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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Test Approaches and Techniques for AI Systems | 10% | - Exploratory testing and red teaming - Risk-based testing - Test environment and tool considerations - A/B testing and back-to-back testing |
| Topic 2: Input Data Testing for Machine Learning Systems | 20% | - Testing for bias, representativeness, and completeness - Label verification and ground truth assessment - Data quality attributes - Validation of data pipelines and preprocessing |
| Topic 3: Machine Learning Development Testing | 10% | - Regression testing for retrained models - Testing ML development workflows - Testing MLOps and deployment pipelines |
| Topic 4: Test Levels and Machine Learning Systems | 15% | - Testing across ML lifecycle stages - Unit, integration, system, and acceptance testing for ML - Test strategies for AI projects |
| Topic 5: Model Testing for Machine Learning Systems | 20% | - Metamorphic and statistical testing - Adversarial and robustness testing - Drift detection and monitoring - Explainability and transparency testing - Model performance and accuracy testing |
| Topic 6: Introduction to Testing AI-Based Systems | 10% | - Challenges in testing AI-based systems - Characteristics of AI and ML systems - AI-specific quality characteristics |
| Topic 7: Testing Generative AI and Large Language Models | 15% | - Evaluation of generative AI outputs - Specific risks and test approaches for LLMs - Generative AI fundamentals |
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NEW QUESTION # 128
You are developing a "flower" ML model... Which of the following describes an objection that you can NEGLECT in your risk assessment?
Answer: A
Explanation:
The ISTQB CT-AI syllabus explains that reusing pre-trained models is strongly related to similarity between the original task and the new task. Section1.8 - Pre-trained Models and Transfer Learningstates that reuse is effective when the new task is similar to the original one, such as adapting a cat-classifier to classify dog breeds. The syllabus warns about risks related toinput differences,data preparation inconsistencies, inherited shortcomings, and explain ability issues. These are legitimate objections (matching options A, B, and C) because large differences in image inputs or patterns can undermine transfer learning; misclassification risk can increase; and explainability often decreases when reusing pre-trained models .
However, output differences are NOT a valid concernhere. Both the leaf-based and flower-based ML models classify the same plant species, meaning theiroutputs are identical. The syllabus does not identify output mismatch as a transfer-learning risk. Real risks concerninputs,bias inheritance,model transparency, andtraining differences--not output labels. Therefore, Option D describes an objection that can be safely neglected, because output classes are the same and do not hinder reuse.
NEW QUESTION # 129
Which ONE of the following options describes a scenario of A/B testing the LEAST?
SELECT ONE OPTION
Answer: A
Explanation:
A/B testing, also known as split testing, is a method used to compare two versions of a product or system to determine which one performs better. It is widely used in web development, marketing, and machine learning to optimize user experiences and model performance. Here's why option C is the least descriptive of an A/B testing scenario:
Understanding A/B Testing:
In A/B testing, two versions (A and B) of a system or feature are tested against each other. The objective is to measure which version performs better based on predefined metrics such as user engagement, conversion rates, or other performance indicators.
Application in Machine Learning:
In ML systems, A/B testing might involve comparing two different models, algorithms, or system configurations on the same set of data to observe which yields better results.
Why Option C is the Least Descriptive:
Option C describes comparing the performance of an ML system on two different input datasets. This scenario focuses on the input data variation rather than the comparison of system versions or features, which is the essence of A/B testing. A/B testing typically involves a controlled experiment with two versions being tested under the same conditions, not different datasets.
Clarifying the Other Options:
A . A comparison of two different websites for the same company to observe from a user acceptance perspective: This is a classic example of A/B testing where two versions of a website are compared.
B . A comparison of two different offers in a recommendation system to decide on the more effective offer for the same users: This is another example of A/B testing in a recommendation system.
D . A comparison of the performance of two different ML implementations on the same input data: This fits the A/B testing model where two implementations are compared under the same conditions.
Reference:
ISTQB CT-AI Syllabus, Section 9.4, A/B Testing, explains the methodology and application of A/B testing in various contexts.
"Understanding A/B Testing" (ISTQB CT-AI Syllabus).
NEW QUESTION # 130
Which ONE of the below is MOST likely to be a challenge in ML data preparation?
Answer: D
Explanation:
In machine learning, data preparation is often the most time-consuming and costly phase. It involves collecting, cleaning, and transforming raw data into a usable form for training models.
This can require significant effort and resources, making it the most likely challenge compared to the other options listed.
NEW QUESTION # 131
You are developing a "flower" ML model... Which of the following describes an objection that you can NEGLECT in your risk assessment?
Choose ONE option (1 out of 4)
Answer: A
Explanation:
The ISTQB CT-AI syllabus explains that reusing pre-trained models is strongly related tosimilarity between the original task and the new task. Section1.8 - Pre-trained Models and Transfer Learningstates that reuse is effective when the new task is similar to the original one, such as adapting a cat-classifier to classify dog breeds. The syllabus warns about risks related toinput differences,data preparation inconsistencies, inherited shortcomings, andexplainability issues. These are legitimate objections (matching options A, B, and C) because large differences in image inputs or patterns can undermine transfer learning; misclassification risk can increase; and explainability often decreases when reusing pre-trained models .
However,output differences are NOT a valid concernhere. Both the leaf-based and flower-based ML models classifythe same plant species, meaning theiroutputs are identical. The syllabus does not identify output mismatch as a transfer-learning risk. Real risks concerninputs,bias inheritance,model transparency, andtraining differences-not output labels. Therefore, OptionDdescribes an objection that can be safely neglected, because output classes are the same and do not hinder reuse.
NEW QUESTION # 132
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: C
Explanation:
The syllabus explains that supervised learning requires correctly labeled data so the algorithm can learn the relationship between input features and output labels:
"In supervised learning, the algorithm creates the ML model from labeled data during the training phase. The labeled data is used to infer the relationship between the input data and output labels." (Reference: ISTQB CT-AI Syllabus v1.0, Section 3.1.1)
NEW QUESTION # 133
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