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CT-AI시험준비공부시험준비에가장좋은인기덤프공부

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.

ISTQB CT-AI 시험요강:

주제	소개
주제 1	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 2	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 3	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 4	systems from those required for conventional systems.
주제 5	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 6	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
주제 7	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 8	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 9	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.

 

>> CT-AI시험준비공부 <<

CT-AI덤프데모문제 다운 - CT-AI시험대비 최신버전 문제

여러분은 먼저 우리 ExamPassdump사이트에서 제공되는ISTQB인증CT-AI시험덤프의 일부분인 데모를 다운받으셔서 체험해보세요. ExamPassdump는 여러분이 한번에ISTQB인증CT-AI시험을 패스하도록 하겠습니다. 만약ISTQB인증CT-AI시험에서 떨어지셨다고 하면 우리는 덤프비용전액 환불입니다.

최신 ISTQB AI Testing CT-AI 무료샘플문제 (Q62-Q67):

질문 # 62
Which ONE of the following tests is LEAST likely to be performed during the ML model testing phase?
SELECT ONE OPTION

• A. Testing the speed of the training of the model.
• B. Testing the API of the service powered by the ML model.
• C. Testing the speed of the prediction by the model.
• D. Testing the accuracy of the classification model.

정답：A

설명：
The question asks which test is least likely to be performed during the ML model testing phase. Let's consider each option:
Testing the accuracy of the classification model (A): Accuracy testing is a fundamental part of the ML model testing phase. It ensures that the model correctly classifies the data as intended and meets the required performance metrics.
Testing the API of the service powered by the ML model (B): Testing the API is crucial, especially if the ML model is deployed as part of a service. This ensures that the service integrates well with other systems and that the API performs as expected.
Testing the speed of the training of the model (C): This is least likely to be part of the ML model testing phase. The speed of training is more relevant during the development phase when optimizing and tuning the model. During testing, the focus is more on the model's performance and behavior rather than how quickly it was trained.
Testing the speed of the prediction by the model (D): Testing the speed of prediction is important to ensure that the model meets performance requirements in a production environment, especially for real-time applications.
Reference:
ISTQB CT-AI Syllabus Section 3.2 on ML Workflow and Section 5 on ML Functional Performance Metrics discuss the focus of testing during the model testing phase, which includes accuracy and prediction speed but not the training speed.

 

질문 # 63
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 testing data from a boat manufacturer's bolt longevity data
• C. Selecting training data by purposely excluding specific faulty conditions
• D. Selecting training data by purposely including all known faulty conditions

정답：C

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 # 64
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. 1,0.87,0.84
• B. 0.87.0.9. 0.84
• C. 1,0.9, 0.8
• D. 0.84.1,0.9

정답：B

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 # 65
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. I and II
• B. Only II
• C. I and III
• D. Only III

정답：A

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 # 66
Which ONE of the following options describes a scenario of A/B testing the LEAST?
SELECT ONE OPTION

• A. A comparison of two different offers in a recommendation system to decide on the more effective offer for same users.
• B. A comparison of the performance of two different ML implementations on the same input data.
• C. A comparison of the performance of an ML system on two different input datasets.
• D. A comparison of two different websites for the same company to observe from a user acceptance perspective.

정답：C

설명：
A/B testing, also known as split testing, is a method used to compare two versions of a product or system to determine which one performs better. It is widely used in web development, marketing, and machine learning to optimize user experiences and model performance. Here's why option C is the least descriptive of an A/B testing scenario:
* Understanding A/B Testing:
* In A/B testing, two versions (A and B) of a system or feature are tested against each other. The objective is to measure which version performs better based on predefined metrics such as user engagement, conversion rates, or other performance indicators.
* Application in Machine Learning:
* In ML systems, A/B testing might involve comparing two different models, algorithms, or system configurations on the same set of data to observe which yields better results.
* Why Option C is the Least Descriptive:
* Option C describes comparing the performance of an ML system on two different input datasets.
This scenario focuses on the input data variation rather than the comparison of system versions or features, which is the essence of A/B testing. A/B testing typically involves a controlled experiment with two versions being tested under the same conditions, not different datasets.
* Clarifying the Other Options:
* A. A comparison of two different websites for the same company to observe from a user acceptance perspective: This is a classic example of A/B testing where two versions of a website are compared.
* B. A comparison of two different offers in a recommendation system to decide on the more effective offer for the same users: This is another example of A/B testing in a recommendation system.
* D. A comparison of the performance of two different ML implementations on the same input data: This fits the A/B testing model where two implementations are compared under the same conditions.
References:
* ISTQB CT-AI Syllabus, Section 9.4, A/B Testing, explains the methodology and application of A/B testing in various contexts.
* "Understanding A/B Testing" (ISTQB CT-AI Syllabus).

 

질문 # 67
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