CT-AI최신시험대비자료덤프자료는Certified Tester AI Testing Exam최고의시험대비자료

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참고: Itexamdump에서 Google Drive로 공유하는 무료, 최신 CT-AI 시험 문제집이 있습니다: https://drive.google.com/open?id=1feaK0sv1A-SSQKnAheZleniyML8c0a1X

많은 사이트에서ISTQB 인증CT-AI 인증시험대비자료를 제공하고 있습니다. 그중에서 Itexamdump를 선택한 분들은ISTQB 인증CT-AI시험통과의 지름길에 오른것과 같습니다. Itexamdump는 시험에서 불합격성적표를 받으시면 덤프비용을 환불하는 서비스를 제공해드려 아무런 걱정없이 시험에 도전하도록 힘이 되어드립니다. Itexamdump덤프를 사용하여 시험에서 통과하신 분이 전해주신 희소식이 Itexamdump 덤프품질을 증명해드립니다.

ISTQB CT-AI 시험요강:

주제소개
주제 1
  • Neural Networks and Testing: This section of the exam covers defining the structure and function of a neural network including a DNN and the different coverage measures for neural networks.
주제 2
  • 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 3
  • ML: Data: This section of the exam covers explaining the activities and challenges related to data preparation. It also covers how to test datasets create an ML model and recognize how poor data quality can cause problems with the resultant ML model.
주제 4
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
주제 5
  • Testing AI-Based Systems Overview: In this section, focus is given to how system specifications for AI-based systems can create challenges in testing and explain automation bias and how this affects testing.
주제 6
  • 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 7
  • ML Functional Performance Metrics: In this section, the topics covered include how to calculate the ML functional performance metrics from a given set of confusion matrices.
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.

>> CT-AI최신 시험대비자료 <<

CT-AI인증시험덤프, CT-AI인기자격증 덤프공부자료

ISTQB CT-AI인증시험을 패스하려면 시험대비자료선택은 필수입니다. 우리Itexamdump에서는 빠른 시일 내에ISTQB CT-AI관련 자료를 제공할 수 있습니다. Itexamdump의 전문가들은 모두 경험도 많고, 그들이 연구자료는 실제시험의 문제와 답과 거이 일치합니다. Itexamdump 는 인증시험에 참가하는 분들한테 편리를 제공하는 사이트이며,여러분들이 시험패스에 도움을 줄 수 있는 사이트입니다.

최신 ISTQB AI Testing CT-AI 무료샘플문제 (Q98-Q103):

질문 # 98
You are a test manager planning testing for an invoice financing company. The company buys unpaid invoices from companies and provides them with immediate cash.
The company is replacing their existing conventional system, which takes company accounting records as inputs, with an ML system that classifies each invoice for sales as something that should, or should not be bought. Significant historical production data is available. It is important that invoices are not bought incorrectly.
Which ONE of the following test techniques would be MOST appropriate for you to plan for system testing?

정답：C

설명：
Back-to-back testing is the most appropriate technique in this scenario. It involves comparing the outputs of the new machine learning system with the outputs of the existing conventional system, using the same input data. This approach allows to verify that the ML system performs at least as well as the existing system, particularly in avoiding incorrect purchases of invoices, which is crucial for the company's operations.


질문 # 99
Which ONE of the following options BEST DESCRIBES clustering?
SELECT ONE OPTION

정답：C

설명：
Clustering is a type of machine learning technique used to group similar data points into clusters. It is a key concept in unsupervised learning, where the algorithm tries to find patterns or groupings in data without prior knowledge of output classes. Let's analyze each option:
A . Clustering is classification of a continuous quantity.
This is incorrect. Classification typically involves discrete categories, whereas clustering involves grouping similar data points. Classification of continuous quantities is generally referred to as regression.
B . Clustering is supervised learning.
This is incorrect. Clustering is an unsupervised learning technique because it does not rely on labeled data.
C . Clustering is done without prior knowledge of output classes.
This is correct. In clustering, the algorithm groups data points into clusters without any prior knowledge of the classes. It discovers the inherent structure in the data.
D . Clustering requires you to know the classes.
This is incorrect. Clustering does not require prior knowledge of classes. Instead, it aims to identify and form the classes or groups based on the data itself.
Therefore, the correct answer is C because clustering is an unsupervised learning technique done without prior knowledge of output classes.


질문 # 100
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.
References:
* 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 # 101
Upon testing a model used to detect rotten tomatoes, the following data was observed by the test engineer, based on certain number of tomato images.

For this confusion matrix which combinations of values of accuracy, recall, and specificity respectively is CORRECT?
SELECT ONE OPTION

정답：C

설명：
To calculate the accuracy, recall, and specificity from the confusion matrix provided, we use the following formulas:
* Confusion Matrix:
* Actually Rotten: 45 (True Positive), 8 (False Positive)
* Actually Fresh: 5 (False Negative), 42 (True Negative)
* Accuracy:
* Accuracy is the proportion of true results (both true positives and true negatives) in the total population.
* Formula: Accuracy=TP+TNTP+TN+FP+FN ext{Accuracy} = rac{TP + TN}{TP + TN + FP + FN}Accuracy=TP+TN+FP+FNTP+TN
* Calculation: Accuracy=45+4245+42+8+5=87100=0.87 ext{Accuracy} = rac{45 + 42}{45 + 42
+ 8 + 5} = rac{87}{100} = 0.87Accuracy=45+42+8+545+42=10087=0.87
* Recall (Sensitivity):
* Recall is the proportion of true positive results in the total actual positives.
* Formula: Recall=TPTP+FN ext{Recall} = rac{TP}{TP + FN}Recall=TP+FNTP
* Calculation: Recall=4545+5=4550=0.9 ext{Recall} = rac{45}{45 + 5} = rac{45}{50} = 0.9 Recall=45+545=5045=0.9
* Specificity:
* Specificity is the proportion of true negative results in the total actual negatives.
* Formula: Specificity=TNTN+FP ext{Specificity} = rac{TN}{TN + FP}Specificity=TN+FPTN
* Calculation: Specificity=4242+8=4250=0.84 ext{Specificity} = rac{42}{42 + 8} = rac{42}{50} = 0.84Specificity=42+842=5042=0.84 Therefore, the correct combinations of accuracy, recall, and specificity are 0.87, 0.9, and 0.84 respectively.
References:
* ISTQB CT-AI Syllabus, Section 5.1, Confusion Matrix, provides detailed formulas and explanations for calculating various metrics including accuracy, recall, and specificity.
* "ML Functional Performance Metrics" (ISTQB CT-AI Syllabus, Section 5).


질문 # 102
A local business has a mail pickup/delivery robot for their office. The robot currently uses a track to move between pickup/drop off locations. When it arrives at a destination, the robot stops to allow a human to remove or deposit mail.
The office has decided to upgrade the robot to include AI capabilities that allow the robot to perform its duties without a track, without running into obstacles, and without human intervention.
The test team is creating a list of new and previously established test objectives and acceptance criteria to be used in the testing of the robot upgrade. Which of the following test objectives will test an AI quality characteristic for this system?

정답：D

설명：
AI-based systems have specific quality characteristics, includingevolution,autonomy, andadaptability. A test objective that evaluates whether an AI systemevolvesto improve performance over time directly aligns with AI quality characteristics.
Explanation of Answer Choices:
* Option A: The robot must evolve to optimize its routing.
* Correct.Evolution is an AI quality characteristic that ensures the systemlearns from past experiencesand adapts to improve efficiency.
* Option B: The robot must recharge for no more than six hours a day.
* Incorrect.This is an operational constraint rather than an AI-specific quality characteristic.
* Option C: The robot must record the time of each delivery which is compiled into a report.
* Incorrect.Logging data does not relate to AI quality characteristics likeadaptability or autonomy.
* Option D: The robot must complete 99.99% of its deliveries each day.
* Incorrect.This is a performance target rather than an AI quality characteristic.
ISTQB CT-AI Syllabus References:
* Evolution as an AI Quality Characteristic:"Check how well the system learns from its own experience. Check how well the system copes when the profile of data changes (i.e., concept drift)".
Thus,Option A is the best choice as it directly tests an AI quality characteristic (evolution) in the upgraded autonomous robot.


질문 # 103
......

현재 많은 IT인사들이 같은 생각하고 잇습니다. 그것은 바로ISTQB CT-AI인증시험자격증 취득으로 하여 IT업계의 아주 중요한 한걸음이라고 말입니다.그만큼ISTQB CT-AI인증시험의 인기는 말 그대로 하늘을 찌르고 잇습니다,

CT-AI인증시험덤프: https://www.itexamdump.com/CT-AI.html

2026 Itexamdump 최신 CT-AI PDF 버전 시험 문제집과 CT-AI 시험 문제 및 답변 무료 공유: https://drive.google.com/open?id=1feaK0sv1A-SSQKnAheZleniyML8c0a1X

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