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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Testing AI-Based Systems | 20% | - Bias and Fairness Considerations - Test Levels for AI-Based Systems - Testing AI-Specific Quality Characteristics - Challenges in Testing AI-Based Systems |
| Topic 2: Neural Networks and Deep Learning | 20% | - Neural Network Architecture Basics - Model Training and Evaluation Metrics - Testing Deep Learning Models - Overfitting and Underfitting |
| Topic 3: Testing AI-Specific Quality Characteristics | 20% | - Fairness and Bias Detection - Accuracy, Precision, Recall, F1-Score - Robustness Testing - Explainability and Interpretability |
| Topic 4: AI Test Methods and Techniques | 20% | - Metamorphic Testing - Adversarial Testing - Black-Box Testing for AI Systems - Test Oracle Techniques for AI |
| Topic 5: Practical Considerations | 12% | - Documentation and Reporting - Test Environment Setup for AI - Test Data Quality and Preparation - Automation in AI Testing |
| Topic 6: Basics of AI | 8% | - Definitions and Terminology - Data Concepts (Training, Validation, Test Data) - AI Types and Techniques - Machine Learning Types (Supervised, Unsupervised, Reinforcement) |
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NEW QUESTION # 160
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: D
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 # 161
Before deployment of an AI-based system, a developer is expected to demonstrate in a test environment how decisions are made. Which of the following characteristics does decision making fall under?
Answer: C
Explanation:
The syllabus definesexplainabilityas the ability to understand how the AI-based system comes up with a particular result:
"Explainability is considered to be the ease with which users can determine how the AI-based system comes up with a particular result." (Reference: ISTQB CT-AI Syllabus v1.0, Section 2.7)
NEW QUESTION # 162
You have been asked for your opinion on the ML approach to be used for a new age verification system. A training dataset is available with many pictures of faces and the subject's age. When new users sign up to a mobile app, they have to take a picture of their face. If the system believes they are under 18 years old it will ask them to provide a picture of their passport to prove their age. Which ONE of the following approaches do you expect to be MOST likely to succeed?
Answer: C
Explanation:
The correct answer is B . The operational decision required by the system is categorical: the user is either treated as under 18 or not under 18 for the purpose of requesting passport evidence. The CT-AI syllabus states that classification is used when an input must be assigned to one of a few predefined classes, and that supervised learning requires labelled data. Here, the training dataset contains facial images and ages, so it can be converted into labelled classes such as "under 18" and "18 or over." Option A is weaker because unsupervised learning is used where labels are not provided and the goal is to identify patterns or clusters. In this case, useful labels already exist. Option C could predict a numeric age using regression, but the business decision is binary, so classification is the more direct and controlled approach. Option D is inappropriate because reinforcement learning is better suited to sequential decision- making through interaction with an environment, states, and rewards. The passport feedback provides useful labelled evidence for retraining, supporting supervised classification.
References/topics: CT-AI Syllabus Chapter 3, Sections 3.1.1 and 3.3 "Selecting a Form of ML."
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NEW QUESTION # 163
Which ONE of the following options represents a technology MOST TYPICALLY used to implement Al?
Answer: A
Explanation:
Technology Most Typically Used to Implement AI: Genetic algorithms are a well-known technique used in AI . They are inspired by the process of natural selection and are used to find approximate solutions to optimization and search problems. Unlike search engines, procedural programming, or case control structures, genetic algorithms are specifically designed for evolving solutions and are commonly employed in AI implementations.
NEW QUESTION # 164
The stakeholders of a machine learning model have confirmed that they understand the objective and purpose of the model, and ensured that the proposed model aligns with their business priorities. They have also selected a framework and a machine learning model that they will be using. What should be the next step to progress along the machine learning workflow?
Answer: C
Explanation:
The ML workflow typically involves iterative steps, beginning with data preparation once the model and framework are selected. The syllabus explains:
"The steps shown in Figure 1 (the ML workflow) do not include the integration of the ML model with the non- ML parts of the overall system. Typically, ML models cannot be deployed in isolation and need to be integrated with the non-ML parts... The next step would be data preparation as part of the ML workflow to provide input data to support training by an ML algorithm or prediction by an ML model." (Reference: ISTQB CT-AI Syllabus v1.0, Sections 3.2 & 4.1)
NEW QUESTION # 165
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