Prepare with ISQI CT-AI_v1.0_World exam torrent, pass for sure
Updated: Oct 05, 2026
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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 |
| Available Languages: | English |
| Related Certifications: | ISTQB Certified Tester Foundation Level (CTFL) |
| Certificate Validity Period: | Lifetime (no expiry) |
| Exam Duration: | 60 minutes |
| Exam Format: | Multiple-choice questions |
| Sample Questions: | ISQI CT-AI_v1.0_World Sample Questions |
| 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 |
|---|---|
| Machine Learning Fundamentals for Testers | - Supervised and unsupervised learning concepts - Model training and evaluation basics |
| AI in Test Automation | - AI-supported test generation - AI-based defect prediction and analytics |
| Introduction to AI in Software Testing | - AI systems overview - Role of AI in testing |
| AI System Quality Risks | - Ethical and legal considerations - Model drift and performance degradation |
| Testing AI-Based Systems | - Data quality and dataset validation - Bias, fairness, and explainability testing - Testing ML models |
ISQI ISTQB Certified Tester AI Testing (v1.0) Sample Questions:
Which ONE of the following describes a situation of back-to-back testing the LEAST?
SELECT ONE OPTION
- A. 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.
- B. Comparison of the results of the current neural network ML model on the current data set with a slightly modified data set.
- C. 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
- D. Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.
Correct Answer: D 🗳️
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"AllerEgo" is a product that uses sell-learning to predict the behavior of a pilot under combat situation for a variety of terrains and enemy aircraft formations. Post training the model was exposed to the real- world data and the model was found to bebehaving poorly. A lot of data quality tests had been performed on the data to bring it into a shape fit for training and testing.
Which ONE of the following options is least likely to describes the possible reason for the fall in the performance, especially when considering the self-learning nature of the Al system?
SELECT ONE OPTION
- A. The fast pace of change did not allow sufficient time for testing.
- B. There was an algorithmic bias in the Al system.
- C. The difficulty of defining criteria for improvement before the model can be accepted.
- D. The unknown nature and insufficient specification of the operating environment might have caused the poor performance.
Correct Answer: C 🗳️
Explanation: Only visible for Test4Sure members. You can sign-up / login (it's free).
Which ONE of the following statements is a CORRECT adversarial example in the context of machine learning systems that are working on image classifiers.
SELECT ONE OPTION
- A. Black box attacks based on adversarial examples create an exact duplicate model of the original.
- B. These examples are model specific and are not likely to cause another model trained on same task to fail.
- C. These attack examples cause a model to predict the correct class with slightly less accuracy even though they look like the original image.
- D. These attacks can't be prevented by retraining the model with these examples augmented tothe training data.
Correct Answer: B 🗳️
Explanation: Only visible for Test4Sure members. You can sign-up / login (it's free).
Pairwise testing can be used in the context of self-driving cars for controlling an explosion in the number of combinations of parameters.
Which ONE of the following options is LEAST likely to be a reason for this incredible growth of parameters?
SELECT ONE OPTION
- A. ML model metrics to evaluate the functional performance
- B. Different features like ADAS, Lane Change Assistance etc.
- C. Different weather conditions
- D. Different Road Types
Correct Answer: A 🗳️
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