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ISTQB CT-AI Exam Overview:

Certification Vendor:	ISTQB
Exam Name:	ISTQB Certified Tester AI Testing (CT-AI) Exam
Exam Number:	CT-AI
Exam Format:	Multiple Choice
Exam Duration:	60 minutes
Available Languages:	English
Real Exam Qty:	40
Passing Score:	65%
Related Certifications:	ISTQB Certified Tester Foundation Level (CTFL)
Certificate Validity Period:	Lifetime
Recommended Training:	ISTQB Accredited Training Providers
Exam Registration:	ISTQB Official Website
Sample Questions:	ISTQB CT-AI Sample Questions
Exam Way:	Online and onsite proctored exam via accredited ISTQB examination providers
Pre Condition:	Recommended prior knowledge of ISTQB Foundation Level (CTFL) and basic understanding of software testing concepts and machine learning fundamentals.
Official Syllabus URL:	https://www.istqb.org

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ISTQB CT-AI Prüfungsplan:

Thema	Einzelheiten
Thema 1	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.
Thema 2	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.
Thema 3	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Thema 4	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.
Thema 5	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.
Thema 6	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.
Thema 7	systems from those required for conventional systems.
Thema 8	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.

ISTQB Certified Tester AI Testing Exam CT-AI Prüfungsfragen mit Lösungen (Q67-Q72):

67. Frage
"BioSearch" is creating an Al model used for predicting cancer occurrence via examining X-Ray images. The accuracy of the model in isolation has been found to be good. However, the users of the model started complaining of the poor quality of results, especially inability to detect real cancer cases, when put to practice in the diagnosis lab, leading to stopping of the usage of the model. A testing expert was called in to find the deficiencies in the test planning which led to the above scenario.
Which ONE of the following options would you expect to MOST likely be the reason to be discovered by the test expert?

A. The input data has not been tested for quality prior to use for testing.
B. A lack of similarity between the training and testing data.
C. A lack of focus on non-functional requirements testing.
D. A lack of focus on choosing the right functional-performance metrics.

Antwort: B

Begründung:
A lack of similarity between the training and testing data (A): This is a common issue in ML where the model performs well on training data but poorly on real-world data due to a lack of representativeness in the training data. This leads to poor generalization to new, unseen data.

68. Frage
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. Accuracy issues
B. Bias issues
C. Privacy issues
D. Security issues

Antwort: A

Begründung:
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.
References:This explanation is consistent with the concepts covered in the ISTQB CT-AI syllabus under dataset quality issues and their impact on machine learning models.

69. Frage
Which statement regarding testing transparency, explainability, or interpretability is MOST correct?

A. Since different users have different backgrounds, interpretability testing depends on the comprehensibility of the ML algorithm
B. Tests for explainability and transparency are comparable to exploratory testing and can be performed with little information about development
C. LIME can precisely state the decisive reason for a change in the output
D. Dynamic testing is one way to quantify explainability; however, each method is specific to a particular model type

Antwort: A

Begründung:
The ISTQB CT-AI syllabus states in Section2.10 - Explainability, Transparency, and Interpretability that interpretability isuser-dependent, meaning different users understand explanations differently. This is because interpretability depends not only on the ML algorithm but also on the user's domain knowledge, experience, and expectations. Option B directly reflects this syllabus principle: interpretability testing must consider user background, and explanations must be comprehensible to the intended user group.

70. Frage
An e-commerce developer built an application for automatic classification of online products in order to allow customers to select products faster. The goal is to provide more relevant products to the user based on prior purchases. Which of the following factors is necessary for a supervised machine learning algorithm to be successful?

A. Labeling the data correctly
B. Selecting the correct data pipeline for the ML training
C. Minimizing the amount of time spent training the algorithm
D. Grouping similar products together before feeding them into the algorithm

Antwort: A

Begründung:
The syllabus explains that supervised learning requires correctly labeled data so the algorithm can learn the relationship between input features and output labels:
"In supervised learning, the algorithm creates the ML model from labeled data during the training phase. The labeled data is used to infer the relationship between the input data and output labels."

71. Frage
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. 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.
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.
C. 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.
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.

Antwort: C

Begründung:
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.

72. Frage
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