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| Section | Weight | Objectives |
|---|---|---|
| Neural Networks and Deep Learning | 20% | - Overfitting and Underfitting - Neural Network Architecture Basics - Testing Deep Learning Models - Model Training and Evaluation Metrics |
| Basics of AI | 8% | - Machine Learning Types (Supervised, Unsupervised, Reinforcement) - Data Concepts (Training, Validation, Test Data) - Definitions and Terminology - AI Types and Techniques |
| Testing AI-Based Systems | 20% | - Test Levels for AI-Based Systems - Testing AI-Specific Quality Characteristics - Challenges in Testing AI-Based Systems - Bias and Fairness Considerations |
| AI Test Methods and Techniques | 20% | - Black-Box Testing for AI Systems - Test Oracle Techniques for AI - Adversarial Testing - Metamorphic Testing |
| Testing AI-Specific Quality Characteristics | 20% | - Fairness and Bias Detection - Accuracy, Precision, Recall, F1-Score - Explainability and Interpretability - Robustness Testing |
| Practical Considerations | 12% | - Automation in AI Testing - Test Data Quality and Preparation - Test Environment Setup for AI - Documentation and Reporting |
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NEW QUESTION # 135
Which ONE of the following characteristics is the least likely to cause safety related issues for an Al system?
SELECT ONE OPTION
Answer: C
Explanation:
The question asks which characteristic is least likely to cause safety-related issues for an AI system. Let's evaluate each option:
* Non-determinism (A): Non-deterministic systems can produce different outcomes even with the same inputs, which can lead to unpredictable behavior and potential safety issues.
* Robustness (B): Robustness refers to the ability of the system to handle errors, anomalies, and unexpected inputs gracefully. A robust system is less likely to cause safety issues because it can maintain functionality under varied conditions.
* High complexity (C): High complexity in AI systems can lead to difficulties in understanding, predicting, and managing the system's behavior, which can cause safety-related issues.
* Self-learning (D): Self-learning systems adapt based on new data, which can lead to unexpected changes in behavior. If not properly monitored and controlled, this can result in safety issues.
:
ISTQB CT-AI Syllabus Section 2.8 on Safety and AI discusses various factors affecting the safety of AI systems, emphasizing the importance of robustness in maintaining safe operation.
NEW QUESTION # 136
An e-commerce developer built an application for automatic classification of online products in order to allow customers to select products faster. The goal is to provide more relevant products to the user based on prior purchases.
Which of the following factors is necessary for a supervised machine learning algorithm to be successful?
Answer: A
Explanation:
Supervised machine learning requires correctly labeled data to train an effective model. The learning process relies on input-output mappings where each training example consists of an input (features) and a correctly labeled output (target variable). Incorrect labeling can significantly degrade model performance.
* Supervised Learning Process
* The algorithm learns from labeled data, mapping inputs to correct outputs during training.
* If labels are incorrect, the model will learn incorrect relationships and produce unreliable predictions.
* Quality of Training Data
* The accuracy of any supervised ML model ishighly dependent on the quality of labels.
* Poorly labeled data leads to mislabeled training sets, resulting inbiased or underperforming models.
* Error Minimization and Model Accuracy
* Incorrectly labeled data affects theconfusion matrix, reducing precision, recall, and accuracy.
* It leads to overfitting or underfitting, which decreases the model's ability to generalize.
* Industry Standard Practices
* Many AI development teams spend a significant amount of time ondata annotation and quality controlto ensure high-quality labeled datasets.
* (B) Minimizing the amount of time spent training the algorithm#(Incorrect)
* While reducing training time is important for efficiency, the quality of training is more critical. A well-trained model takes time to process large datasets and optimize its parameters.
* (C) Selecting the correct data pipeline for the ML training#(Incorrect)
* A good data pipeline helps, butit does not directly impact learning successas much as labeling does.Even a well-optimized pipeline cannot fix incorrect labels.
* (D) Grouping similar products together before feeding them into the algorithm#(Incorrect)
* This describesclustering, which is anunsupervised learning technique. Supervised learningrequires labeled examples, not just grouping of data.
* Labeled data is necessary for supervised learning."For supervised learning, it is necessary to have properly labeled data."
* Data labeling errors can impact performance."Supervised learning assumes that the data is correctly labeled by the data annotators.However, it is rare in practice for all items in a dataset to be labeled correctly." Why Labeling is Critical?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, ascorrectly labeled data is essential for supervised machine learning success.
NEW QUESTION # 137
You are a test manager planning testing for an invoice financing company. The company buy unpaid invoices from companies and provide them with immediate cash. The company is replacing their existing conventional system, which takes company accounting records as inputs, with an ML system that classifies each invoice for sales as something that should, or should not be bought. Significant historical production data is available. It is important that invoices are not bought incorrectly. Which ONE of the following test techniques would be MOST appropriate for you to plan for system testing?
Answer: A
Explanation:
The correct answer is B. Back-to-back testing . The scenario contains the key conditions for this technique:
an existing conventional system with similar functionality, a new ML-based replacement, and significant historical production data. The CT-AI syllabus explains that back-to-back testing uses an alternative version of the system as a pseudo-oracle , comparing its outputs with those produced by the system under test. The pseudo-oracle may be an existing system or even conventional non-AI software.
This is stronger than A/B testing because the objective is not primarily to compare two production variants statistically to determine which is better; it is to detect classification defects in a replacement system. It is also stronger than metamorphic testing because an existing oracle-like comparator is available, reducing the need to rely only on relative output relations. Experience-based testing is useful as a supplement, but it would not be the primary systematic technique where historical production inputs and an existing system are available.
References/topics: CT-AI Syllabus Chapter 9, Section 9.3 "Back-to-Back Testing"; Section 9.7
"Selecting Test Techniques for AI-Based Systems."
=========
NEW QUESTION # 138
An image classification system is being trained for classifying faces of humans. The distribution of the data is 70% ethnicity A and 30% for ethnicities B, C and D. Based ONLY on the above information, which of the following options BEST describes the situation of this image classification system?
SELECT ONE OPTION
Answer: D
Explanation:
A . This is an example of expert system bias.
Expert system bias refers to bias introduced by the rules or logic defined by experts in the system, not by the data distribution.
B . This is an example of sample bias.
Sample bias occurs when the training data is not representative of the overall population that the model will encounter in practice. In this case, the over-representation of ethnicity A (70%) compared to B, C, and D (30%) creates a sample bias, as the model may become biased towards better performance on ethnicity A.
C . This is an example of hyperparameter bias.
Hyperparameter bias relates to the settings and configurations used during the training process, not the data distribution itself.
D . This is an example of algorithmic bias.
Algorithmic bias refers to biases introduced by the algorithmic processes and decision-making rules, not directly by the distribution of training data.
Based on the provided information, option B (sample bias) best describes the situation because the training data is skewed towards ethnicity A, potentially leading to biased model performance.
NEW QUESTION # 139
Which ONE of the following options is an example that BEST describes a system with Al-based autonomous functions?
Answer: D
Explanation:
AI-Based Autonomous Functions: An AI-based autonomous system is one that can respond to its environment without human intervention. The other options either involve human decisions or do not use AI at all.
NEW QUESTION # 140
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