matrix multiplication is a fundamental operation in many machine learning algorithms, especially in neural networks and deep learning. Hardware optimized for fast matrix multiplication, such as GPUs (Graphics Processing Units), is most suitable for implementing AI and ML because it can handle the parallel processing required for these operations efficiently.
C . High powered CPUs.
High powered CPUs are beneficial for general-purpose computing tasks and some aspects of ML, but they are not as efficient as specialized hardware like GPUs for matrix multiplication and other ML-specific tasks.
D . Hardware supporting high precision floating point operations.
High precision floating point operations are important for scientific computing and some specific AI tasks, but for many ML applications, fast matrix multiplication is more critical than high precision alone.
Therefore, the correct answer is B because hardware supporting fast matrix multiplication, such as GPUs, is most suitable for the parallel processing requirements of machine learning.
質問 # 78
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：C
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 # 79
Which statement regarding AI for defect prediction is correct?
Choose ONE option (1 out of 4)
正解：D
解説：
Section5.3 - AI Support for Defect Predictionof the ISTQB CT-AI syllabus explains that AI-based defect prediction models rely onhistorical patterns, including past defects, code behavior, and similar system configurations. ML models trained on prior defect data can identifycomponents likely to contain defects when new changes resemble previous defect-inducing patterns. This directly supports OptionA, which states that defect prediction is most effective when based on previous similar constellations.
Option B is incorrect: ML can predictwhich componentsare likely to fail, not only whether defects exist.
Option C is incomplete; code metrics help, but defect prediction relies onmanycontextual features (historical defects, code churn, commit frequency, etc.). Option D is wrong because defect prediction isnotbased on formal principles and typically requiresmany features, not just a few.
Thus,Option Ais the correct and syllabus-consistent answer.
質問 # 80
Which of the following statements about ML functional performance metrics is correct?
Choose ONE option (1 out of 4)
正解：A
解説：
The ISTQB CT-AI syllabus explains ML performance metrics in Section3.2 - Evaluating ML Models. For clustering, which is an unsupervised learning method, the syllabus lists metrics such asintra-cluster distance, inter-cluster distance, and coherence measures. Intra-cluster metrics evaluate how close data points are within a cluster, which directly corresponds to Option A.
Option B is incorrect becauseR-squaredis a regression metric measuring goodness-of-fit, not classification performance, and has no connection to ROC curves. Option C is wrong because thesilhouette coefficientis also a clustering metric, measuring cohesion vs. separation-not regression accuracy. Option D is incorrect because ROC curves evaluatebinary or multiclass classification, not clustering.
Thus, OptionAis the only accurate statement based on the syllabus.
質問 # 81
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数千人の専門家で構成された権威ある制作チームが、CT-AI学習の質問を理解し、質の高い学習体験を楽しんでいます。試験概要と現在のポリシーの最近の変更に応じて、CT-AIテストガイドの内容を随時更新します。また、CT-AI試験の質問は、わかりにくい概念を簡素化して学習方法を最適化し、習熟度を高めるのに役立ちます。
CT-AI予想試験: https://www.certshiken.com/CT-AI-shiken.html
さらに、CertShiken CT-AIダンプの一部が現在無料で提供されています：https://drive.google.com/open?id=1eAyNKMR_CGbyfvyW15K4q-EwRed2EO_R