CT-AI Valid Test Forum | CT-AI Latest Study Questions

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ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • systems from those required for conventional systems.
Topic 3
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 4
  • 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 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.
Topic 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.
Topic 7
  • 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 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.
Topic 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.
Topic 10
  • 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 11
  • 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.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q154-Q159):

NEW QUESTION # 154
A motorcycle engine repair shop owner wants to detect a leaking exhaust valve and fix it before it falls and causes catastrophic damage to the engine. The shop developed and trained a predictive model with historical data files from known health engines and ones which experienced a catastrophic fails due to exhaust valve failure. The shop evaluated 200 engines using this model and then disassembled the engines to assess the true state of the valves, recording the results in the confusion matrix below.
What is the precision of this predictive model

Answer: C

Explanation:
Precision is a performance metric used to evaluate the accuracy of positive predictions in a classification model. It is defined by the formula:
Precision=TPTP+FP×100%\text{Precision} = \frac{TP}{TP + FP} \times 100\%Precision=TP+FPTP×100% Where:
* TP (True Positives)= Number of correctly predicted positive cases
* FP (False Positives)= Number of incorrectly predicted positive cases
The confusion matrix provided in the question would typically list these values. Based on ISTQB's guidelines for calculating precision, selecting the correct number of true positives and false positives from the given data should yield94.2%as the precision.
* Section 5.1 - Confusion Matrix and ML Functional Performance Metricsexplains the calculation of precisionusing the confusion matrix.
Reference from ISTQB Certified Tester AI Testing Study Guide:


NEW QUESTION # 155
Which of the following characteristics of AI-based systems make it more difficult to ensure they are safe?

Answer: D

Explanation:
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."


NEW QUESTION # 156
A mobile app start-up company is implementing an AI-based chat assistant for e-commerce customers. In the process of planning the testing, the team realizes that the specifications are insufficient.
Which testing approach should be used to test this system?

Answer: C


NEW QUESTION # 157
Which ONE of the below is MOST likely to be a challenge in ML data preparation?

Answer: D

Explanation:
In machine learning, data preparation is often the most time-consuming and costly phase. It involves collecting, cleaning, and transforming raw data into a usable form for training models.
This can require significant effort and resources, making it the most likely challenge compared to the other options listed.


NEW QUESTION # 158
Which statement regarding data preparation in the ML workflow is correct?
Choose ONE option (1 out of 4)

Answer: A

Explanation:
The ISTQB CT-AI syllabus describes theML data preparation workflowin Section2.2 - Data Preparation.
Data preparation consists ofdata gathering,cleaning,transformation, andsampling. The syllabus emphasizes that one significant challenge duringdata gatheringis combining data frommultiple heterogeneous sources, which often differ in structure, quality, and format. Ensuring the resulting dataset is accurate, complete, and representative can be complex, making this a critical challenge in the ML workflow.
This aligns directly with OptionC.
Option A is incorrect because erroneous data correction is part ofcleaning, not transformation. Option B contradicts the syllabus: while automation can help,not all steps should be automateddue to the need for expert oversight, especially in detecting subtle data quality issues. Option D is incorrect because sampling continues to involve risk-particularly around representativeness-and the syllabus emphasizes caution, not complacency.
Thus, OptionCis the only statement that accurately reflects the syllabus.


NEW QUESTION # 159
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