CT-AI Übungsfragen: Certified Tester AI Testing Exam & CT-AI Dateien Prüfungsunterlagen

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CT-AI Fragenpool, CT-AI Fragen Und Antworten

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ISTQB CT-AI Prüfungsplan:

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

ISTQB Certified Tester AI Testing Exam CT-AI Prüfungsfragen mit Lösungen (Q70-Q75):

70. Frage
Written requirements are given in text documents, which ONE of the following options is the BEST way to generate test cases from these requirements?
SELECT ONE OPTION

A. Natural language processing on textual requirements
B. GUI analysis by computer vision
C. Analyzing source code for generating test cases
D. Machine learning on logs of execution

Antwort: A

Begründung:
When written requirements are given in text documents, the best way to generate test cases is by using Natural Language Processing (NLP). Here's why:
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.
Why Not Other Options:
Analyzing source code for generating test cases: This is more suitable for white-box testing where the code is available, but it doesn't apply to text-based requirements.
Machine learning on logs of execution: This approach is used for dynamic analysis based on system behavior during execution rather than static textual requirements.
GUI analysis by computer vision: This is used for testing graphical user interfaces and is not applicable to text-based requirements.

71. Frage
Which ONE of the following options represents a technology MOST TYPICALLY used to implement Al?
SELECT ONE OPTION

A. Procedural programming
B. Search engines
C. Case control structures
D. Genetic algorithms

Antwort: D

Begründung:
* 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.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 1.4 AI Technologies, which identifies different technologies used to implement AI.

72. Frage
Written requirements are given in text documents, which ONE of the following options is the BEST way to generate test cases from these requirements?
SELECT ONE OPTION

A. Natural language processing on textual requirements
B. GUI analysis by computer vision
C. Analyzing source code for generating test cases
D. Machine learning on logs of execution

Antwort: A

Begründung:
When written requirements are given in text documents, the best way to generate test cases is by using Natural Language Processing (NLP). Here's why:
* 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.
* Why Not Other Options:
* Analyzing source code for generating test cases: This is more suitable for white-box testing where the code is available, but it doesn't apply to text-based requirements.
* Machine learning on logs of execution: This approach is used for dynamic analysis based on system behavior during execution rather than static textual requirements.
* GUI analysis by computer vision: This is used for testing graphical user interfaces and is not applicable to text-based requirements.
References:This aligns with the methodology discussed in the syllabus under the section on using AI for generating test cases from textual requirements.

73. Frage
A beer company is trying to understand how much recognition its logo has in the market. It plans to do that by monitoring images on various social media platforms using a pre-trained neural network for logo detection.
This particular model has been trained by looking for words, as well as matching colors on social media images. The company logo has a big word across the middle with a bold blue and magenta border.
Which associated risk is most likely to occur when using this pre-trained model?

A. Improper data preparation
B. Inherited bias: the model could have inherited unknown defects
C. There is no risk, as the model has already been trained
D. Insufficient function; the model was not trained to check for colors or words

Antwort: B

Begründung:
A major risk when using apre-trained neural networkfor logo detection is that it mayinherit biases and defectsfrom the original dataset and training process. This means that the model could misidentify or fail to recognize certain logos due to:
* Differences in data preparation:The original training data may have used a different preprocessing method than the new dataset, leading to inconsistencies.
* Limited transparency:The exact details of the dataset and biases within it may not be known, which can cause unexpected behavior.
* Bias in logo detection:If the model was trained on a dataset with certain color or text preferences, it may disproportionately misidentify logos with similar characteristics.
This inherited bias can result in:
* False Positives:Recognizing other brand logos as the beer company's logo.
* False Negatives:Failing to detect the actual logo when variations occur (e.g., different lighting or partial visibility).
* Algorithmic Bias:The model may favor certain shapes or color contrasts due to biased training data.
Thus,the most appropriate risk associated with using this pre-trained model is inherited bias.
* Section 1.8.3 - Risks of Using Pre-Trained Models and Transfer Learningexplains how pre-trained models may inheritbiases and undocumented defectsthat affect performance in a new environment.
Reference from ISTQB Certified Tester AI Testing Study Guide:

74. Frage
You have been developing test automation for an e-commerce system. One of the problems you are seeing is that object recognition in the GUI is having frequent failures. You have determined this is because the developers are changing the identifiers when they make code updates.
How could AI help make the automation more reliable?

A. It could dynamically name the objects, altering the source code, so the object names will match the object names used in the automation.
B. It could identify the objects multiple ways and then determine the most commonly used and stable identification for each object.
C. It could generate a model that will anticipate developer changes and pre-alter the test automation code accordingly.
D. It could modify the automation code to ignore unrecognizable objects to avoid failures.

Antwort: B

75. Frage
......

Die Schwierigkeiten können den Charakter eines Menschen testen. Eine schlechte Situation kann die Aufrichtigkeit eines Menschen zeigen. Wenn man einer schlechten Situation gegenüberstehen, können nur die mutigen es gant leichtnehmen. Sind Sie ein mutiger Mensch? Wenn Sie sich nicht so gut auf Ihre Prüfung vorbereiten, können Sie es noch leichtnehmen. Weil Sie die Fragenkataloge zur ISTQB CT-AI Prüfung von Zertpruefung haben. Und eine ISTQB CT-AI Prüfung wird Sie nicht niederschlagen.

CT-AI Fragenpool: https://www.zertpruefung.de/CT-AI_exam.html

P.S. Kostenlose und neue CT-AI Prüfungsfragen sind auf Google Drive freigegeben von Zertpruefung verfügbar: https://drive.google.com/open?id=11iVxgB_glwnGZIcEPJJJp1jUrR-z_7R_

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CT-AI Übungsfragen: Certified Tester AI Testing Exam & CT-AI Dateien Prüfungsunterlagen

CT-AI Fragenpool, CT-AI Fragen Und Antworten

ISTQB CT-AI Prüfungsplan:

ISTQB Certified Tester AI Testing Exam CT-AI Prüfungsfragen mit Lösungen (Q70-Q75):

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