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ISTQB CT-AI Exam Overview:

Certification Vendor:	ISTQB
Exam Name:	ISTQB Certified Tester - AI Testing
Exam Number:	CT-AI
Exam Price:	EUR 250
Real Exam Qty:	40
Passing Score:	65%
Exam Format:	Multiple Choice
Exam Duration:	60 minutes
Related Certifications:	ISTQB CTAL-TTA ISTQB CTFL
Certificate Validity Period:	Lifetime (no expiration)
Available Languages:	English
Sample Questions:	ISTQB CT-AI Sample Questions
Exam Way:	Online proctored or in-person at authorized testing centers
Pre Condition:	ISTQB CTFL (Certified Tester Foundation Level) certification is recommended but not mandatory
Official Syllabus URL:	https://www.istqb.org/certifications/artificial-intelligence-testing-certification

>> CT-AI인기자격증 시험 덤프자료 <<

CT-AI시험덤프문제 & CT-AI시험대비 최신 덤프공부

최근 ISTQB인증 CT-AI시험이 IT업계에서 제일 높은 인지도를 가지고 있습니다.바라만 보지 마시고ISTQB인증 CT-AI시험에 도전해보세요. Pass4Test 의 ISTQB인증 CT-AI덤프로 시험준비공부를 하시면 한방에 시험패스 가능합니다. ISTQB인증 CT-AI덤프로 자격증취득에 가까워지고 나아가서는 IT업계에서 인정을 받는 열쇠를 소유한것과 같다고 할수 있습니다.

ISTQB CT-AI 시험요강:

주제	소개
주제 1	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 2	systems from those required for conventional systems.
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	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 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	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 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 (Q63-Q68):

질문 # 63
A mobile app start-up company is implementing an AI-based chat assistant for e-commerce customers. In the process of planning the testing, the team realizes that the specifications are insufficient.
Which testing approach should be used to test this system?

A. State transition testing
B. Exploratory testing
C. Static analysis
D. Equivalence partitioning

정답：B

설명：
When testing an AI-based chat assistant for e-commerce customers, the lack of sufficient specifications makes it difficult to use structured test techniques. The ISTQB CT-AI Syllabus recommends exploratory testing in such cases:
Exploratory testing is useful when specifications are incomplete or unclear. AI-based systems, particularly those using natural language processing (NLP), may not behave deterministically, making scripted test cases ineffective.
The tester interacts dynamically with the system, identifying unexpected behaviors not documented in the specification.

질문 # 64
When verifying that an autonomous AI-based system is acting appropriately, which of the following are MOST important to include?

A. Test cases to detect the system prompting for unnecessary human intervention
B. Test cases to verify that the system automatically suppresses invalid output data
C. Test cases to detect the system appropriately automating its data input
D. Test cases to verify that the system automatically confirms the correct classification of training data

정답：A

설명：
The syllabus highlights that testing for unnecessary human intervention is a key focus for autonomous AI- based systems:
"For autonomous AI-based systems, testers must ensure that the system does not prompt for unnecessary human intervention, as this contradicts the autonomy concept." (Reference: ISTQB CT-AI Syllabus v1.0, Section 8.2, page 59 of 99)

질문 # 65
The activation value output for a neuron in a neural network is obtained by applying computation to the neuron.
Which ONE of the following options BEST describes the inputs used to compute the activation value?

A. Individual bias at the neuron level, and activation values of neurons in the previous layer.
B. Individual bias at the neuron level, activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.
C. Individual bias at the neuron level, and weights assigned to the connections between the neurons.
D. Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.

정답：B

질문 # 66
Which of the following is an example of overfitting?

A. The model is too simplistic for the data.
B. The model is missing relationships between the inputs and outputs.
C. The model is not able to generalize to accommodate new types of data.
D. The model discards data it considers to be noise or outliers.

정답：C

설명：
Overfitting occurs when a machine learning (ML) model learns patterns that are too specific to the training data, leading to a lack of generalization for new, unseen data. This means the model performs exceptionally well on the training data but poorly on validation or test data because it has memorized the noise and minor details rather than learning the underlying patterns.
* Option A:"The model is not able to generalize to accommodate new types of data."
* This is the correct definition of overfitting. When a model cannot generalize beyond its training data, it struggles with new input, which results in overfitting.
* Option B:"The model is too simplistic for the data."
* This describes underfitting rather than overfitting. Underfitting happens when a model is too simple to capture the underlying patterns in the data.
* Option C:"The model is missing relationships between the inputs and outputs."
* This also aligns more with underfitting, where the model fails to capture important relationships in the data.
* Option D:"The model discards data it considers to be noise or outliers."
* While some ML models may ignore outliers, overfitting actually occurs when the model includes noise and outliers in its learning process rather than discarding them.
* Overfitting Definition:"Overfitting occurs when the model fits too closely to a set of data points and fails to properly generalize. It works well on training data but struggles with new data.".
* Testing for Overfitting:"Overfitting may be detected by testing the model with a dataset that is completely independent of the training dataset" Analysis of the Answer Options:ISTQB CT-AI Syllabus References:

질문 # 67
A bank wants to use an algorithm to determine which applicants should be given a loan. The bank hires a data scientist to construct a logistic regression model to predict whether the applicant will repay the loan or not.
The bank has enough data on past customers to randomly split the data into a training data set and a test
/validation data set. A logistic regression model is constructed on the training data set using the following independent variables:
Gender
Marital status
Number of dependents
Education
Income
Loan amount
Loan term
Credit score
The model reveals that those with higher credit scores and larger total incomes are more likely to repay their loans. The data scientist has suggested that there might be bias present in the model based on previous models created for other banks.
Given this information, what is the best test approach to check for potential bias in the model?

A. Experienced-based testing should be used to confirm that the training data set is operationally relevant.
This can include applying exploratory data analysis (EDA) to check for bias within the training data set.
B. A/B testing should be used to verify that the test data set does not detect any bias that might have been introduced by the original training data. If the two models significantly differ, it will indicate there is bias in the original model.
C. Back-to-back testing should be used to compare the model created using the training data set to another model created using the test data set, if the two models significantly differ, it will indicate there is bias in the original model.
D. Acceptance testing should be used to make sure the algorithm is suitable for the customer. The team can re-work the acceptance criteria such that the algorithm is sure to correctly predict the remaining applicants that have been set aside for the validation data set ensuring no bias is present.

정답：A

설명：
Bias in an AI system occurs when the training data contains inherent prejudices that cause the model to make unfair predictions. Experience-based testing, particularlyExploratory Data Analysis (EDA), helps uncover these biases by analyzing patterns, distributions, and potential discriminatory factors in the training data.
* Option A:"Experience-based testing should be used to confirm that the training data set is operationally relevant. This can include applying exploratory data analysis (EDA) to check for bias within the training data set."
* This is the correct answer. EDA involves examining the dataset for bias, inconsistencies, or missing values, ensuring fairness in ML model predictions.
* Option B:"Back-to-back testing should be used to compare the model created using the training data set to another model created using the test data set. If the two models significantly differ, it will indicate there is bias in the original model."
* Back-to-back testing is used for regression testing and to compare versions of an AI system but is not primarily used to detect bias.
* Option C:"Acceptance testing should be used to make sure the algorithm is suitable for the customer.
The team can re-work the acceptance criteria such that the algorithm is sure to correctly predict the remaining applicants that have been set aside for the validation data set ensuring no bias is present."
* Acceptance testing focuses on meeting predefined business requirements rather than detecting and mitigating bias.
* Option D:"A/B testing should be used to verify that the test data set does not detect any bias that might have been introduced by the original training data. If the two models significantly differ, it will indicate there is bias in the original model."
* A/B testing is used for evaluating variations of a model rather than for explicitly identifying bias.
* Bias Testing Methods:"AI-based systems should be tested for algorithmic bias, sample bias, and inappropriate bias. Experience-based testing and EDA are useful for detecting bias".
* Exploratory Data Analysis (EDA):"EDA helps uncover potential bias in training data through visualization and statistical analysis".
Analysis of the Answer Options:ISTQB CT-AI Syllabus References:Thus,Option A is the best choice for detecting bias in the loan applicant model.

질문 # 68
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