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ISTQB CT-AI Desktop Practice Exam Software of PDFDumps

PDFDumps Certified Tester AI Testing Exam (CT-AI) practice test has real Certified Tester AI Testing Exam (CT-AI) exam questions. You can change the difficulty of these questions, which will help you determine what areas appertain to more study before taking your ISTQB CT-AI Exam Dumps. Here we listed some of the most important benefits you can get from using our ISTQB CT-AI practice questions.

ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 4
  • systems from those required for conventional systems.
Topic 5
  • 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 6
  • 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 7
  • Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 8
  • 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 9
  • 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 10
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 11
  • 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.

ISTQB Certified Tester AI Testing Exam Sample Questions (Q102-Q107):

NEW QUESTION # 102
A test engineer is planning testing for a wearable medical device using AI. The medical device will detect possible heart issues in patients and dispatch emergency services automatically. It is not expected that many patients will have heart issues, and this is reflected in the available data.
In this case, it is decided it is more important that emergency services are not sent un- necessarily, than it is to detect actual heart problems.
Which ONE of the following metrics should the test engineer choose to ensure that the emergency services are sent only when needed?

Answer: C

Explanation:
Precision is the most appropriate metric in this case, as it focuses on the proportion of true positive results (correctly identifying heart issues) out of all the positive predictions made by the model. Since it is more important that emergency services are not sent unnecessarily (false positives), optimizing for precision ensures that the model is more cautious about dispatching emergency services, minimizing false alarms.


NEW QUESTION # 103
Which of the following is a dataset issue that can be resolved using pre-processing?

Answer: C


NEW QUESTION # 104
When verifying that an autonomous AI-based system is acting appropriately, which of the following are MOST important to include?

Answer: D

Explanation:
When verifyingautonomous AI-based systems, a critical aspect is ensuring that they maintain an appropriate level of autonomy whileonly requesting human intervention when necessary. If an AI system unnecessarily asks for human input, it defeats the purpose of autonomy and can:
* Slow down operations.
* Reduce trust in the system.
* Indicate improper confidence thresholds in decision-making.
This is particularly crucial inautonomous vehicles, AI-driven financial trading, and robotic process automation, where excessive human intervention would hinder performance.
* A. Test cases to verify that the system automatically confirms the correct classification of training data# This is relevant for verifying training consistency but not for autonomy validation.
* B. Test cases to detect the system appropriately automating its data input# While relevant, data automation does not directly address the verification of autonomy.
* D. Test cases to verify that the system automatically suppresses invalid output data# This focuses on output filtering rather than decision-making autonomy.
Why are the other options incorrect?Thus, the mostcritical test casefor verifyingautonomous AI-based systemsis ensuring that itdoes not unnecessarily request human intervention.
* Section 8.2 - Testing Autonomous AI-Based Systemsstates that it is crucial to testwhether the system requests human intervention only when necessaryand does not disrupt autonomy.
Reference from ISTQB Certified Tester AI Testing Study Guide:


NEW QUESTION # 105
You have access to the training data that was used to train an AI-based system. You can review this information and use it as a guideline when creating your tests. What type of characteristic is this?

Answer: C

Explanation:
The syllabus states:
"Transparency: This is considered to be the ease with which the algorithm and training data used to generate the model can be determined." Access to the training data is an example of transparency.
(Reference: ISTQB CT-AI Syllabus v1.0, Section 2.7, page 24 of 99)


NEW QUESTION # 106
Which of the following problems would best be solved using the supervised learning category of regression?

Answer: C

Explanation:
The syllabus states:
"Supervised learning... divides problems into two categories: classification and regression.
Regression is used when the problem requires the ML model to predict a numeric output, for example predicting the age of a person based on their habits."


NEW QUESTION # 107
......

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