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시험패스가능한CT-AI최신버전시험덤프문제덤프최신문제

퍼펙트한ISTQB CT-AI시험대비덤프자료는 Itexamdump가 전문입니다. ISTQB CT-AI덤프를 다운받아 가장 쉬운 시험준비를 하여 한방에 패스가는것입니다. 다같이 ISTQB CT-AI덤프로 시험패스에 주문걸어 보아요. 마술처럼ISTQB CT-AI시험합격이 실현될것입니다.

ISTQB CT-AI시험패스는 어려운 일이 아닙니다. Itexamdump의 ISTQB CT-AI 덤프로 시험을 쉽게 패스한 분이 헤아릴수 없을 만큼 많습니다. ISTQB CT-AI덤프의 데모를 다운받아 보시면 구매결정이 훨씬 쉬워질것입니다. 하루 빨리 덤프를 받아서 시험패스하고 자격증 따보세요.

>> CT-AI최신버전 시험덤프문제 <<

CT-AI인기자격증 & CT-AI적중율 높은 덤프공부

Itexamdump의ISTQB인증 CT-AI덤프공부가이드에는ISTQB인증 CT-AI시험의 가장 최신 시험문제의 기출문제와 예상문제가 정리되어 있어ISTQB인증 CT-AI시험을 패스하는데 좋은 동반자로 되어드립니다. ISTQB인증 CT-AI시험에서 떨어지는 경우ISTQB인증 CT-AI덤프비용전액 환불신청을 할수 있기에 보장성이 있습니다.시험적중율이 떨어지는 경우 덤프를 빌려 공부한 것과 같기에 부담없이 덤프를 구매하셔도 됩니다.

ISTQB CT-AI 시험요강:

주제
소개

주제 1

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

주제 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.

주제 3

Introduction to AI: This exam section covers topics such as the AI effect and how it influences the definition of AI. It covers how to distinguish between narrow AI, general AI, and super AI; moreover, the topics covered include describing how standards apply to AI-based systems.

주제 4

systems from those required for conventional systems.

주제 5

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

주제 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.

주제 7

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.

주제 8

Quality Characteristics for AI-Based Systems: This section covers topics covered how to explain the importance of flexibility and adaptability as characteristics of AI-based systems and describes the vitality of managing evolution for AI-based systems. It also covers how to recall the characteristics that make it difficult to use AI-based systems in safety-related applications.

최신 ISTQB AI Testing CT-AI 무료샘플문제 (Q73-Q78):

질문 # 73
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?

A. The black-box testing method will allow the tester to check the transparency of the algorithm used to create the internal structure
B. Test automation can be built quickly and easily from the test cases developed during black-box testing
C. Black-box testing eliminates the need for the tester to understand the internal structure of the AI-system
D. The tester wishes to better understand the logic of the software used to create the internal structure

정답：C

설명：
The syllabus explains:
"Where the internal structure of an AI-based system is too complex for humans to understand, the system can only be tested as a black box. Even when the internal structure is visible, this provides no additional useful information to help with testing." This confirms that black-box testing is chosen because the tester does not need to understand the system's internal structure.
(Reference: ISTQB CT-AI Syllabus v1.0, Section 8.5, page 61 of 99)

질문 # 74
An e-commerce developer built an application for automatic classification of online products in order to allow customers to select products faster. The goal is to provide more relevant products to the user based on prior purchases.
Which of the following factors is necessary for a supervised machine learning algorithm to be successful?

A. Grouping similar products together before feeding them into the algorithm
B. Selecting the correct data pipeline for the ML training
C. Labeling the data correctly
D. Minimizing the amount of time spent training the algorithm

정답：C

설명：
Supervised machine learning requires correctly labeled data to train an effective model. The learning process relies on input-output mappings where each training example consists of an input (features) and a correctly labeled output (target variable). Incorrect labeling can significantly degrade model performance.
* Supervised Learning Process
* The algorithm learns from labeled data, mapping inputs to correct outputs during training.
* If labels are incorrect, the model will learn incorrect relationships and produce unreliable predictions.
* Quality of Training Data
* The accuracy of any supervised ML model ishighly dependent on the quality of labels.
* Poorly labeled data leads to mislabeled training sets, resulting inbiased or underperforming models.
* Error Minimization and Model Accuracy
* Incorrectly labeled data affects theconfusion matrix, reducing precision, recall, and accuracy.
* It leads to overfitting or underfitting, which decreases the model's ability to generalize.
* Industry Standard Practices
* Many AI development teams spend a significant amount of time ondata annotation and quality controlto ensure high-quality labeled datasets.
* (B) Minimizing the amount of time spent training the algorithm#(Incorrect)
* While reducing training time is important for efficiency, the quality of training is more critical. A well-trained model takes time to process large datasets and optimize its parameters.
* (C) Selecting the correct data pipeline for the ML training#(Incorrect)
* A good data pipeline helps, butit does not directly impact learning successas much as labeling does.Even a well-optimized pipeline cannot fix incorrect labels.
* (D) Grouping similar products together before feeding them into the algorithm#(Incorrect)
* This describesclustering, which is anunsupervised learning technique. Supervised learningrequires labeled examples, not just grouping of data.
* Labeled data is necessary for supervised learning."For supervised learning, it is necessary to have properly labeled data."
* Data labeling errors can impact performance."Supervised learning assumes that the data is correctly labeled by the data annotators.However, it is rare in practice for all items in a dataset to be labeled correctly." Why Labeling is Critical?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, ascorrectly labeled data is essential for supervised machine learning success.

질문 # 75
Which of the following is a technique used in machine learning?

A. Decision trees
B. Equivalence partitioning
C. Decision tables
D. Boundary value analysis

정답：A

설명：
Decision trees are a widely usedmachine learning (ML) techniquethat falls undersupervised learning. They are used for bothclassification and regressiontasks and are popular due to their interpretability and effectiveness.
* How Decision Trees Work:
* The model splits the dataset into branches based on feature conditions.
* It continues to divide the data until each subset belongs to a single category (classification) or predicts a continuous value (regression).
* The final result is a tree structure where decisions are made atnodes, and predictions are given at leaf nodes.
* Common Applications of Decision Trees:
* Fraud detection
* Medical diagnosis
* Customer segmentation
* Recommendation systems
* B (Equivalence Partitioning):This is asoftware testing technique, not a machine learning method. It is used to divide input data into partitions to reduce test cases while maintaining coverage.
* C (Boundary Value Analysis):Anothersoftware testing technique, used to check edge cases around input boundaries.
* D (Decision Tables):A structuredtesting techniqueused to validate business rules and logic, not a machine learning method.
* ISTQB CT-AI Syllabus (Section 3.1: Forms of Machine Learning - Decision Trees)
* "Decision trees are used in classification and regression models and are fundamental ML algorithms".
Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Sincedecision trees are a core technique in machine learning, while the other options are software testing techniques, thecorrect answer is A.

질문 # 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?

A. The number of parameters to test can be reduced to less than a dozen
B. Pairwise cannot be applied to this problem because there is AI involved and the evolving values may result in unexpected results that cannot be verified
C. All high priority defects will be identified using this method
D. While the number of tests needed can be reduced, there may still be a large enough set of tests that automation will be required to execute all of them

정답：D

설명：
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." (Reference: ISTQB CT-AI Syllabus v1.0, Section 9.2, Page 67 of 99)

질문 # 77
Which of the following is an example of a clustering problem that can be resolved by unsupervised learning?

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

정답：A

설명：
The syllabus defines clustering as:
"Clustering: This is when the problem requires the identification of similarities in input data points that allows them to be grouped based on common characteristics or attributes. For example, clustering is used to categorize different types of customers for the purpose of marketing." (Reference: ISTQB CT-AI Syllabus v1.0, Section 3.1.2, page 26 of 99)

질문 # 78
......

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