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| Section | Weight | Objectives |
|---|---|---|
| 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 |
| Testing AI-Specific Quality Characteristics | 20% | - Accuracy, Precision, Recall, F1-Score - Robustness Testing - Fairness and Bias Detection - Explainability and Interpretability |
| Basics of AI | 8% | - Definitions and Terminology - AI Types and Techniques - Data Concepts (Training, Validation, Test Data) - Machine Learning Types (Supervised, Unsupervised, Reinforcement) |
| Neural Networks and Deep Learning | 20% | - Model Training and Evaluation Metrics - Overfitting and Underfitting - Neural Network Architecture Basics - Testing Deep Learning Models |
| AI Test Methods and Techniques | 20% | - Test Oracle Techniques for AI - Black-Box Testing for AI Systems - Adversarial Testing - Metamorphic Testing |
| Practical Considerations | 12% | - Documentation and Reporting - Automation in AI Testing - Test Environment Setup for AI - Test Data Quality and Preparation |
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NEW QUESTION # 119
An airline has created a ML model to project fuel requirements for future flights. The model imports weather data such as wind speeds and temperatures, calculates flight routes based on historical routings from air traffic control, and estimates loads from average passenger and baggage weights. The model performed within an acceptable standard for the airline throughout the summer but as winter set in the load weights became less accurate. After some exploratory data analysis it became apparent that luggage weights were higher in the winter than in summer.
Which of the following statements BEST describes the problem and how it could have been prevented?
Answer: C
Explanation:
The syllabus states:
"Concept drift occurs when the operational environment changes without the trained model changing correspondingly. The outputs of the model become less accurate and less useful.
Therefore, the operational model should be regularly evaluated against its acceptance criteria."
NEW QUESTION # 120
Which ONE of the following tests is LEAST likely to be performed during the ML model testing phase?
SELECT ONE OPTION
Answer: D
Explanation:
The question asks which test is least likely to be performed during the ML model testing phase. Let's consider each option:
* Testing the accuracy of the classification model (A): Accuracy testing is a fundamental part of the ML model testing phase. It ensures that the model correctly classifies the data as intended and meets the required performance metrics.
* Testing the API of the service powered by the ML model (B): Testing the API is crucial, especially if the ML model is deployed as part of a service. This ensures that the service integrates well with other systems and that the API performs as expected.
* Testing the speed of the training of the model (C): This is least likely to be part of the ML model testing phase. The speed of training is more relevant during the development phase when optimizing and tuning the model. During testing, the focus is more on the model's performance and behavior rather than how quickly it was trained.
* Testing the speed of the prediction by the model (D): Testing the speed of prediction is important to ensure that the model meets performance requirements in a production environment, especially for real- time applications.
References:
* ISTQB CT-AI Syllabus Section 3.2 on ML Workflow and Section 5 on ML Functional Performance Metrics discuss the focus of testing during the model testing phase, which includes accuracy and prediction speed but not the training speed.
NEW QUESTION # 121
Which option describes a reasonable application of AIB testing for a self-learning system after it has changed its behavior due to user input?
Answer: C
Explanation:
According to Section4.6 - AI Behaviour Testing (AIB Testing)of the ISTQB CT-AI syllabus, AIB testing is used to evaluate changes in the functional behavior of self-learning systems. The core principle iscomparing pre-change and post-change model behavior using the same test inputs, so that any difference in outputs can be attributed to the model's learning and not to differences in input data. This directly corresponds to Option C.
NEW QUESTION # 122
Which performance metric is BEST suited to assess the quality of trained models detecting fraudulent credit card transactions?
Answer: A
Explanation:
The ISTQB CT-AI syllabus explains in Section3.2 - Functional Performance Criteria of ML Modelsthat accuracy becomes unreliable when class imbalance exists. In fraud detection, more than 99% of transactions are non-fraudulent, meaning the dataset is extremely imbalanced.
Because accuracy counts all correct non-fraudulent classifications, it will appear artificially high, even if the fraud detection performance is poor. Therefore, accuracy is not suitable for evaluating fraud detection systems.
The syllabus further explains that sensitivity (recall)captures the proportion of correctly identified fraudulent cases. This metric is important, as missing fraudulent events can cause high financial loss. However, the client also stresses thatlegitimate transactions must be correctly identified, meaning false positives must be minimized to maintain customer satisfaction.
The F1 score, defined as the harmonic mean of precision and recall, balances both:
Precision protects legitimate customers by minimizing false alarms.
Recall ensures fraudulent transactions are detected.
Section 3.2 emphasizes that when both false positives and false negatives have significant consequences, and the data is highly imbalanced, F1 is the most appropriate metricbecause it reflects the combined importance of detecting fraud while avoiding unnecessary alerts. Thus, Option C is the correct choice.
NEW QUESTION # 123
Which of the following technologies for implementing AI is considered to be a reasoning technique?
Answer: A
Explanation:
The ISTQB Certified Tester AI Testing Syllabus v1.0explicitly categorizes different AI implementation technologies in Section1.4 - AI Technologies. Within this section, AI methods are grouped into categories, one of which is"Reasoning techniques."These reasoning techniques includerule engines, deductive classifiers, case-based reasoning, and procedural reasoning.
Because deductive classifiers are directly listed under this set of reasoning approaches, they are recognized as a reasoning-based AI technology.
Reasoning techniques differ from machine learning approaches because they rely onstructured, predefined rules or logicto reach conclusions. Deductive classifiers use logical inference and symbolic reasoning to classify inputs by applying encoded knowledge. This makes them fundamentally different from statistical or data-driven ML algorithms.
The other options--Linear regression,Random Forest, and Genetic algorithms--are listed by the syllabus asmachine learning techniques, not reasoning methods. Linear regression performs numerical prediction, Random Forest is an ensemble decision-tree ML model, and genetic algorithms are optimization-based ML approaches inspired by evolutionary processes. None of these involve symbolic logical deduction.
Thus, based on the authoritative definitions in the syllabus, Deductive classifiers (Option A)is the only technology classified as a reasoning technique.
NEW QUESTION # 124
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