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| Certification Vendor: | ISTQB |
|---|---|
| Exam Name: | ISTQB Certified Tester AI Testing (CT-AI) Exam |
| Exam Number: | CT-AI |
| Exam Duration: | 60 minutes |
| Available Languages: | English |
| Related Certifications: | ISTQB Certified Tester Foundation Level (CTFL) |
| Passing Score: | 65% |
| Real Exam Qty: | 40 |
| Exam Format: | Multiple Choice |
| 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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NEW QUESTION # 47
Which statement regarding the use of training, validation, and test data sets is correct?
Choose ONE option (1 out of 4)
Answer: B
Explanation:
The ISTQB CT-AI syllabus (Section3.2 - Model Evaluation) specifies the correct usage oftraining, validation, andtestdatasets. It emphasizes that thetest dataset must be representative of the real operational dataand must beequivalent in distribution to the training and validation sets, ensuring a fair and unbiased evaluation. Option D precisely matches this requirement.
Option A contradicts the syllabus because validation and test sets servedifferent purposes: validation is for tuning, test is for final evaluation. Combining them undermines the reliability of results. Option B is incorrect because even with limited data, the syllabus recommends maintaining a test set or using techniques such as cross-validationrather than eliminating testing. Option C is wrong because equal distribution (33/33/33) isnot recommended; typically, the training set is much larger (e.g., 70-80%).
Thus, OptionDis the only statement aligned with the syllabus' guidance.
NEW QUESTION # 48
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?
Answer: C
NEW QUESTION # 49
You are a test manager planning testing for an invoice financing company. The company buy unpaid invoices from companies and provide them with immediate cash. The company is replacing their existing conventional system, which takes company accounting records as inputs, with an ML system that classifies each invoice for sales as something that should, or should not be bought. Significant historical production data is available. It is important that invoices are not bought incorrectly. Which ONE of the following test techniques would be MOST appropriate for you to plan for system testing?
Answer: A
Explanation:
The correct answer is B. Back-to-back testing . The scenario contains the key conditions for this technique:
an existing conventional system with similar functionality, a new ML-based replacement, and significant historical production data. The CT-AI syllabus explains that back-to-back testing uses an alternative version of the system as a pseudo-oracle , comparing its outputs with those produced by the system under test. The pseudo-oracle may be an existing system or even conventional non-AI software.
This is stronger than A/B testing because the objective is not primarily to compare two production variants statistically to determine which is better; it is to detect classification defects in a replacement system. It is also stronger than metamorphic testing because an existing oracle-like comparator is available, reducing the need to rely only on relative output relations. Experience-based testing is useful as a supplement, but it would not be the primary systematic technique where historical production inputs and an existing system are available.
References/topics: CT-AI Syllabus Chapter 9, Section 9.3 "Back-to-Back Testing"; Section 9.7
"Selecting Test Techniques for AI-Based Systems."
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NEW QUESTION # 50
Which ONE of the following options for a test basis would give the LEAST coverage when using AI-based test generation?
Answer: B
Explanation:
An XML schema would give the least coverage when using AI-based test generation. While it defines the structure of data (e.g., tags, elements), it does not provide detailed information about the application functionality or behavior, which are essential for generating meaningful tests for AI systems. The other options (test model, web pages list, and pseudo-oracle) provide more comprehensive insights into the system's behavior and logic, which are better suited for effective AI-based test generation.
NEW QUESTION # 51
Which of the following are the three activities in the data acquisition activities for data preparation?
Answer: B
Explanation:
According to the ISTQB Certified Tester AI Testing (CT-AI) syllabus, data acquisition, a critical step in data preparation for machine learning (ML) workflows, consists of three key activities:
* Identification:This step involves determining the types of data required for training and prediction. For example, in a self-driving car application, data types such as radar, video, laser imaging, and LiDAR (Light Detection and Ranging) data may be identified as necessary sources.
* Gathering:After identifying the required data types, the sources from which the data will be collected are determined, along with the appropriate collection methods. An example could be gathering financial data from the International Monetary Fund (IMF) and integrating it into an AI-based system.
* Labeling:This process involves annotating or tagging the collected data to make it meaningful for supervised learning models. Labeling is an essential activity that helps machine learning algorithms differentiate between categories and make accurate predictions.
These activities ensure that the data is suitable for training and testing machine learning models, forming the foundation of data preparation.
NEW QUESTION # 52
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