그리고 Itexamdump CT-AI 시험 문제집의 전체 버전을 클라우드 저장소에서 다운로드할 수 있습니다: https://drive.google.com/open?id=15_vNFRgOXtGJVbrWYQh7axc7psWd8fvD
저희 Itexamdump의 덤프 업데이트시간은 업계에서 가장 빠르다고 많은 덤프구매자 분들께서 전해주셨습니다. ISTQB CT-AI 덤프도 마찬가지 입니다. 저희는 수시로 덤프업데이트 가능성을 체크하여 덤프를 항상 시중에서 가장 최신버전이 될수있도록 최선을 다하고 있습니다. 구매후 1년무료업데이트서비스를 해드리기에 구매후에도 덤프유효성을 최대한 연장해드립니다.
| Section | Weight | Objectives |
|---|---|---|
| Input Data Testing for Machine Learning Systems | 20% | - Testing for bias, representativeness, and completeness - Data quality attributes - Validation of data pipelines and preprocessing - Label verification and ground truth assessment |
| Testing Generative AI and Large Language Models | 15% | - Specific risks and test approaches for LLMs - Evaluation of generative AI outputs - Generative AI fundamentals |
| Introduction to Testing AI-Based Systems | 10% | - Characteristics of AI and ML systems - Challenges in testing AI-based systems - AI-specific quality characteristics |
| Test Approaches and Techniques for AI Systems | 10% | - Risk-based testing - Test environment and tool considerations - Exploratory testing and red teaming - A/B testing and back-to-back testing |
| Model Testing for Machine Learning Systems | 20% | - Model performance and accuracy testing - Drift detection and monitoring - Metamorphic and statistical testing - Explainability and transparency testing - Adversarial and robustness testing |
| Test Levels and Machine Learning Systems | 15% | - Test strategies for AI projects - Unit, integration, system, and acceptance testing for ML - Testing across ML lifecycle stages |
| Machine Learning Development Testing | 10% | - Testing ML development workflows - Regression testing for retrained models - Testing MLOps and deployment pipelines |
Itexamdump의ISTQB인증 CT-AI덤프는 인터넷에서 검색되는ISTQB인증 CT-AI시험공부자료중 가장 출중한 시험준비 자료입니다. ISTQB인증 CT-AI덤프를 공부하면 시험패스는 물론이고 IT지식을 더 많이 쌓을수 있어 일거량득입니다.자격증을 취득하여 자신있게 승진하여 연봉협상하세요.
질문 # 102
Consider an AI system in which the complex internal structure has been generated by another software system. Why would the tester choose to do black-box testing on this particular system?
정답：B
설명：
In AI-based systems, particularly those where theinternal structure has been generated by another software system, the complexity often makes it difficult for human testers to analyze the inner workings. As per the ISTQB Certified Tester AI Testing (CT-AI) Syllabus:
* Black-box testingis particularly useful when dealing with AI systems that have been generated by another system because:
* It allows testingwithout requiring knowledge of the internal logic.
* The AI model may be too complex for human testers to comprehend, making white-box testing ineffective.
* Black-box testing evaluates theinputs and outputs, ensuring functional correctnesswithout needing insight into how the system reaches a decision.
* Why other options are incorrect?
* A (Test automation and black-box testing): While automation is possible,black-box testing is not primarily about automationbut aboutabstracting the internal complexity.
* B (Understanding the logic of the software): This contradicts the premise of black-box testing, which is designed totest functionality without needing to understandthe inner workings.
* C (Checking transparency of the algorithm):Black-box testing does not check algorithm transparency-that would requirewhite-box testing or explainability techniques.
Thus, the best choice isOption D, as black-box testingremoves the need to analyze the internal structure of AI systems, making it the most appropriate testing method in this case.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 8.5 (Challenges Testing Complex AI-Based Systems)
* ISTQB CT-AI Syllabus v1.0, Section 8.6 (Testing the Transparency, Interpretability, and Explainability of AI-Based Systems)
질문 # 103
A data scientist is performing unsupervised learning on a set of financial records relating to previous loan applications, and trying to predict defaults on future loans. They are reporting poor functional performance because of data issues.
Which ONE of the below is LEAST likely to be a contributory factor?
정답：A
설명：
Irrelevant data included in the account records is less likely to contribute to poor functional performance in unsupervised learning, especially compared to missing records, missing key data (such as whether loans were granted or repaid), or inconsistent pre-processing. While irrelevant data can affect the quality of the model, missing or inconsistent data typically has a more direct negative impact on unsupervised learning models.
질문 # 104
A system was developed for screening the X-rays of patients for potential malignancy detection (skin cancer).
A workflow system has been developed to screen multiple cancers by using several individually trained ML models chained together in the workflow.
Testing the pipeline could involve multiple kind of tests (I - III):
I.Pairwise testing of combinations
II.Testing each individual model for accuracy
III.A/B testing of different sequences of models
Which ONE of the following options contains the kinds of tests that would be MOST APPROPRIATE to include in the strategy for optimal detection?
SELECT ONE OPTION
정답：D
설명：
The question asks which combination of tests would be most appropriate to include in the strategy for optimal detection in a workflow system using multiple ML models.
* Pairwise testing of combinations (I): This method is useful for testing interactions between different components in the workflow to ensure they work well together, identifying potential issues in the integration.
* Testing each individual model for accuracy (II): Ensuring that each model in the workflow performs accurately on its own is crucial before integrating them into a combined workflow.
* A/B testing of different sequences of models (III): This involves comparing different sequences to determine which configuration yields the best results. While useful, it might not be as fundamental as pairwise and individual accuracy testing in the initial stages.
:
ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing and Section 9.3 on Testing ML Models emphasize the importance of testing interactions and individual model accuracy in complex ML workflows.
질문 # 105
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
정답：B
설명：
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).
질문 # 106
Which ONE of the below is MOST likely to be a challenge in ML data preparation?
정답：B
설명：
The correct answer is A . The CT-AI syllabus identifies data preparation as one of the most resource-intensive parts of the ML workflow, stating that it uses an average of 43% of ML workflow effort. It also explains that data preparation forms part of the data pipeline and includes acquisition, pre-processing, feature engineering, and related exploratory analysis.
The syllabus lists several data preparation challenges, including the need for domain and data knowledge, difficulty obtaining high-quality data from multiple sources, difficulty automating a scalable and efficient production data pipeline, costs associated with data preparation, defects introduced into the data pipeline, and sample bias.
Option B, functional performance, is a result or evaluation concern for the ML model rather than a data preparation challenge itself. Option C, designing a reward function, is specifically a reinforcement learning challenge. Option D, specifying acceptance criteria, belongs primarily to understanding objectives and defining evaluation targets, not the data preparation activity.
References/topics: CT-AI Syllabus Chapter 4, Section 4.1 "Data Preparation as Part of the ML Workflow"; Section 4.1.1 "Challenges in Data Preparation."
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질문 # 107
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Itexamdump의ISTQB CT-AI덤프는 레알시험의 모든 유형을 포함하고 있습니다.객관식은 물론 드래그앤드랍,시뮬문제등 실제시험문제의 모든 유형을 포함하고 있습니다. ISTQB CT-AI덤프의 문제와 답은 모두 엘리트한 인증강사 및 전문가들에 의하여 만들어져ISTQB CT-AI 시험응시용만이 아닌 학습자료용으로도 손색이 없는 덤프입니다.저희 착한ISTQB CT-AI덤프 데려가세용~!
CT-AI시험대비 덤프 최신버전: https://www.itexamdump.com/CT-AI.html
참고: Itexamdump에서 Google Drive로 공유하는 무료, 최신 CT-AI 시험 문제집이 있습니다: https://drive.google.com/open?id=15_vNFRgOXtGJVbrWYQh7axc7psWd8fvD