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| Certification Vendor: | ISTQB |
|---|---|
| Exam Name: | ISTQB Certified Tester - AI Testing Exam |
| Exam Number: | CT-AI |
| Related Certifications: | ISTQB Certified Tester Foundation Level (CTFL) ISTQB Certified Tester Testing with Generative AI (CT-GenAI) |
| Exam Format: | 1-2 points per question, Multiple-choice questions |
| Exam Price: | โฌ180 - โฌ250 (varies by region and provider) |
| Real Exam Qty: | 40 |
| Available Languages: | German, English, Portuguese, Chinese, French, Korean, Spanish, Japanese |
| Exam Duration: | 60 (75 for non-native language) |
| Certificate Validity Period: | Valid indefinitely (no expiration) |
| Passing Score: | 65% (29/44 points for v2.0; 31/47 points for v1.0) |
| Recommended Training: | CT-AI Syllabus v2.0 ISTQB Accredited Training Providers |
| Exam Registration: | ISTQB Official Registration Pearson VUE iSQI Exam Registration |
| Sample Questions: | ISTQB CT-AI Sample Questions |
| Exam Way: | Online remote proctored / Onsite test center |
| Pre Condition: | Must hold ISTQB Certified Tester Foundation Level (CTFL) certification |
| Official Syllabus URL: | https://istqb.org/certifications/certified-tester-ai-testing-ct-ai/ |
>> CT-AI Reliable Test Labs <<
We all know that CT-AI learning guide can help us solve learning problems. But if it is too complex, not only canโt we get good results, but also the burden of students' learning process will increase largely. Unlike those complex and esoteric materials, our CT-AI Preparation prep is not only of high quality, but also easy to learn. For our professional experts simplified the content of theCT-AI exam questions for all our customers to be understood.
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NEW QUESTION # 44
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?
Answer: A
Explanation:
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.
NEW QUESTION # 45
Which statement regarding AI for defect prediction is correct?
Choose ONE option (1 out of 4)
Answer: C
Explanation:
Section5.3 - AI Support for Defect Predictionof the ISTQB CT-AI syllabus explains that AI-based defect prediction models rely onhistorical patterns, including past defects, code behavior, and similar system configurations. ML models trained on prior defect data can identifycomponents likely to contain defects when new changes resemble previous defect-inducing patterns. This directly supports OptionA, which states that defect prediction is most effective when based on previous similar constellations.
Option B is incorrect: ML can predictwhich componentsare likely to fail, not only whether defects exist.
Option C is incomplete; code metrics help, but defect prediction relies onmanycontextual features (historical defects, code churn, commit frequency, etc.). Option D is wrong because defect prediction isnotbased on formal principles and typically requiresmany features, not just a few.
Thus,Option Ais the correct and syllabus-consistent answer.
NEW QUESTION # 46
A facial recognition system is being deployed at airports in order to scan passengers' faces and compare them to a database of vaccinations, in order to identify unvaccinated passengers in a pandemic.
There are a number of components involved including cameras, a model to segment the image, and a model to identify the face and match it against a known photograph. It is important that there are few false negatives, and that passengers cannot subvert the system.
Which ONE of the following types of testing is the MOST appropriate options for the tests you would choose in system testing?
Answer: B
Explanation:
Adversarial testing is the most appropriate approach in this case because the system needs to be robust against attempts to subvert it (e.g., by using masks, photos, or other methods to deceive the system). Adversarial testing specifically focuses on identifying vulnerabilities where attackers may try to manipulate or bypass the system's security or functionality, ensuring that the facial recognition system is resilient against such tactics.
NEW QUESTION # 47
Which AI-specific test objective and acceptance criterion should be selected MOST LIKELY for testing GPT_Legal?
Choose ONE option (1 out of 4)
Answer: B
Explanation:
The ISTQB CT-AI syllabus introducesAI-specific quality characteristics, includingevolution,functional safety, compatibility, andbias-related data quality. Section5.1 - AI-Specific Test Objectivesexplains thatevolutionrefers to an AI system's capability to continue improving or at least maintain performance as it undergoes additional training. GPT_Legal is explicitly described as aself-learning systemexpected to:
continuously reduce false positives,
achieve weekly accuracy improvements of 10%,
reach and maintain 90% accuracy,
adapt to new environments (patent law firm # corporate legal department).
This aligns perfectly with the syllabus definition ofevidence of evolution: ensuring the model doesnot degradeas additional training data is introduced. OptionBtherefore directly supports the described acceptance criteria for this evolving, self-learning application.
Option A (functional safety) is irrelevant because patent searching and drafting do not constitute safety- critical domains. Option C (compatibility) is necessary but not the primary AI-specific objective. Option D addresses bias, which is important but not central to the described performance and continuous-learning expectations.
Thus,Option Bis the most appropriate AI-specific test objective.
NEW QUESTION # 48
Which ONE of the following combinations of Training, Validation, Testing data is used during the process of learning/creating the model?
Answer: B
Explanation:
The process of developing a machine learning model typically involves the use of three types of datasets:
Training Data:This is used to train the model, i.e., to learn the patterns and relationships in the data.
Validation Data:This is used to tune the model's hyperparameters and to prevent overfitting during the training process.
Test Data:This is used to evaluate the final model's performance and to estimate how it will perform on unseen data.
Let's analyze each option:
A). Training data - validation data - test data
This option correctly includes all three types of datasets used in the process of creating and validating a model. The training data is used for learning, validation data for tuning, and test data for final evaluation.
B). Training data - validation data
This option misses the test data, which is crucial for evaluating the model's performance on unseen data after the training and validation phases.
C). Training data - test data
This option misses the validation data, which is important for tuning the model and preventing overfitting during training.
D). Validation data - test data
This option misses the training data, which is essential for the initial learning phase of the model.
Therefore, the correct answer is A because it includes all necessary datasets used during the process of learning and creating the model: training, validation, and test data.
NEW QUESTION # 49
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