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| Certification Vendor: | ISTQB |
|---|---|
| Exam Name: | ISTQB Certified Tester - AI Testing |
| Exam Number: | CT-AI |
| Related Certifications: | ISTQB CTFL ISTQB CTAL-TTA |
| Real Exam Qty: | 40 |
| Certificate Validity Period: | Lifetime (no expiration) |
| Exam Duration: | 60 minutes |
| Exam Format: | Multiple Choice |
| Passing Score: | 65% |
| Available Languages: | English |
| Exam Price: | EUR 250 |
| Sample Questions: | ISTQB CT-AI Sample Questions |
| Exam Way: | Online proctored or in-person at authorized testing centers |
| Pre Condition: | ISTQB CTFL (Certified Tester Foundation Level) certification is recommended but not mandatory |
| Official Syllabus URL: | https://www.istqb.org/certifications/artificial-intelligence-testing-certification |
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NEW QUESTION # 88
Before deployment of an AI based system, a developer is expected to demonstrate in a test environment how decisions are made. Which of the following characteristics does decision making fall under?
Answer: A
Explanation:
Explainability in AI-based systems refers to the ease with which users can determine how the system reaches a particular result. It is a crucial aspect when demonstrating AI decision-making, as it ensures that decisions made by AI models are transparent, interpretable, and understandable by stakeholders.
Before deploying an AI-based system, a developer must validate how decisions are made in a test environment. This process falls under the characteristic of explainability because it involves clarifying how an AI model arrives at its conclusions, which helps build trust in the system and meet regulatory and ethical requirements.
* ISTQB CT-AI Syllabus (Section 2.7: Transparency, Interpretability, and Explainability)
* "Explainability is considered to be the ease with which users can determine how the AI-based system comes up with a particular result".
* "Most users are presented with AI-based systems as 'black boxes' and have little awareness of how these systems arrive at their results. This ignorance may even apply to the data scientists who built the systems. Occasionally, users may not even be aware they are interacting with an AI- based system".
* ISTQB CT-AI Syllabus (Section 8.6: Testing the Transparency, Interpretability, and Explainability of AI-based Systems)
* "Testing the explainability of AI-based systems involves verifying whether users can understand and validate AI-generated decisions. This ensures that AI systems remain accountable and do not make incomprehensible or biased decisions".
* Contrast with Other Options:
* Autonomy (B): Autonomy relates to an AI system's ability to operate independently without human oversight. While decision-making is a key function of autonomy, the focus here is on demonstrating the reasoning behind decisions, which falls under explainability rather than autonomy.
* Self-learning (C): Self-learning systems adapt based on previous data and experiences, which is different from making decisions understandable to humans.
* Non-determinism (D): AI-based systems are often probabilistic and non-deterministic, meaning they do not always produce the same output for the same input. This can make testing and validation more challenging, but it does not relate to explaining the decision-making process.
Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Since the question explicitly asks about the characteristic under which decision-making falls when being demonstrated before deployment,explainability is the correct choicebecause it ensures that AI decisions are transparent, understandable, and accountable to stakeholders.
NEW QUESTION # 89
Which of the following statements about the structure and function of neural networks is true?
Choose ONE option (1 out of 4)
Answer: C
Explanation:
Section1.7 - Neural Networksof the ISTQB CT-AI syllabus explains that neural networks consist of neurons connected by weighted links. During training,learning occurs by adjusting the weights on these connections.
This is the essence of gradient descent and backpropagation. Option B correctly states this behavior: only theweightsare modified, not the activation functions, neuron counts, or architectural structure.
Option A is incorrect because a neuron'sbiasis not determined by previous activations; it is an independent trainable parameter added to the weighted input sum. Option C is incorrect because the syllabus states that asingle-layer perceptron is a valid type of neural network, although limited to linearly separable problems.
Option D is incorrect because no rule requires the number of input neurons to exceed or equal the number of output neurons. Instead, input neurons correspond to thenumber of features, while output neurons correspond totasks or classes.
Therefore,Option Bprecisely reflects the syllabus definition of what changes during neural network training.
NEW QUESTION # 90
An ML engineer performing supervised learning needs to label images of football games based on the location of the football in the image. Which ONE of the below labelling approaches can be used?
Answer: A
Explanation:
The correct answer is B. Annotation . The CT-AI syllabus explains that data labelling enriches unlabeled or poorly labelled data by adding labels so that it becomes suitable for supervised learning. For image-based supervised learning, the syllabus specifically describes labelling objects in images by drawing rectangles around them as a common labelling technique, known as annotation . It also notes that more complex annotation may be required for 3D objects or irregular bounding boxes, and that data labelling and annotation are typically supported by tools.
This directly matches the football-game scenario: the ML engineer must identify the location of the football in each image, which requires object localization rather than merely assigning the whole image to a class.
Option A, "internal," describes who performs labelling within an organization, not the image-labelling technique itself. Option C, augmentation, creates additional data points from an existing dataset, such as transformed images, but it does not identify the football's location. Option D, benchmarking, is unrelated to labelling and is used for comparative evaluation.
References/topics: CT-AI Syllabus Chapter 4, Section 4.5 "Data Labelling for Supervised Learning"; Appendix B "Annotation."
NEW QUESTION # 91
Arihant Meditation is a startup using Al to aid people in deeper and better meditation based on analysis of various factors such as time and duration of the meditation, pulse and blood pressure, EEG patters etc. among others. Their model accuracy and other functional performance parameters have not yet reached their desired level.
Which ONE of the following factors is NOT a factor affecting the ML functional performance?
SELECT ONE OPTION
Answer: B
Explanation:
* Factors Affecting ML Functional Performance: The data pipeline, quality of the labeling, and biased data are all factors that significantly affect the performance of machine learning models. The number of classes, while relevant for the model structure, is not a direct factor affecting the performance metrics such as accuracy or bias.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Data Quality and its Effect on the ML Model and ML Functional Performance Metrics.
NEW QUESTION # 92
In a conference on artificial intelligence (Al), a speaker made the statement, "The current implementation of Al using models which do NOT change by themselves is NOT true Al*. Based on your understanding of Al, is this above statement CORRECT or INCORRECT and why?
SELECT ONE OPTION
Answer: B
Explanation:
* A. This statement is incorrect. Current AI is true AI and there is no reason to believe that this fact will change over time.
AI is an evolving field, and the definition of what constitutes AI can change as technology advances.
* B. This statement is correct. In general, what is considered AI today may change over time.
The term AI is dynamic and has evolved over the years. What is considered AI today might be viewed as standard computing in the future. Historically, as technologies become mainstream, they often cease to be considered "AI".
* C. This statement is incorrect. What is considered AI today will continue to be AI even as technology evolves and changes.
This perspective does not account for the historical evolution of the definition of AI . As new technologies emerge, the boundaries of AI shift.
* D. This statement is correct. In general, today the term AI is utilized incorrectly.
While some may argue this, it is not a universal truth. The term AI encompasses a broad range of technologies and applications, and its usage is generally consistent with current technological capabilities.
NEW QUESTION # 93
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P.S. Free 2026 ISTQB CT-AI dumps are available on Google Drive shared by PDFVCE: https://drive.google.com/open?id=1Tbp1Wg8XKEvkQOSOzEDRX-MdO0CgIOCK