Neueste Certified Tester AI Testing Exam Prüfung pdf & CT-AI Prüfung Torrent

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ISTQB CT-AI Prüfungsplan:

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

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ISTQB CT-AI Prüfung - CT-AI Zertifikatsdemo

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ISTQB Certified Tester AI Testing Exam CT-AI Prüfungsfragen mit Lösungen (Q22-Q27):

22. Frage
A company producing consumable goods wants to identify groups of people with similar tastes for the purpose of targeting different products for each group. You have to choose and apply an appropriate ML type for this problem.
Which ONE of the following options represents the BEST possible solution for this above- mentioned task?

Antwort: C

Begründung:
Clustering is an unsupervised learning method used to group similar data points based on their features. It is ideal for identifying groups of people with similar tastes without prior knowledge of the group labels. This technique will help the company segment its customer base effectively.


23. Frage
Which ONE of the following statements about the hardware used to implement ML systems is MOST likely to be correct?

Antwort: D

Begründung:
ML systems often require hardware that supports complex operations, such as matrix multiplications and other linear algebra computations. These operations are fundamental to many machine learning algorithms, particularly in deep learning. Specialist hardware (e.g., GPUs) may be used for efficiency, but complex operations are the core requirement.


24. Frage
Which ONE of the following tests is LEAST likely to be performed during the ML model testing phase?
SELECT ONE OPTION

Antwort: A

Begründung:
The question asks which test is least likely to be performed during the ML model testing phase. Let's consider each option:
* Testing the accuracy of the classification model (A): Accuracy testing is a fundamental part of the ML model testing phase. It ensures that the model correctly classifies the data as intended and meets the required performance metrics.
* Testing the API of the service powered by the ML model (B): Testing the API is crucial, especially if the ML model is deployed as part of a service. This ensures that the service integrates well with other systems and that the API performs as expected.
* Testing the speed of the training of the model (C): This is least likely to be part of the ML model testing phase. The speed of training is more relevant during the development phase when optimizing and tuning the model. During testing, the focus is more on the model's performance and behavior rather than how quickly it was trained.
* Testing the speed of the prediction by the model (D): Testing the speed of prediction is important to ensure that the model meets performance requirements in a production environment, especially for real- time applications.
:
ISTQB CT-AI Syllabus Section 3.2 on ML Workflow and Section 5 on ML Functional Performance Metrics discuss the focus of testing during the model testing phase, which includes accuracy and prediction speed but not the training speed.


25. Frage
Which of the following characteristics of AI-based systems make it more difficult to ensure they are safe?

Antwort: D

Begründung:
The syllabus states that non-determinism is one of the key challenges for ensuring safety in AI-based systems:
"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, and lack of robustness." (Reference: ISTQB CT-AI Syllabus v1.0, Section 2.8, page 25 of 99)


26. Frage
Which assignment of AI techniques to testing support is BEST?

Antwort: B

Begründung:
The ISTQB CT-AI syllabus (Section5.2 - AI for Testing) explains that various AI approaches can support testing activities. Probabilistic methods--one of the three major AI technique groups--are used topredict system failures, especially when dealing with uncertainty, likelihood estimation, and reliability analysis. This aligns precisely with Option B.


27. Frage
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