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ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 4
  • 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 5
  • 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 6
  • 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.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q35-Q40):

NEW QUESTION # 35
Which AI-specific test objective and acceptance criterion should be selected MOST LIKELY for testing GPT_Legal?
Choose ONE option (1 out of 4)

Answer: D

Explanation:
The ISTQB CT-AI syllabus introducesAI-specific quality characteristics, includingevolution,functional safety,compatibility, andbias-related data quality. Section5.1 - AI-Specific Test Objectivesexplains that evolutionrefers to an AI system's capability to continue improving or at least maintain performance as it undergoes additional training. GPT_Legal is explicitly described as aself-learning systemexpected to:
* continuously reduce false positives,
* achieve weekly accuracy improvements of 10%,
* reach and maintain 90% accuracy,
* adapt to new environments (patent law firm # corporate legal department).
This aligns perfectly with the syllabus definition ofevidence of evolution: ensuring the model doesnot degrade as additional training data is introduced. OptionBtherefore directly supports the described acceptance criteria for this evolving, self-learning application.
Option A (functional safety) is irrelevant because patent searching and drafting do not constitute safety- critical domains. Option C (compatibility) is necessary but not the primary AI-specific objective. Option D addresses bias, which is important but not central to the described performance and continuous-learning expectations.
Thus,Option Bis the most appropriate AI-specific test objective.


NEW QUESTION # 36
The activation value output for a neuron in a neural network is obtained by applying computation to the neuron.
Which ONE of the following options BEST describes the inputs used to compute the activation value?
SELECT ONE OPTION

Answer: B

Explanation:
In a neural network, the activation value of a neuron is determined by a combination of inputs from the previous layer, the weights of the connections, and the bias at the neuron level. Here's a detailed breakdown:
* Inputs for Activation Value:
* Activation Values of Neurons in the Previous Layer:These are the outputs from neurons in the preceding layer that serve as inputs to the current neuron.
* Weights Assigned to the Connections:Each connection between neurons has an associated weight, which determines the strength and direction of the input signal.
* Individual Bias at the Neuron Level:Each neuron has a bias value that adjusts the input sum, allowing the activation function to be shifted.
* Calculation:
* The activation value is computed by summing the weighted inputs from the previous layer and adding the bias.
* Formula: z=#(wi#ai)+bz = \sum (w_i \cdot a_i) + bz=#(wi#ai)+b, where wiw_iwi are the weights, aia_iai are the activation values from the previous layer, and bbb is the bias.
* The activation function (e.g., sigmoid, ReLU) is then applied to this sum to get the final activation value.
* Why Option A is Correct:
* Option A correctly identifies all components involved in computing the activation value: the individual bias, the activation values of the previous layer, and the weights of the connections.
* Eliminating Other Options:
* B. Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons: This option misses the bias, which is crucial.
* C. Individual bias at the neuron level, and weights assigned to the connections between the neurons: This option misses the activation values from the previous layer.
* D. Individual bias at the neuron level, and activation values of neurons in the previous layer
This option misses the weights, which are essential.
References:
ISTQB CT-AI Syllabus, Section 6.1, Neural Networks, discusses the components and functioning of neurons in a neural network.
"Neural Network Activation Functions" (ISTQB CT-AI Syllabus, Section 6.1.1).


NEW QUESTION # 37
Which of the following is one of the reasons for data mislabelling?

Answer: D

Explanation:
The syllabus lists multiple reasons for mislabelled data, including the lack of domain knowledge:
"Lack of required domain knowledge may lead to incorrect labelling."
(Reference: ISTQB CT-AI Syllabus v1.0, Section 4.5.2, page 38 of 99)


NEW QUESTION # 38
An engine manufacturing facility wants to apply machine learning to detect faulty bolts. Which of the following would result in bias in the model?

Answer: A

Explanation:
The syllabus defines bias as:
"Bias is the systematic difference in treatment of certain objects, people or groups in comparison to others." It also discusses:
"Sample bias can occur if the data used for training the model does not represent the operational environment, or if some relevant faulty conditions are excluded deliberately." (Reference: ISTQB CT-AI Syllabus v1.0, Section 7.6 and 8.3)


NEW QUESTION # 39
A robotic AI-based system is being built by a logistics company to operate within its unmanned warehouses. The warehouses can all be very different and new ones are being added each year.
It is expected that the system will not require retraining for each warehouse, and will be able to learn the location of different items and move them to specified locations on request.
Which ONE of the following attributes should be MOST carefully considered when specifying the objectives and acceptance criteria for the system?

Answer: D

Explanation:
Adaptability is the most important attribute to consider in this scenario. Since the system will operate in different warehouses, each with potentially different layouts and configurations, it must be adaptable to various environments without needing retraining. The system should be able to learn and adjust to the specific characteristics of each warehouse, ensuring it functions effectively in all locations.


NEW QUESTION # 40
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