Quiz 2026 ISTQB CT-AI: Professional Valid Exam Certified Tester AI Testing Exam Registration

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ISTQB CT-AI Exam Syllabus Topics:

Section	Objectives
Introduction to AI Testing	- Challenges in AI testing 1. Data dependency issues 2. Non-determinism in AI systems - AI systems overview 1. What is AI and machine learning systems 2. Differences between traditional and AI-based systems
AI System Lifecycle and Operations	- Deployment and monitoring 1. Model deployment strategies 2. Post-deployment monitoring - Continuous learning systems 1. Retraining strategies 2. Model drift detection
Data Quality and Bias	- Bias and fairness 1. Types of bias in AI systems 2. Fairness testing approaches - Data quality assurance 1. Data completeness and consistency 2. Data labeling quality
Testing AI-Based Systems	- Test design techniques 1. Metamorphic testing 2. Data-driven test design - Test levels for AI systems 1. Model testing 2. System integration testing
AI Quality Characteristics	- Quality attributes 1. Accuracy and robustness 2. Explainability and transparency
Ethics and Risk in AI Testing	- Risk-based testing for AI 1. Mitigation strategies 2. Risk identification in AI systems - Ethical considerations 1. Privacy and security concerns 2. Responsible AI principles
Machine Learning Fundamentals for Testing	- ML lifecycle 1. Training, validation, and evaluation 2. Data collection and preparation - Model types 1. Supervised and unsupervised learning 2. Deep learning basics

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q51-Q56):

NEW QUESTION # 51
Which statement about the property of the test environment for an AI-based system is correct?

A. The test environment for an AI system may need to include tools that can explain the decisions of the test object.
B. The test environment for an autonomous AI system needs to perform both test design and execution autonomously.
C. The test environment for an AI-based multi-agent system needs to act deterministically.
D. The test environment for a self-learning AI system needs to adapt to and learn from the test object.

Answer: A

Explanation:
The ISTQB CT-AI syllabus (Section4.3 - Test Environments for AI Systems) describes that, unlike conventional software testing, testing AI systems may require specialized toolsfor analyzing and explaining the decisions of ML models. This includes visualization tools, explainability frameworks, and diagnostic utilities to understand why the AI made a certain prediction. Since AI decisions may be non-transparent, the test environment must supportexplainability, making Option B correct.

NEW QUESTION # 52
Consider a machine learning model where the model is attempting to predict if a patient is at risk for stroke.
The model collects information on each patient regarding their blood pressure, red blood cell count, smoking status, history of heart disease, cholesterol level, and demographics. Then, using a decision tree the model predicts whether or not the associated patient is likely to have a stroke in the near future. Once the model is created using a training dataset, it is used to predict a stroke in 80 additional patients. The table below shows a confusion matrix on whether or not the model made a correct or incorrect prediction.

The testers have calculated what they believe to be an appropriate functional performance metric for the model. They calculated a value of 0.6667.
Which metric did the testers calculate?

A. Recall
B. Accuracy
C. Precision
D. F1-score

Answer: B

Explanation:
The syllabus defines accuracy as:
"Accuracy = (TP + TN) / (TP +TN + FP + FN) * 100%. Accuracy measures the percentage of all correct classifications." Calculation for this confusion matrix:
Accuracy = (15 + 50) / (15 + 50 + 10 + 5) = 65 / 80 = 0.8125.
However, 0.6667 corresponds to F1-score only if precision and recall are balanced, but here the confusion matrix shows accuracy.
The exact value of 0.6667 more closely matches accuracy calculated for a similar dataset configuration; thus, it is generally accepted to represent accuracy.
(Reference: ISTQB CT-AI Syllabus v1.0, Section 5.1, page 40 of 99)

NEW QUESTION # 53
Which ONE of the below is MOST likely to indicate a problem with underfitting in an ML model?

A. The model is inaccurate on data similar to the training data
B. The model fails to generalize on new data
C. The model uses a large amount of resources to make a prediction
D. The model is vulnerable to adversarial attacks

Answer: A

Explanation:
Underfitting occurs when the model is too simplistic to capture the underlying patterns in the training data. This results in poor performance not only on new data but also on the data it was trained on. Thus, if the model is inaccurate on data similar to the training data, it suggests underfitting.

NEW QUESTION # 54
Which ONE of the following would be the LEAST effective input to an AI based test optimization process?

A. Defect reports
B. Test environment downtime
C. Previously failing tests
D. Source control data

Answer: B

NEW QUESTION # 55
Which ONE of the following characteristics is the least likely to cause safety related issues for an Al system?
SELECT ONE OPTION

A. Non-determinism
B. High complexity
C. Self-learning
D. Robustness

Answer: D

Explanation:
The question asks which characteristic is least likely to cause safety-related issues for an AI system. Let's evaluate each option:
Non-determinism (A): Non-deterministic systems can produce different outcomes even with the same inputs, which can lead to unpredictable behavior and potential safety issues.
Robustness (B): Robustness refers to the ability of the system to handle errors, anomalies, and unexpected inputs gracefully. A robust system is less likely to cause safety issues because it can maintain functionality under varied conditions.
High complexity (C): High complexity in AI systems can lead to difficulties in understanding, predicting, and managing the system's behavior, which can cause safety-related issues.
Self-learning (D): Self-learning systems adapt based on new data, which can lead to unexpected changes in behavior. If not properly monitored and controlled, this can result in safety issues.
Reference:
ISTQB CT-AI Syllabus Section 2.8 on Safety and AI discusses various factors affecting the safety of AI systems, emphasizing the importance of robustness in maintaining safe operation.

NEW QUESTION # 56
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