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| Section | Weight | Objectives |
|---|---|---|
| Testing AI-Specific Quality Characteristics | 20% | - Robustness Testing - Explainability and Interpretability - Accuracy, Precision, Recall, F1-Score - Fairness and Bias Detection |
| Neural Networks and Deep Learning | 20% | - Neural Network Architecture Basics - Model Training and Evaluation Metrics - Overfitting and Underfitting - Testing Deep Learning Models |
| Basics of AI | 8% | - AI Types and Techniques - Machine Learning Types (Supervised, Unsupervised, Reinforcement) - Definitions and Terminology - Data Concepts (Training, Validation, Test Data) |
| Practical Considerations | 12% | - Test Environment Setup for AI - Test Data Quality and Preparation - Automation in AI Testing - Documentation and Reporting |
| Testing AI-Based Systems | 20% | - Challenges in Testing AI-Based Systems - Testing AI-Specific Quality Characteristics - Bias and Fairness Considerations - Test Levels for AI-Based Systems |
| AI Test Methods and Techniques | 20% | - Black-Box Testing for AI Systems - Metamorphic Testing - Test Oracle Techniques for AI - Adversarial Testing |
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NEW QUESTION # 104
Which of the following is an example of a clustering problem that can be resolved by unsupervised learning?
Answer: D
Explanation:
Clustering is a form ofunsupervised learning, which groups data points based onsimilarities without predefined labels. According toISTQB CT-AI Syllabus, clustering is used in scenarios where:
* The objective is to find natural groupings in data.
* The dataset does not have labeled outputs.
* Patterns and structures need to be identified automatically.
Analyzing the answer choices:
* A. Associating shoppers with their shopping tendencies # Correct
* Shoppers can be grouped based on purchasing behaviors(e.g., luxury shoppers vs. budget- conscious shoppers), which is a typical clustering application in market segmentation.
* B. Grouping individual fish together based on their types of fins # Incorrect
* If thetypes of fins are labeled, it becomes aclassification problem, which requires supervised learning.
* C. Classifying muffin purchases based on packaging attractiveness # Incorrect
* Classification, not clustering, because attractiveness scores or labels must be predefined.
* D. Estimating the expected purchase of cat food after an ad campaign # Incorrect
* This is a prediction task, best suited forregression models, which are part of supervised learning.
Thus,Option A is the best answer, asclusteringis used togroup shoppers based on tendencies without predefined labels.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 3.1.2 (Unsupervised Learning - Clustering and Association)
* ISTQB CT-AI Syllabus v1.0, Section 3.3 (Selecting a Form of ML - Clustering).
NEW QUESTION # 105
Which of the following is a dataset issue that can be resolved using pre-processing?
Answer: B
Explanation:
Pre-processing is an essential step in data preparation that ensures data is clean, formatted correctly, and structured for effective machine learning (ML) model training. One common issue that can be resolved during pre-processing isnumbers stored as strings.
Explanation of Answer Choices:
* Option A: Insufficient data
* Incorrect. Pre-processing cannot resolve insufficient data. If data is lacking, techniques like data augmentation or external data collection are needed.
* Option B: Invalid data
* Incorrect. While pre-processing can identify and handle some forms of invalid data (e.g., missing values, duplicate entries), it does not resolve all invalid data issues. Some cases may require domain expertise to determine validity.
* Option C: Wanted outliers
* Incorrect. Pre-processing usually focuses on handling unwanted outliers. Wanted outliers may need to be preserved, which is more of a data selection decision rather than pre-processing.
* Option D: Numbers stored as strings
* Correct. One of the key functions of data pre-processing isdata transformation, which includes converting incorrectly formatted data types, such as numbers stored as strings, into their correct numerical format.
ISTQB CT-AI Syllabus References:
* Data Pre-Processing Steps:"Transformation: The format of the given data is changed (e.g., breaking an address held as a string into its constituent parts, dropping a field holding a random identifier, converting categorical data into numerical data, changing image formats)".
NEW QUESTION # 106
Which of the following characteristics of AI-based systems make it more difficult to ensure they are safe?
Answer: D
Explanation:
The syllabus states that non-determinism is one of the key challenges for ensuring safety in AI-based systems:
"The characteristics of AI-based systems that make it more difficult to ensure they are safe... include:
complexity, non-determinism, probabilistic nature, self-learning, lack of transparency, interpretability and explainability, and lack of robustness." (Reference: ISTQB CT-AI Syllabus v1.0, Section 2.8, page 25 of 99)
NEW QUESTION # 107
Pairwise testing can be used in the context of self-driving cars for controlling an explosion in the number of combinations of parameters.
Which ONE of the following options is LEAST likely to be a reason for this incredible growth of parameters?
Answer: C
Explanation:
While evaluating machine learning (ML) model performance is crucial, it does not directly contribute to the explosion of parameter combinations in the same way that road types, weather conditions, and car features do. Metrics are used to measure and assess performance but are not themselves variable conditions that the system must handle.
NEW QUESTION # 108
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: B
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 # 109
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