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ISTQB CT-AI높은통과율시험공부자료 & CT-AI높은통과율덤프샘플문제

ISTQB 인증 CT-AI시험이 너무 어려워서 시험 볼 엄두도 나지 않는다구요? ExamPassdump 덤프만 공부하신다면 IT인증시험공부고민은 이젠 그만 하셔도 됩니다. ExamPassdump에서 제공해드리는ISTQB 인증 CT-AI시험대비 덤프는 덤프제공사이트에서 가장 최신버전이여서 시험패스는 한방에 갑니다. ISTQB 인증 CT-AI시험뿐만 아니라 IT인증시험에 관한 모든 시험에 대비한 덤프를 제공해드립니다. 많은 애용 바랍니다.

ExamPassdump 는 아주 우수한 IT인증자료사이트입니다. 우리ExamPassdump에서 여러분은ISTQB CT-AI인증시험관련 스킬과시험자료를 얻을수 있습니다. 여러분은 우리ExamPassdump 사이트에서 제공하는ISTQB CT-AI관련자료의 일부분문제와답등 샘플을 무료로 다운받아 체험해볼 수 있습니다. 그리고ExamPassdump에서는ISTQB CT-AI자료구매 후 추후 업데이트되는 동시에 최신버전을 무료로 발송해드립니다. 우리는ISTQB CT-AI인증시험관련 모든 자료를 여러분들에서 제공할 것입니다. 우리의 IT전문 팀은 부단한 업계경험과 연구를 이용하여 정확하고 디테일 한 시험문제와 답으로 여러분을 어시스트 해드리겠습니다.

>> ISTQB CT-AI높은 통과율 시험공부자료 <<

CT-AI높은 통과율 시험공부자료 최신 시험 최신 덤프

ExamPassdump전문가들은ISTQB CT-AI인증시험만을 위한 특별학습가이드를 만들었습니다.ISTQB CT-AI인증시험을 응시하려면 30분이란 시간만 투자하여 특별학습가이드로 빨리 관련지식을 장악하고,또 다시 복습하고 안전하게ISTQB CT-AI인증시험을 패스할 수 잇습니다.자격증취득 많은 시간과 돈을 투자한 분들보다 더 가볍게 이루어졌습니다

ISTQB CT-AI 시험요강:

주제
소개

주제 1

systems from those required for conventional systems.

주제 2

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 3

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 4

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.

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

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 (Q12-Q17):

질문 # 12
Arihant Meditation is a startup using Al to aid people in deeper and better meditation based on analysis of various factors such as time and duration of the meditation, pulse and blood pressure, EEG patters etc. among others. Their model accuracy and other functional performance parameters have not yet reached their desired level.
Which ONE of the following factors is NOT a factor affecting the ML functional performance?
SELECT ONE OPTION

A. The quality of the labeling
B. Biased data
C. The number of classes
D. The data pipeline

정답：C

설명：
* Factors Affecting ML Functional Performance: The data pipeline, quality of the labeling, and biased data are all factors that significantly affect the performance of machine learning models. The number of classes, while relevant for the model structure, is not a direct factor affecting the performance metrics such as accuracy or bias.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Data Quality and its Effect on the ML Model and ML Functional Performance Metrics.

질문 # 13
A company is using a spam filter to attempt to identify which emails should be marked as spam. Detection rules are created by the filter that causes a message to be classified as spam. An attacker wishes to have all messages internal to the company be classified as spam. So, the attacker sends messages with obvious red flags in the body of the email and modifies the from portion of the email to make it appear that the emails have been sent by company members. The testers plan to use exploratory data analysis (EDA) to detect the attack and use this information to prevent future adversarial attacks.
How could EDA be used to detect this attack?

A. EDA can detect and remove the false emails.
B. EDA cannot be used to detect the attack.
C. EDA can restrict how many inputs can be provided by unique users.
D. EDA can help detect the outlier emails from the real emails.

정답：D

설명：
Exploratory Data Analysis (EDA) is an essential technique for examining datasets to uncover patterns, trends, and anomalies, including outliers. In this case, the attacker manipulates the spam filter by injecting emails with red flags and masking them as internal company emails. The primary goal of EDA here is to detect these adversarial modifications.
* Detecting Outliers:
* EDA techniques such as statistical analysis, clustering, and visualization can reveal patterns in email metadata (e.g., sender details, email content, frequency).
* Outlier detection methods like Z-score, IQR (Interquartile Range), or machine learning-based anomaly detection can identify emails that significantly deviate from typical internal communications.
* Identifying Distribution Shifts:
* By analyzing the frequency and characteristics of emails flagged as spam, testers can detect if the attack has introduced unusual patterns.
* If a surge of internal emails is suddenly classified as spam, EDA can help verify whether these classifications are consistent with historical data.
* Feature Analysis for Adversarial Patterns:
* EDA enables visualization techniques such as scatter plots or histograms to distinguish normal emails from manipulated ones.
* Examining email metadata (e.g., changes in headers, unusual wording in email bodies) can reveal adversarial tactics.
* Counteracting Adversarial Attacks:
* Once anomalies are identified, the spam filter's detection rules can be improved by retraining the model on corrected datasets.
* The adversarial examples can be added to the training data to enhance the robustness of the filter against future attacks.
* Exploratory Data Analysis (EDA) is used to detect outliers and adversarial attacks."EDA is where data are examined for patterns, relationships, trends, and outliers. It involves the interactive, hypothesis-driven exploration of data."
* EDA can identify poisoned or manipulated data by detecting anomalies and distribution shifts.
"Testing to detect data poisoning is possible using EDA, as poisoned data may show up as outliers."
* EDA helps validate ML models and detect potential vulnerabilities."The use of exploratory techniques, primarily driven by data visualization, can help validate the ML algorithm being used, identify changes that result in efficient models, and leverage domain expertise." References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, as EDA is specifically useful for detecting outliers, which can help identify manipulated spam emails.

질문 # 14
An image classification system is being trained for classifying faces of humans. The distribution of the data is
70% ethnicity A and 30% for ethnicities B, C and D. Based ONLY on the above information, which of the following options BEST describes the situation of this image classification system?
SELECT ONE OPTION

A. This is an example of sample bias.
B. This is an example of expert system bias.
C. This is an example of algorithmic bias.
D. This is an example of hyperparameter bias.

정답：A

설명：
* A. This is an example of expert system bias.
* Expert system bias refers to bias introduced by the rules or logic defined by experts in the system, not by the data distribution.
* B. This is an example of sample bias.
* Sample bias occurs when the training data is not representative of the overall population that the model will encounter in practice. In this case, the over-representation of ethnicity A (70%) compared to B, C, and D (30%) creates a sample bias, as the model may become biased towards better performance on ethnicity A.
* C. This is an example of hyperparameter bias.
* Hyperparameter bias relates to the settings and configurations used during the training process, not the data distribution itself.
* D. This is an example of algorithmic bias.
* Algorithmic bias refers to biases introduced by the algorithmic processes and decision-making rules, not directly by the distribution of training data.
Based on the provided information, optionB(sample bias) best describes the situation because the training data is skewed towards ethnicity A, potentially leading to biased model performance.

질문 # 15
There is a growing backlog of unresolved defects for your project. You know the developers have an ML model that they have created which has learned which developers work on which type of software and the speed with which they resolve issues. How could you use this model to help reduce the backlog and implement more efficient defect resolution?

A. Use it to review the code and determine where more defects are likely to occur so that testing can be targeted to those areas.
B. Use it to prioritize defects automatically based on the time expected for the fix to be made, the speed of the fix, and the likelihood of regressions.
C. Use it to assign defects to the best developer to resolve the problem and to load balance the defect assignments among the developers.
D. Use it to determine the root cause of each defect and develop a process improvement plan that can be implemented to remove the most common root causes.

정답：C

설명：
AI and ML models can play a significant role in optimizing defect resolution processes. According to the ISTQB Certified Tester AI Testing (CT-AI) Syllabus, ML models can be used toanalyze defect reports, prioritize critical defects, and assign defects to developersbased on historical defect resolution patterns.
The key AI applications for defect management include:
* Defect Categorization- NLP techniques can analyze defect reports and classify them based on metadata like severity and impact.
* Defect Prioritization- ML models trained on past defects can predict which issues are likely to cause failures, allowing teams toprioritizethe most critical issues.
* Defect Assignment- AI-based models can suggest which developers are best suited for specific defects, optimizing the resolution process based on past performance and specialization.
From the given answer choices:
* Option A (Automatic Prioritization)is useful but does not directlyreduce backlog efficientlyby considering developer expertise and workload balancing.
* Option C (Root Cause Analysis for Process Improvement)is along-term strategybut does not directly address backlog reduction.
* Option D (Defect Prediction for Testing Focus)helps preemptively identify issues but does not resolve the existing backlog.
Thus,Option Bis the best choice as it aligns with AI's capability toassign defects to the most suitable developersbased on historical data, ensuring efficient defect resolution and backlog reduction.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 11.2 (Using AI to Analyze Reported Defects)
* ISTQB CT-AI Syllabus v1.0, Section 11.5 (Using AI for Defect Prediction).

질문 # 16
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

A. Only II
B. I and II
C. Only III
D. I and III

정답：B

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 # 17
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ExamPassdump 의 엘리트는 다년간 IT업계에 종사한 노하우로 높은 적중율을 자랑하는 ISTQB CT-AI덤프를 연구제작하였습니다. 한국어 온라인서비스가 가능하기에 ISTQB CT-AI덤프에 관하여 궁금한 점이 있으신 분은 구매전 문의하시면 됩니다. ISTQB CT-AI덤프로 시험에서 좋은 성적 받고 자격증 취득하시길 바랍니다.

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

Mia Anderson Mia Anderson

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