Valid CT-AI Mock Exam, CT-AI Reliable Test Question

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ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 4
  • Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 5
  • 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 6
  • 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 7
  • 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.

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ISTQB CT-AI Exam Questions for Authentic Preparation

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q160-Q165):

NEW QUESTION # 160
Which statement about AI-based test case generation is correct?
Choose ONE option (1 out of 4)

Answer: D

Explanation:
The ISTQB CT-AI syllabus indicates inSection 5.2 - AI for Testingthat AI-generated test cases may not come withpredefined expected results. This is because test-case generation methods-such as evolutionary algorithms, reinforcement learning, or clustering-based sampling-produceinputs, but the tester must still determine the correct outputs. Therefore, OptionCis correct: expected results may not be available, especially when AI produces novel or previously unseen input combinations.
Option A is incorrect: AI-based test generation can improve coverage by exploring large input spaces. Option B is incorrect because the need for oracles does not depend on whether the test case is AI-generated. Option D is incorrect because the syllabus allows using a model as an oracle inback-to-back testing, provided limitations are understood.
Therefore,Option Cis the correct syllabus-aligned statement.


NEW QUESTION # 161
Written requirements are given in text documents, which ONE of the following options is the BEST way to generate test cases from these requirements?

Answer: B

Explanation:
Natural Language Processing (NLP): NLP can analyze and understand human language. It can be used to process textual requirements to extract relevant information and generate test cases.
This method is efficient in handling large volumes of textual data and identifying key elements necessary for testing.


NEW QUESTION # 162
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

Answer: C

Explanation:
A . Black box attacks based on adversarial examples create an exact duplicate model of the original.
Black box attacks do not create an exact duplicate model. Instead, they exploit the model by querying it and using the outputs to craft adversarial examples without knowledge of the internal workings.
B . These attack examples cause a model to predict the correct class with slightly less accuracy even though they look like the original image.
Adversarial examples typically cause the model to predict the incorrect class rather than just reducing accuracy. These examples are designed to be visually indistinguishable from the original image but lead to incorrect classifications.
C . These attacks can't be prevented by retraining the model with these examples augmented to the training data.
This statement is incorrect because retraining the model with adversarial examples included in the training data can help the model learn to resist such attacks, a technique known as adversarial training.
D . These examples are model specific and are not likely to cause another model trained on the same task to fail.
Adversarial examples are often model-specific, meaning that they exploit the specific weaknesses of a particular model. While some adversarial examples might transfer between models, many are tailored to the specific model they were generated for and may not affect other models trained on the same task.
Therefore, the correct answer is D because adversarial examples are typically model-specific and may not cause another model trained on the same task to fail.


NEW QUESTION # 163
Which ONE of the following options represents a technology MOST TYPICALLY used to implement Al?

Answer: C

Explanation:
Technology Most Typically Used to Implement AI: Genetic algorithms are a well-known technique used in AI . They are inspired by the process of natural selection and are used to find approximate solutions to optimization and search problems. Unlike search engines, procedural programming, or case control structures, genetic algorithms are specifically designed for evolving solutions and are commonly employed in AI implementations.


NEW QUESTION # 164
A local business has a mail pickup/delivery robot for their office. The robot currently uses a track to move between pickup/drop off locations. When it arrives at a destination, the robot stops to allow a human to remove or deposit mail.
The office has decided to upgrade the robot to include AI capabilities that allow the robot to perform its duties without a track, without running into obstacles, and without human intervention.
The test team is creating a list of new and previously established test objectives and acceptance criteria to be used in the testing of the robot upgrade. Which of the following test objectives will test an AI quality characteristic for this system?

Answer: C

Explanation:
In the syllabus, theevolutioncharacteristic for AI-based systems means the ability of the system to evolve and adapt its behavior in response to changes in the environment or in its own performance:
"Evolution is the system's ability to change itself to adapt to new situations, different hardware, or a changing operational environment."


NEW QUESTION # 165
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