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CT-AI인증자료 - CT-AI높은통과율시험자료

요즘같이 시간인즉 금이라는 시대에 시간도 절약하고 빠른 시일 내에 학습할 수 있는 ExamPassdump의 덤프를 추천합니다. 귀중한 시간절약은 물론이고 한번에ISTQB CT-AI인증시험을 패스함으로 여러분의 발전공간을 넓혀줍니다.

ExamPassdump의 연구팀에서는ISTQB CT-AI인증덤프만 위하여 지금까지 노력해왔고 ExamPassdump 학습가이드ISTQB CT-AI덤프로 시험이 어렵지 않아졌습니다. ExamPassdump는 100%한번에ISTQB CT-AI이장시험을 패스할 것을 보장하며 우리가 제공하는 문제와 답을 시험에서 백프로 나올 것입니다.여러분이ISTQB CT-AI시험에 응시하여 우리의 도움을 받는다면 ExamPassdump에서는 꼭 완벽한 자료를 드릴 것을 약속합니다. 또한 일년무료 업데이트서비스를 제공합니다.즉 문제와 답이 갱신이 되었을 경우 우리는 여러분들한테 최신버전의 문제와 답을 다시 보내드립니다.

>> CT-AI인증자료 <<

ISTQB CT-AI높은 통과율 시험자료, CT-AI최고품질 덤프자료

IT업계에 종사하는 분이라면 국제적으로 인정받는 IT인증시험에 도전하여 자격증을 취득하셔야 합니다. ExamPassdump의 ISTQB인증 CT-AI덤프는 이 시험에 참가한 IT인사들의 검증을 받은 최신 시험대비 공부자료입니다. ExamPassdump의 ISTQB인증 CT-AI덤프로 시험을 쉽게 패스하여 자격증을 취득하면 승진이나 연봉인상에 많은 편리를 가져다드립니다. 저희는 항상 여러분들의 곁을 지켜줄것입니다.

ISTQB CT-AI 시험요강:

주제
소개

주제 1

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 2

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 3

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

주제 4

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 5

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 6

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

주제 7

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.

최신 ISTQB AI Testing CT-AI 무료샘플문제 (Q11-Q16):

질문 # 11
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

A. 1,0.9, 0.8
B. 1,0.87,0.84
C. 0.84.1,0.9
D. 0.87.0.9. 0.84

정답：D

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} = 0.9Recall=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.
Reference:
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 # 12
A software component uses machine learning to recognize the digits from a scan of handwritten numbers. In the scenario above, which type of Machine Learning (ML) is this an example of?
SELECT ONE OPTION

A. Clustering
B. Reinforcement learning
C. Regression
D. Classification

정답：D

설명：
Recognizing digits from a scan of handwritten numbers using machine learning is an example of classification. Here's a breakdown:
* Classification: This type of machine learning involves categorizing input data into predefined classes.
In this scenario, the input data (handwritten digits) are classified into one of the 10 digit classes (0-9).
* Why Not Other Options:
* Reinforcement Learning: This involves learning by interacting with an environment to achieve a goal, which does not fit the problem of recognizing digits.
* Regression: This is used for predicting continuous values, not discrete categories like digit recognition.
* Clustering: This involves grouping similar data points together without predefined classes, which is not the case here.
References:The explanation is based on the definitions of different machine learning types as outlined in the ISTQB CT-AI syllabus, specifically under supervised learning and classification.

질문 # 13
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 excluding specific faulty conditions
C. Selecting training data by purposely including all known faulty conditions
D. Selecting testing data from a boat manufacturer's bolt longevity data

정답：B

설명：
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:

질문 # 14
A team of software testers is attempting to create an AI algorithm to assist in software testing. This particular team has gone through over 40 iterations of testing and cannot afford to spend as much time as it takes to run the full regression test suite. They are hoping to have the algorithm reduce the amount of testing required thus reducing the time needed for each testing cycle.
How can an AI-based tool be expected to assist in this reduction?

A. By performing bayesian analysis to estimate the types of human interactions that are expected to be seen in the system and then selecting those test cases
B. By performing optimization of the data from past iterations to see where the most common defects occurred and select the corresponding test cases
C. By using a clustering method to quantify the relationships between test cases and then assigning each test case to a category
D. By using A/B testing to compare the last update with the newest change and compare metrics between the two

정답：B

설명：
AI-based tools can significantly optimize regression test suites by analyzing historical data, past test results, associated defects, and changes made to the software. These tools prioritize and select the most relevant test cases based on previous defect patterns and frequently failing features, which helps in reducing the test execution time while maintaining effectiveness.
The optimization process involves:
* Prioritizing test cases:AI-based tools rank test cases based on past defect detection trends, ensuring that the most relevant tests are executed first.
* Reducing redundant test cases:The tool can eliminate test cases that do not contribute significantly to defect detection, reducing overall test execution time.
* Augmenting test cases:The AI can also suggest new test cases if certain features are more prone to defects.
This approach has been proven to reduce regression test suite sizes by up to 50% while maintaining fault detection capabilities.
* Section 11.4 - Using AI for the Optimization of Regression Test Suitesstates that AI-based tools can optimize regression test suites by analyzing past test data and defect occurrences, leading to significant reductions in test execution time.
Reference from ISTQB Certified Tester AI Testing Study Guide:

질문 # 15
Data used for an object detection ML system was found to have been labelled incorrectly in many cases.
Which ONE of the following options is most likely the reason for this problem?
SELECT ONE OPTION

A. Bias issues
B. Privacy issues
C. Accuracy issues
D. Security issues

정답：C

설명：
The question refers to a problem where data used for an object detection ML system was labelled incorrectly. This issue is most closely related to "accuracy issues." Here's a detailed explanation:
Accuracy Issues: The primary goal of labeling data in machine learning is to ensure that the model can accurately learn and make predictions based on the given labels. Incorrectly labeled data directly impacts the model's accuracy, leading to poor performance because the model learns incorrect patterns.
Why Not Other Options:
Security Issues: This pertains to data breaches or unauthorized access, which is not relevant to the problem of incorrect data labeling.
Privacy Issues: This concerns the protection of personal data and is not related to the accuracy of data labeling.
Bias Issues: While bias in data can affect model performance, it specifically refers to systematic errors or prejudices in the data rather than outright incorrect labeling.

질문 # 16
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ISTQB인증 CT-AI시험은 등록하였는데 시험준비는 아직이라구요? ISTQB인증 CT-AI시험일이 다가오고 있는데 공부를 하지 않아 두려워 하고 계시는 분들은 이 글을 보는 순간 시험패스에 자신을 가지게 될것입니다. 시험준비 시간이 적다고 하여 패스할수 없는건 아닙니다. ExamPassdump의ISTQB인증 CT-AI덤프와의 근사한 만남이ISTQB인증 CT-AI패스에 화이팅을 불러드립니다. 덤프에 있는 문제만 공부하면 되기에 시험일이 며칠뒤라도 시험패스는 문제없습니다. 더는 공부하지 않은 자신을 원망하지 마시고 결단성있게ExamPassdump의ISTQB인증 CT-AI덤프로 시험패스에 고고싱하세요.

CT-AI높은 통과율 시험자료: https://www.exampassdump.com/CT-AI_valid-braindumps.html

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