CT-AI Latest Study Guide, Exam CT-AI Questions

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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	systems from those required for conventional systems.
Topic 2	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 3	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 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.
Topic 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.
Topic 6	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.
Topic 7	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 8	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 9	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.

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Exam CT-AI Questions - Exam CT-AI Pattern

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q39-Q44):

NEW QUESTION # 39
Which of the following is a dataset issue that can be resolved using pre-processing?

A. Wanted outliers
B. Numbers stored as strings
C. Invalid data
D. Insufficient data

Answer: B

Explanation:
The syllabus describes that data pre-processing includes cleaning (e.g., fixing or removing invalid data) and transforming data (e.g., changing data types such as numbers stored as strings).
"Transformation: The format of the given data is changed... converting categorical data into numerical data, changing image formats..."

NEW QUESTION # 40
Which of the following options is an example of the concept of overfitting?

A. A model for the recognition of dogs was trained predominantly with pictures of dogs in parks. On pictures with other animals in parks, dogs are also falsely recognized.
B. A model for predicting academic performance was trained with data from students at one university. The model shows low predictive accuracy when applied to other universities.
C. A model for predicting IT system failures delivers too many false-negative predictions because the failures cannot be adequately explained via the log files used for training.
D. A previously trained model for recognizing cars is adapted and extended so that it can also identify the make of the car beyond its original function.

Answer: B

Explanation:
The ISTQB CT-AI syllabus defines overfitting in Section3.2 - ML Model Evaluationas a condition where an ML model learns the training data too precisely--including noise and irrelevant detail-- resulting in poor performance on unseen data. Overfitting is characterized byhigh accuracy on training data but low accuracy on validation or real-world data. Option A perfectly matches this definition: a model trained only on one university's student data generalizes poorly to students from other universities.
This is a textbook example of overfitting because the model has essentially memorized patterns unique to a narrow dataset, instead of learning generalizable relationships applicable across environments .

NEW QUESTION # 41
Data used for an object detection ML system was found to have been labelled incorrectly in many cases.
Which ONE of the following options is most likely the reason for this problem?
SELECT ONE OPTION

A. Security issues
B. Accuracy issues
C. Privacy issues
D. Bias issues

Answer: B

Explanation:
The question refers to a problem where data used for an object detection ML system was labelled incorrectly. This issue is most closely related to "accuracy issues." Here's a detailed explanation:
Accuracy Issues: The primary goal of labeling data in machine learning is to ensure that the model can accurately learn and make predictions based on the given labels. Incorrectly labeled data directly impacts the model's accuracy, leading to poor performance because the model learns incorrect patterns.
Why Not Other Options:
Security Issues: This pertains to data breaches or unauthorized access, which is not relevant to the problem of incorrect data labeling.
Privacy Issues: This concerns the protection of personal data and is not related to the accuracy of data labeling.
Bias Issues: While bias in data can affect model performance, it specifically refers to systematic errors or prejudices in the data rather than outright incorrect labeling.

NEW QUESTION # 42
Which of the following is a dataset issue that can be resolved using pre-processing?

A. Wanted outliers
B. Numbers stored as strings
C. Invalid data
D. Insufficient data

Answer: B

NEW QUESTION # 43
A test engineer is planning the best functional performance metrics to evaluate an unsupervised learning model. The model groups data points based on their similarity. The test engineer wants to measure how similar the data points in each group actually are.
Which is the MOST likely metric they should use?

A. Recall
B. AUC
C. Intra-cluster
D. ROC

Answer: C

Explanation:
The most appropriate metric for evaluating the similarity of data points within each group in an unsupervised learning model is intra-cluster. This metric measures how similar the data points within each cluster are to one another. The goal is to have high intra-cluster similarity, meaning the data points within a group should be similar to each other.

NEW QUESTION # 44
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