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Top Latest CT-AI Learning Material Pass Certify | Valid Lab CT-AI Questions: Certified Tester AI Testing Exam

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ISTQB CT-AI Exam Syllabus Topics:

Topic
Details

Topic 1

Testing AI-Based Systems Overview: In this section, focus is given to how system specifications for AI-based systems can create challenges in testing and explain automation bias and how this affects testing.

Topic 2

ML: Data: This section of the exam covers explaining the activities and challenges related to data preparation. It also covers how to test datasets create an ML model and recognize how poor data quality can cause problems with the resultant ML model.

Topic 3

Methods and Techniques for the Testing of AI-Based Systems: In this section, the focus is on explaining how the testing of ML systems can help prevent adversarial attacks and data poisoning.

Topic 4

Testing AI-Specific Quality Characteristics: In this section, the topics covered are about the challenges in testing created by the self-learning of AI-based systems.

Topic 5

Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.

Topic 6

Introduction to AI: This exam section covers topics such as the AI effect and how it influences the definition of AI. It covers how to distinguish between narrow AI, general AI, and super AI; moreover, the topics covered include describing how standards apply to AI-based systems.

Topic 7

ML Functional Performance Metrics: In this section, the topics covered include how to calculate the ML functional performance metrics from a given set of confusion matrices.

Topic 8

systems from those required for conventional systems.

Topic 9

Neural Networks and Testing: This section of the exam covers defining the structure and function of a neural network including a DNN and the different coverage measures for neural networks.

Topic 10

Quality Characteristics for AI-Based Systems: This section covers topics covered how to explain the importance of flexibility and adaptability as characteristics of AI-based systems and describes the vitality of managing evolution for AI-based systems. It also covers how to recall the characteristics that make it difficult to use AI-based systems in safety-related applications.

Topic 11

Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based

ISTQB Certified Tester AI Testing Exam Sample Questions (Q27-Q32):

NEW QUESTION # 27
A word processing company is developing an automatic text correction tool. A machine learning algorithm was used to develop the auto text correction feature. The testers have discovered that when they start typing
"Isle of Wight" it fills in "Isle of Eight". Several UAT testers have accepted this change without noticing.
What type of bias is this?

A. Automation/Complacency
B. Ignorance/Cognitive
C. Complacency/Disregard
D. Geographical/Locality

Answer: A

Explanation:
The syllabus describes automation bias as:
"A type of bias caused by a person favoring the recommendations of an automated decision-making system over other sources." This is also known as complacency bias, where testers accept automated system outputs without questioning them.
(Reference: ISTQB CT-AI Syllabus v1.0, Glossary, Page 89 of 99)

NEW QUESTION # 28
Which ONE of the following options does NOT describe an Al technology related characteristic which differentiates Al test environments from other test environments?
SELECT ONE OPTION

A. Challenges in the creation of scenarios of human handover for autonomous systems.
B. The challenge of mimicking undefined scenarios generated due to self-learning
C. The challenge of providing explainability to the decisions made by the system.
D. Challenges resulting from low accuracy of the models.

Answer: A

Explanation:
AI test environments have several unique characteristics that differentiate them from traditional test environments. Let's evaluate each option:
A . Challenges resulting from low accuracy of the models.
Low accuracy is a common challenge in AI systems, especially during initial development and training phases. Ensuring the model performs accurately in varied and unpredictable scenarios is a critical aspect of AI testing.
B . The challenge of mimicking undefined scenarios generated due to self-learning.
AI systems, particularly those that involve machine learning, can generate undefined or unexpected scenarios due to their self-learning capabilities. Mimicking and testing these scenarios is a unique challenge in AI environments.
C . The challenge of providing explainability to the decisions made by the system.
Explainability, or the ability to understand and articulate how an AI system arrives at its decisions, is a significant and unique challenge in AI testing. This is crucial for trust and transparency in AI systems.
D . Challenges in the creation of scenarios of human handover for autonomous systems.
While important, the creation of scenarios for human handover in autonomous systems is not a characteristic unique to AI test environments. It is more related to the operational and deployment challenges of autonomous systems rather than the intrinsic technology-related characteristics of AI .
Given the above points, option D is the correct answer because it describes a challenge related to operational deployment rather than a technology-related characteristic unique to AI test environments.

NEW QUESTION # 29
Which of the following characteristics of AI-based systems make it more difficult to ensure they are safe?

A. Simplicity
B. Robustness
C. Non-determinism
D. Sustainability

Answer: C

Explanation:
AI-based systems oftenexhibit non-deterministic behavior, meaning theydo not always produce the same output for the same input. This makesensuring safety more difficult, as the system's behavior can change based on new data, environmental factors, or updates.
* Why Non-determinism Affects Safety:
* In traditional software, the same input always produces the same output.
* In AI systems, outputsvary probabilisticallydepending on learned patterns and weights.
* This unpredictability makes itharder to verify correctness, reliability, and safety, especially in critical domains likeautonomous vehicles, medical AI, and industrial automation.
* A (Simplicity):AI-based systems are typicallycomplex, not simple, which contributes to safety challenges.
* B (Sustainability):While sustainability is an important AI consideration, it doesnot directly affect safety.
* D (Robustness):Lack of robustnesscan make AI systems unsafe, butnon-determinism is the primary issuethat complicates safety verification.
* ISTQB CT-AI Syllabus (Section 2.8: Safety and AI)
* "The characteristics of AI-based systems that make it more difficult to ensure they are safe include: complexity, non-determinism, probabilistic nature, self-learning, lack of transparency, interpretability and explainability, lack of robustness".
Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Sincenon-determinism makes AI behavior unpredictable, complicating safety assurance, thecorrect answer is C.

NEW QUESTION # 30
A wildlife conservation group would like to use a neural network to classify images of different animals. The algorithm is going to be used on a social media platform to automatically pick out pictures of the chosen animal of the month. This month's animal is set to be a wolf. The test teamhas already observed that the algorithm could classify a picture of a dog as being a wolf because of the similar characteristics between dogs and wolves. To handle such instances, the team is planning to train the model with additional images of wolves and dogs so that the model is able to better differentiate between the two.
What test method should you use to verify that the model has improved after the additional training?

A. Pairwise testing using combinatorics to look at a long list of photo parameters.
B. Metamorphic testing because the application domain is not clearly understood at this point.
C. Adversarial testing to verify that no incorrect images have been used in the training.
D. Back-to-back testing using the version of the model before training and the new version of the model after being trained with additional images.

Answer: D

Explanation:
Back-to-back testing isused to compare two different versions of an ML model, which is precisely what is needed in this scenario.
* The model initiallymisclassified dogs as wolvesdue to feature similarities.
* Thetest team retrains the modelwith additional images of dogs and wolves.
* The best way to verify whether this additional trainingimproved classification accuracyis to compare theoriginal model's output with the newly trained model's output using the same test dataset.
* A (Metamorphic Testing):Metamorphic testing is useful forgenerating new test casesbased on existing ones but does not directly compare different model versions.
* B (Adversarial Testing):Adversarial testing is used to check how robust a model is againstmaliciously perturbed inputs, not to verify training effectiveness.
* C (Pairwise Testing):Pairwise testing is a combinatorial technique for reducing the number of test casesby focusing on key variable interactions, not for validating model improvements.
* ISTQB CT-AI Syllabus (Section 9.3: Back-to-Back Testing)
* "Back-to-back testing is used when an updated ML model needs to be compared against a previous version to confirm that it performs better or as expected".
* "The results of the newly trained model are compared with those of the prior version to ensure that changes did not negatively impact performance".
Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:To verify that the model's performance improved after retraining,back-to-back testing is the most appropriate methodas it compares both model versions. Hence, thecorrect answer is D.

NEW QUESTION # 31
Which of the following is one of the reasons for data mislabelling?

A. Lack of domain knowledge
B. Interoperability error
C. Small datasets
D. Expert knowledge

Answer: A

Explanation:
Data mislabeling occurs for several reasons, which can significantly impact the performance of machine learning (ML) models, especially in supervised learning. According to the ISTQB Certified Tester AI Testing (CT-AI) syllabus, mislabeling of data can be caused by the following factors:
* Random errors by annotators- Mistakes made due to accidental misclassification.
* Systemic errors- Errors introduced by incorrect labeling instructions or poor training of annotators.
* Deliberate errors- Errors introduced intentionally by malicious data annotators.
* Translation errors- Occur when correctly labeled data in one language is incorrectly translated into another language.
* Subjectivity in labeling- Some labeling tasks require subjective judgment, leading to inconsistencies between different annotators.
* Lack of domain knowledge- If annotators do not have sufficient expertise in the domain, they may label data incorrectly due to misunderstanding the context.
* Complex classification tasks- The more complex the task, the higher the probability of labeling mistakes.
Among the answer choices provided, "Lack of domain knowledge" (Option A) is the best answer because expertise is essential to accurately labeling data in complex domains such as medical, legal, or engineering fields.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 4.5.2 (Mislabeled Data in Datasets)
* ISTQB CT-AI Syllabus v1.0, Section 4.3 (Dataset Quality Issues)

NEW QUESTION # 32
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