CT-AI Prüfungsfrage & CT-AI Praxisprüfung

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ISTQB CT-AI Prüfungsplan:

Thema	Einzelheiten
Thema 1	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 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.
Thema 3	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Thema 4	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 5	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.
Thema 6	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Thema 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 Certified Tester AI Testing Exam CT-AI Prüfungsfragen mit Lösungen (Q154-Q159):

154. Frage
Which of the following is correct regarding the layers of a deep neural network?

A. The output layer is not connected with the other layers to maintain integrity
B. There is at least one internal hidden layer
C. There must be a minimum of five total layers to be considered deep
D. There is only an input and output layer

Antwort: B

Begründung:
A deep neural network (DNN) is a type of artificial neural network that consists of multiple layers between the input and output layers. The ISTQB Certified Tester AI Testing (CT-AI) Syllabus outlines the following characteristics of a DNN:
Structure of a Deep Neural Network:
A DNN comprises at least three types of layers:
Input layer: Receives the input data.
Hidden layers: Perform complex feature extraction and transformations.
Output layer: Produces the final prediction or classification.

155. Frage
Which of the following is an example of overfitting?

A. The model is missing relationships between the inputs and outputs.
B. The model discards data it considers to be noise or outliers.
C. The model is too simplistic for the data.
D. The model is not able to generalize to accommodate new types of data.

Antwort: D

Begründung:
Overfitting occurs when a machine learning (ML) model learns patterns that are too specific to the training data, leading to a lack of generalization for new, unseen data. This means the model performs exceptionally well on the training data but poorly on validation or test data because it has memorized the noise and minor details rather than learning the underlying patterns.
* Option A:"The model is not able to generalize to accommodate new types of data."
* This is the correct definition of overfitting. When a model cannot generalize beyond its training data, it struggles with new input, which results in overfitting.
* Option B:"The model is too simplistic for the data."
* This describes underfitting rather than overfitting. Underfitting happens when a model is too simple to capture the underlying patterns in the data.
* Option C:"The model is missing relationships between the inputs and outputs."
* This also aligns more with underfitting, where the model fails to capture important relationships in the data.
* Option D:"The model discards data it considers to be noise or outliers."
* While some ML models may ignore outliers, overfitting actually occurs when the model includes noise and outliers in its learning process rather than discarding them.
* Overfitting Definition:"Overfitting occurs when the model fits too closely to a set of data points and fails to properly generalize. It works well on training data but struggles with new data.".
* Testing for Overfitting:"Overfitting may be detected by testing the model with a dataset that is completely independent of the training dataset" Analysis of the Answer Options:ISTQB CT-AI Syllabus References:

156. Frage
Which ONE of the following options describes a scenario of A/B testing the LEAST?

A. A comparison of two different offers in a recommendation system to decide on the more effective offer for same users.
B. A comparison of the performance of an ML system on two different input datasets.
C. A comparison of the performance of two different ML implementations on the same input data.
D. A comparison of two different websites for the same company to observe from a user acceptance perspective.

Antwort: B

Begründung:
Option C describes comparing the performance of an ML system on two different input datasets.
This scenario focuses on the input data variation rather than the comparison of system versions or features, which is the essence of A/B testing. A/B testing typically involves a controlled experiment with two versions being tested under the same conditions, not different datasets.

157. Frage
A system is to be developed to detect lung cancer using X-ray images. Which statement BEST describes the difference between a conventional system and an AI system with supervised machine learning?

A. The implementation of an AI system consists mainly of training data, whereas that of a conventional system consists of branches and loops.
B. An AI system independently determines patterns in X-rays during training; a conventional system requires a human to program in those patterns.
C. The X-ray images that an AI system can analyze must be structurally different from X-ray images used in a conventional system.
D. The results of analyzing an X-ray for lung cancer using an AI system are more understandable than with a conventional system.

Antwort: B

Begründung:
The syllabus explains the fundamental distinction between conventional systems and AI-based systems using supervised machine learningin Section1.3 - AI-Based and Conventional Systems.
A conventional system relies on human-programmed logic--such as branches, conditions, and explicit rules--to interpret input data. The system behaves exactly as specified by its developers.
In contrast, AI systems using supervised learning automatically learn patterns from labeled data.
The syllabus states that"patterns in data are used by the system to determine how it should react in the future... The AI determines on its own what patterns or features in the data can be used".
This aligns directly with Option C: an AI system identifies relevant diagnostic patterns in X-ray images during training, whereas a conventional system requires human experts to explicitly program those patterns.

158. Frage
Which ONE of the following statements BEST describes a testing challenge that specifically applied to a self-learning system?

A. When systems change themselves the results of previously passing tests may change
B. External data sources might be required to ensure that the system is unbiased
C. In can be difficult to explain the link between test inputs and outputs
D. It is necessary to test whether the system will relinquish control to a human at the right time

Antwort: A

Begründung:
A key challenge in testing self-learning systems is that, as the system learns and adapts over time, the results of previously passing tests may change. This is because the system's behavior evolves as it learns from new data, potentially altering how it responds to test inputs. This dynamic nature of self-learning systems makes it challenging to maintain consistent and reliable test results.

159. Frage
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