Certified Tester AI Testing Exam Actual Exam & CT-AI Practice Vce & Certified Tester AI Testing Exam Updated Torrent

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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	systems from those required for conventional systems.
Topic 3	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 4	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 5	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 6	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 7	Machine Learning ML: This section includes the classification and regression as part of supervised learning, explaining the factors involved in the selection of ML algorithms, and demonstrating underfitting and overfitting.
Topic 8	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.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q36-Q41):

NEW QUESTION # 36
Max. Score: 2
Al-enabled medical devices are used nowadays for automating certain parts of the medical diagnostic processes. Since these are life-critical process the relevant authorities are considenng bringing about suitable certifications for these Al enabled medical devices. This certification may involve several facets of Al testing (I - V).
I.Autonomy
II.Maintainability
III.Safety
IV.Transparency
V.Side Effects
Which ONE of the following options contains the three MOST required aspects to be satisfied for the above scenario of certification of Al enabled medical devices?
SELECT ONE OPTION

A. Aspects I, II, and III
B. Aspects II, III and IV
C. Aspects I, IV, and V
D. Aspects III, IV, and V

Answer: D

Explanation:
For AI-enabled medical devices, the most required aspects for certification are safety, transparency, and side effects. Here's why:
* Safety (Aspect III): Critical for ensuring that the AI system does not cause harm to patients.
* Transparency (Aspect IV): Important for understanding and verifying the decisions made by the AI system.
* Side Effects (Aspect V): Necessary to identify and mitigate any unintended consequences of the AI system.
Why Not Other Options:
* Autonomy and Maintainability (Aspects I and II): While important, they are secondary to the immediate concerns of safety, transparency, and managing side effects in life-critical processes.
References:This explanation is aligned with the critical quality characteristics for AI-based systems as mentioned in the ISTQB CT-AI syllabus, focusing on the certification of medical devices.

NEW QUESTION # 37
Which of the following options BEST describes an example of reinforcement learning?

A. An image recognition system regularly searches the internet to find new examples to add to its training data.
B. A financial system that chooses which stocks to buy regularly optimizes based on new changes in stock price to improve its performance.
C. A house cleaning robot adjusts the path it takes on a daily basis based on the most efficient route and the amount of dirt it is able to remove.
D. A health warning system uses data from thousands of people to predict who is most likely to suffer from a particular health condition based on their behavior.

Answer: C

Explanation:
Reinforcement learning involves an agent learning to make decisions by taking actions that maximize a reward over time. In the example of the house cleaning robot, the robot adjusts its actions (path taken) based on feedback (efficiency and amount of dirt removed), which is characteristic of reinforcement learning.

NEW QUESTION # 38
Which of the following is an example of a clustering problem that can be resolved by unsupervised learning?

A. Grouping individual fish together based on their types of fins
B. Estimating the expected purchase of cat food after a particularly successful ad campaign
C. Classifying muffin purchases based on the perceived attractiveness of their packaging
D. Associating shoppers with their shopping tendencies

Answer: D

Explanation:
The syllabus defines clustering as:
"Clustering: This is when the problem requires the identification of similarities in input data points that allows them to be grouped based on common characteristics or attributes. For example, clustering is used to categorize different types of customers for the purpose of marketing." (Reference: ISTQB CT-AI Syllabus v1.0, Section 3.1.2, page 26 of 99)

NEW QUESTION # 39
Which ONE of the following characteristics is the least likely to cause safety related issues for an Al system?
SELECT ONE OPTION

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

Answer: A

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.
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 # 40
Which of the following neural network coverage criteria can be adapted for its application?
Choose ONE option (1 out of 4)

A. Sign-Sign coverage
B. Sign-Change coverage
C. Neuron coverage
D. Threshold coverage

Answer: D

Explanation:
Section4.2 - Test Coverage Criteria for AI Modelsof the ISTQB CT-AI syllabus describes neural network- specific coverage methods. Among the techniques,threshold coverageis explicitly noted asadaptable, meaning testers may choose different thresholds to determine whether neuron activation is considered "covered." This flexibility makes threshold coverage adjustable to the model architecture, problem domain, and required test thoroughness.
Options A and B (Sign-Sign and Sign-Change coverage) are more rigid structural criteria and are not described as adaptable within the syllabus. They focus on sign patterns of neuron activations and do not allow altering thresholds. Option D, neuron coverage, measures the proportion of neurons activated at least once.
Although simple, it is not defined as an adaptable criterion. Its limitations are documented: it provides shallow insight and too easily achieves high coverage.
Onlythreshold coverageallows testers to adjust activation thresholds for more refined coverage measurement, makingOption Cthe correct choice.

NEW QUESTION # 41
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