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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Test Methods and Techniques | 20% | - Test Oracle Techniques for AI - Metamorphic Testing - Adversarial Testing - Black-Box Testing for AI Systems |
| Topic 2: Neural Networks and Deep Learning | 20% | - Testing Deep Learning Models - Overfitting and Underfitting - Model Training and Evaluation Metrics - Neural Network Architecture Basics |
| Topic 3: Testing AI-Based Systems | 20% | - Challenges in Testing AI-Based Systems - Bias and Fairness Considerations - Test Levels for AI-Based Systems - Testing AI-Specific Quality Characteristics |
| Topic 4: Practical Considerations | 12% | - Test Data Quality and Preparation - Automation in AI Testing - Test Environment Setup for AI - Documentation and Reporting |
| Topic 5: Testing AI-Specific Quality Characteristics | 20% | - Fairness and Bias Detection - Explainability and Interpretability - Accuracy, Precision, Recall, F1-Score - Robustness Testing |
| Topic 6: Basics of AI | 8% | - Definitions and Terminology - Data Concepts (Training, Validation, Test Data) - Machine Learning Types (Supervised, Unsupervised, Reinforcement) - AI Types and Techniques |
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NEW QUESTION # 118
A tourist calls an airline to book a ticket and is connected with an automated system which is able to recognize speech, understand requests related to purchasing a ticket, and provide relevant travel options. When the tourist asks about the expected weather at the destination or potential impacts on operations because of the tight labor market the only response from the automated system is: "I don't understand your question." This AI system should be categorized as?
Answer: B
Explanation:
Narrow AI (also known as Weak AI) is designed to perform specific tasks or solve particular problems within a limited domain, such as booking a ticket or recognizing speech. In this case, the AI system is specialized in handling flight bookings and related tasks but is not capable of addressing broader or more complex questions, like weather conditions or labor market impacts.
NEW QUESTION # 119
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."
NEW QUESTION # 120
Which supervised-learning classification/regression statement is correct?
Choose ONE option (1 out of 4)
Answer: B
Explanation:
The ISTQB CT-AI syllabus explains supervised learning under Section1.6 - Machine Learning Approaches.
It definesclassificationas predictingcategorical labels, whereasregressionpredictscontinuous numerical values.
OptionB-deciding whether an object is a bicycle or a motorcycle-fits the definition of classification precisely because the model chooses between discrete categories. The syllabus also uses similar examples to illustrate classification tasks, reinforcing that this is the correct interpretation .
Option A is incorrect because image recognition of a dog is aclassificationtask, not regression. Option C is incorrect because predicting a 10% price rise involves forecasting anumerical value, which is aregressionproblem. Option D is incorrect because classification can involveany number of classes, not only two. Multiclass classification is explicitly mentioned in the syllabus.
Therefore, OptionBis the only answer aligned with the syllabus' definitions.
NEW QUESTION # 121
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: D
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=โ(wiai)+bz = \sum (w_i \cdot a_i) + bz=โ(wiai)+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.
Reference:
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 # 122
Arihant Meditation is a startup using Al to aid people in deeper and better meditation based on analysis of various factors such as time and duration of the meditation, pulse and blood pressure, EEG patters etc. among others. Their model accuracy and other functional performance parameters have not yet reached their desired level.
Which ONE of the following factors is NOT a factor affecting the ML functional performance?
SELECT ONE OPTION
Answer: C
Explanation:
* Factors Affecting ML Functional Performance: The data pipeline, quality of the labeling, and biased data are all factors that significantly affect the performance of machine learning models. The number of classes, while relevant for the model structure, is not a direct factor affecting the performance metrics such as accuracy or bias.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Data Quality and its Effect on the ML Model and ML Functional Performance Metrics.
NEW QUESTION # 123
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