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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Practical Considerations | 12% | - Test Data Quality and Preparation - Documentation and Reporting - Automation in AI Testing - Test Environment Setup for AI |
| Topic 2: Testing AI-Based Systems | 20% | - Bias and Fairness Considerations - Test Levels for AI-Based Systems - Challenges in Testing AI-Based Systems - Testing AI-Specific Quality Characteristics |
| Topic 3: Testing AI-Specific Quality Characteristics | 20% | - Accuracy, Precision, Recall, F1-Score - Robustness Testing - Fairness and Bias Detection - Explainability and Interpretability |
| Topic 4: Basics of AI | 8% | - Machine Learning Types (Supervised, Unsupervised, Reinforcement) - Data Concepts (Training, Validation, Test Data) - Definitions and Terminology - AI Types and Techniques |
| Topic 5: Neural Networks and Deep Learning | 20% | - Neural Network Architecture Basics - Testing Deep Learning Models - Model Training and Evaluation Metrics - Overfitting and Underfitting |
| Topic 6: AI Test Methods and Techniques | 20% | - Adversarial Testing - Black-Box Testing for AI Systems - Metamorphic Testing - Test Oracle Techniques for AI |
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NEW QUESTION # 57
Which of the following statements about the structure and function of neural networks is true?
Choose ONE option (1 out of 4)
Answer: D
Explanation:
Section1.7 - Neural Networksof the ISTQB CT-AI syllabus explains that neural networks consist of neurons connected by weighted links. During training,learning occurs by adjusting the weights on these connections.
This is the essence of gradient descent and backpropagation. Option B correctly states this behavior: only theweightsare modified, not the activation functions, neuron counts, or architectural structure.
Option A is incorrect because a neuron'sbiasis not determined by previous activations; it is an independent trainable parameter added to the weighted input sum. Option C is incorrect because the syllabus states that asingle-layer perceptron is a valid type of neural network, although limited to linearly separable problems.
Option D is incorrect because no rule requires the number of input neurons to exceed or equal the number of output neurons. Instead, input neurons correspond to thenumber of features, while output neurons correspond totasks or classes.
Therefore,Option Bprecisely reflects the syllabus definition of what changes during neural network training.
NEW QUESTION # 58
"AllerEgo" is a product that uses sell-learning to predict the behavior of a pilot under combat situation for a variety of terrains and enemy aircraft formations. Post training the model was exposed to the real- world data and the model was found to be behaving poorly. A lot of data quality tests had been performed on the data to bring it into a shape fit for training and testing.
Which ONE of the following options is least likely to describes the possible reason for the fall in the performance, especially when considering the self-learning nature of the Al system?
SELECT ONE OPTION
Answer: D
Explanation:
* A. The difficulty of defining criteria for improvement before the model can be accepted.
* Defining criteria for improvement is a challenge in the acceptance of AI models, but it is not directly related to the performance drop in real-world scenarios. It relates more to the evaluation and deployment phase rather than affecting the model's real-time performance post-deployment.
* B. The fast pace of change did not allow sufficient time for testing.
* This can significantly affect the model's performance. If the system is self-learning, it needs to adapt quickly, and insufficient testing time can lead to incomplete learning and poor performance.
* C. The unknown nature and insufficient specification of the operating environment might have caused the poor performance.
* This is highly likely to affect performance. Self-learning AI systems require detailed specifications of the operating environment to adapt and learn effectively. If the environment is insufficiently specified, the model may fail to perform accurately in real-world scenarios.
* D. There was an algorithmic bias in the AI system.
* Algorithmic bias can significantly impact the performance of AI systems. If the model has biases, it will not perform well across different scenarios and data distributions.
Given the context of the self-learning nature and the need for real-time adaptability, optionAis least likely to describe the fall in performance because it deals with acceptance criteria rather than real-time performance issues.
NEW QUESTION # 59
In a certain coffee producing region of Colombia, there have been some severe weather storms, resulting in massive losses in production. This caused a massive drop in stock price of coffee.
Which ONE of the following types of testing SHOULD be performed for a machine learning model for stock-price prediction to detect influence of such phenomenon as above on price of coffee stock.
SELECT ONE OPTION
Answer: C
Explanation:
* Type of Testing for Stock-Price Prediction Models: Concept drift refers to the change in the statistical properties of the target variable over time. Severe weather storms causing massive losses in coffee production and affecting stock prices would require testing for concept drift to ensure that the model adapts to new patterns in data over time.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 7.6 Testing for Concept Drift, which explains the need to test for concept drift in models that might be affected by changing external factors.
NEW QUESTION # 60
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:
Answer: A
Explanation:
The correct answer is B. Intra cluster . The scenario describes an unsupervised clustering model, where the objective is to group data points based on similarity. The requested measurement is specifically how similar the data points are within each group , which maps directly to intra-cluster metrics . The CT-AI syllabus states that inter-cluster metrics, intra-cluster metrics, and the silhouette coefficient may be used for unsupervised clustering problems.
Option A, ROC, and option C, AUC, are used for supervised classification, especially binary classifiers, to evaluate how well the classifier distinguishes between classes. Option D, recall, is also a supervised classification metric and measures how many actual positives were correctly predicted. None of those metrics is designed to evaluate compactness or similarity within clusters. Intra-cluster measurement is the most direct metric family because it evaluates the cohesion of a cluster: the closer or more similar the members are, the stronger the clustering result is likely to be.
References/topics: CT-AI Syllabus Chapter 5, Section 5.2 "Additional ML Functional Performance Metrics for Classification, Regression and Clustering"; Section 5.4 "Selecting ML Functional Performance Metrics."
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NEW QUESTION # 61
A data scientist is performing unsupervised learning on a set of financial records relating to previous loan applications, and trying to predict defaults on future loans. They are reporting poor functional performance because of data issues.
Which ONE of the below is LEAST likely to be a contributory factor?
Answer: A
Explanation:
Irrelevant data included in the account records is less likely to contribute to poor functional performance in unsupervised learning, especially compared to missing records, missing key data (such as whether loans were granted or repaid), or inconsistent pre-processing. While irrelevant data can affect the quality of the model, missing or inconsistent data typically has a more direct negative impact on unsupervised learning models.
NEW QUESTION # 62
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