Exam Name: ISTQB Certified Tester AI Testing (v1.0)
Certification Provider: ISQI
Corresponding Certification: AI Testing
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ISQI CT-AI_v1.0_World Exam Overview:
| Certification Vendor: | iSQI / ISTQB |
|---|---|
| Exam Name: | ISTQB Certified Tester AI Testing (v1.0) |
| Exam Number: | CT-AI_v1.0 |
| Related Certifications: | ISTQB Certified Tester Foundation Level (CTFL) |
| Available Languages: | English |
| Certificate Validity Period: | Lifetime (no expiry) |
| Exam Duration: | 60 minutes |
| Exam Format: | Multiple-choice questions |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | Online or onsite proctored exam via accredited providers (e.g., iSQI) |
| Pre Condition: | ISTQB Foundation Level certification recommended |
ISQI CT-AI_v1.0_World Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| AI System Quality Risks | - Ethical and legal considerations - Model drift and performance degradation |
| Machine Learning Fundamentals for Testers | - Supervised and unsupervised learning concepts - Model training and evaluation basics |
| Introduction to AI in Software Testing | - Role of AI in testing - AI systems overview |
| Testing AI-Based Systems | - Testing ML models - Data quality and dataset validation - Bias, fairness, and explainability testing |
| AI in Test Automation | - AI-based defect prediction and analytics - AI-supported test generation |
Clear Answers for ISQI ISTQB Certified Tester AI Testing (v1.0) Candidates
Immediately and easily. Upon successful payment, our system sends the product file to your mailbox automatically — typically within about a minute — with an instant download link as well. If nothing arrives within two hours, check your spam folder and contact support. Installations are unlimited, the PDF supports printing for paper-based review, and your purchase includes 365 days of free updates: whenever the exam content changes, the latest version reaches you automatically, with a 50% renewal discount after the year ends.
The ISQI ISTQB Certified Tester AI Testing (v1.0) blueprint covers these principal domains:
- Testing AI-Based Systems
- AI in Test Automation
- AI System Quality Risks
The remaining domains appear in the full official outline, all of which our bank addresses.
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ISQI recommends these official training options:
Pick the course matching your experience, then reinforce it with regular question practice.
ISQI lists the following prerequisites for the ISQI ISTQB Certified Tester AI Testing (v1.0): ISTQB Foundation Level certification recommended.
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ISQI ISTQB Certified Tester AI Testing (v1.0) Sample Questions:
A ML engineer is trying to determine the correctness of the new open-source implementation *X", of a supervised regression algorithm implementation. R-Square is one of the functional performance metrics used to determine the quality of the model.
Which ONE of the following would be an APPROPRIATE strategy to achieve this goal?
SELECT ONE OPTION
- A. Compare the R-Square score of the model obtained using two different implementations that utilize two different programming languages while using the same algorithm and the same training and testing data.
- B. Add 10% of the rows randomly and create another model and compare the R-Square scores of both the model.
- C. Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.
- D. Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.
Correct Answer: A 🗳️
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Which ONE of the following approaches to labelling requires the least time and effort?
SELECT ONE OPTION
- A. Outsourced
- B. Internal
- C. Al-Assisted
- D. Pre-labeled dataset
Correct Answer: D 🗳️
Explanation: Only visible for Prep4away members. You can sign-up / login (it's free).
An image classification system is being trained for classifying faces of humans. The distribution of the data is
70% ethnicity A and 30% for ethnicities B, C and D. Based ONLY on the above information, which of the following options BEST describes the situation of this image classification system?
SELECT ONE OPTION
- A. This is an example of algorithmic bias.
- B. This is an example of sample bias.
- C. This is an example of expert system bias.
- D. This is an example of hyperparameter bias.
Correct Answer: B 🗳️
Explanation: Only visible for Prep4away members. You can sign-up / login (it's free).
Which ONE of the following describes a situation of back-to-back testing the LEAST?
SELECT ONE OPTION
- A. Comparison of the results of the current neural network ML model on the current data set with a slightly modified data set.
- B. Comparison of the results of a current neural network model ML model implemented in platform A (for example Pytorch) with a similar neural network model ML model implemented in platform B (for example Tensorflow), for the same data.
- C. Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.
- D. Comparison of the results of a home-grown neural network model ML model with results in a neural network model implemented in a standard implementation (for example Pytorch) for same data
Correct Answer: C 🗳️
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