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CT-AI考試重點，CT-AI最新題庫

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ISTQB CT-AI 考試大綱：

主題
簡介

主題 1

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.

主題 2

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.

主題 3

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.

主題 4

Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based

主題 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.

主題 6

Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.

主題 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.

主題 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.

主題 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.

主題 10

systems from those required for conventional systems.

主題 11

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.

>> CT-AI考試重點 <<

CT-AI最新題庫 - CT-AI最新考證

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最新的 ISTQB AI Testing CT-AI 免費考試真題 (Q53-Q58):

問題 #53
The stakeholders of a machine learning model have confirmed that they understand the objective and purpose of the model, and ensured that the proposed model aligns with their business priorities. They have also selected a framework and a machine learning model that they will be using.
What should be the next step to progress along the machine learning workflow?

A. Tune the machine learning algorithm based on objectives and business priorities
B. Prepare and pre-process the data that will be used to train and test the model
C. Agree on defined acceptance criteria for the machine learning model
D. Evaluate the selection of the framework and the model

答案：A

解題說明：
Themachine learning (ML) workflowfollows a structured sequence of steps. Once stakeholders have agreed on theobjectives, business priorities, and the framework/model selection, the next logical step is to prepare and pre-process the databefore training the model.
* Data Preparationis crucial becausemachine learning models rely heavily on the quality of input data. Poor data can result in biased, inaccurate, or unreliable models.
* The process involvesdata acquisition, cleaning, transformation, augmentation, and feature engineering.
* Preparing the dataensures it is in the right format, free from errors, and representative of the problem domain, leading to better generalization in training.
* A (Tune the ML Algorithm):Hyperparameter tuning occursafter the model has been trainedand evaluated.
* C (Agree on Acceptance Criteria):Acceptance criteria should already have been defined in theinitial objective-setting phasebefore framework and model selection.
* D (Evaluate the Framework and Model):The selection of the framework and ML model has already been completed. The next step isdata preparation, not reevaluation.
* ISTQB CT-AI Syllabus (Section 3.2: ML Workflow - Data Preparation Phase)
* "Data preparation comprises data acquisition, pre-processing, and feature engineering.
Exploratory data analysis (EDA) may be performed alongside these activities".
* "The data used to train, tune, and test the model must be representative of the operational data that will be used by the model".
Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Since the model selection is complete, thenext step in the ML workflow is to prepare and pre-process the datato ensure it is ready for training and testing. Thus, thecorrect answer is B.

問題 #54
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. GUI analysis by computer vision
B. Machine learning on logs of execution
C. Natural language processing on textual requirements
D. Analyzing source code for generating test cases

答案：C

解題說明：
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.

問題 #55
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. Inherited bias: the model could have inherited unknown defects
B. Improper data preparation
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

答案：A

解題說明：
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:

問題 #56
Which ONE of the following options is an example that BEST describes a system with Al-based autonomous functions?
SELECT ONE OPTION

A. A fully automated manufacturing plant that uses no software.
B. A system that utilizes a tool like Selenium.
C. A system that is fully able to respond to its environment.
D. A system that utilizes human beings for all important decisions.

答案：C

解題說明：
* AI-Based Autonomous Functions: An AI-based autonomous system is one that can respond to its environment without human intervention. The other options either involve human decisions or do not use AI at all.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Autonomy and Testing Autonomous AI-Based Systems.

問題 #57
Which of the following is an example of an input change where it would be expected that the AI system should be able to adapt?

A. It has been trained to analyze mathematical models and is given a set of landscape pictures to classify.
B. It has been trained to recognize cats and is given an image of a dog.
C. It has been trained to recognize human faces at a particular resolution and it is given a human face image captured with a higher resolution.
D. It has been trained to analyze customer buying trend data and is given information on supplier cost data.

答案：C

解題說明：
AI systems, particularly machine learning models, need to exhibit adaptability and flexibility to handle slight variations in input data without requiring retraining. The ISTQB CT-AI syllabus outlines adaptability as a crucial feature of AI systems, especially when the system is exposed to variations in its operational environment.
* Option A:"It has been trained to recognize cats and is given an image of a dog."
* This scenario introduces an entirely new class (dogs), which is outside the AI system's expected scope. If the AI was only trained to recognize cats, it would not be expected to recognize dogs correctly without retraining. This does not demonstrate adaptability as expected from an AI system.
* Option B:"It has been trained to recognize human faces at a particular resolution and it is given a human face image captured with a higher resolution."
* This is an example of an AI system encountering a variation of its training data rather than entirely new data. Most AI-based image processing models can adapt to different resolutions by applying downsampling or other pre-processing techniques. Since the data remains within the domain of human faces, the model should be able to process the higher-resolution image without significant issues.
* Option C:"It has been trained to analyze mathematical models and is given a set of landscape pictures to classify."
* This represents a complete shift in the data type from structured numerical data to unstructured image data. The AI system is unlikely to adapt effectively, as it has not been trained on image classification tasks.
* Option D:"It has been trained to analyze customer buying trend data and is given information on supplier cost data."
* This introduces a significant domain shift. Customer buying trends focus on consumer behavior, while supplier cost data relates to pricing structures and logistics. The AI system would likely require retraining to process the new data meaningfully.
* Adaptability Requirements:The syllabus discusses that AI-based systems must be able to adapt to changes in their operational environment and constraints, including minor variations in input quality (such as resolution changes).
* Autonomous Learning & Evolution:AI systems are expected to improve and handle evolving inputs based on prior experience.
* Challenges in Testing Self-Learning Systems:AI systems should be tested to ensure they function correctly when encountering new but related data, such as different resolutions of the same object.
Analysis of the Answer Options:ISTQB CT-AI Syllabus References:Thus,option Bis the best choice as it aligns with the adaptability characteristics expected from AI-based systems.

問題 #58
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