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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Neural Networks and Deep Learning | 20% | - Overfitting and Underfitting - Neural Network Architecture Basics - Testing Deep Learning Models - Model Training and Evaluation Metrics |
| Topic 2: Testing AI-Specific Quality Characteristics | 20% | - Accuracy, Precision, Recall, F1-Score - Robustness Testing - Explainability and Interpretability - Fairness and Bias Detection |
| Topic 3: Basics of AI | 8% | - AI Types and Techniques - Machine Learning Types (Supervised, Unsupervised, Reinforcement) - Definitions and Terminology - Data Concepts (Training, Validation, Test Data) |
| Topic 4: Testing AI-Based Systems | 20% | - Challenges in Testing AI-Based Systems - Bias and Fairness Considerations - Testing AI-Specific Quality Characteristics - Test Levels for AI-Based Systems |
| Topic 5: Practical Considerations | 12% | - Automation in AI Testing - Test Environment Setup for AI - Documentation and Reporting - Test Data Quality and Preparation |
| Topic 6: AI Test Methods and Techniques | 20% | - Metamorphic Testing - Black-Box Testing for AI Systems - Adversarial Testing - Test Oracle Techniques for AI |
>> Current ISTQB CT-AI Exam Content <<
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NEW QUESTION # 44
A test engineer is planning the best functional performance metrics to evaluate an unsupervised learning model. The model groups data points based on their similarity. The test engineer wants to measure how similar the data points in each group actually are.
Which is the MOST likely metric they should use?
Answer: C
Explanation:
The most appropriate metric for evaluating the similarity of data points within each group in an unsupervised learning model is intra-cluster. This metric measures how similar the data points within each cluster are to one another. The goal is to have high intra-cluster similarity, meaning the data points within a group should be similar to each other.
NEW QUESTION # 45
Which ONE of the following options describes a scenario of A/B testing the LEAST?
SELECT ONE OPTION
Answer: D
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.
References:
* 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 # 46
You have access to the training data that was used to train an AI-based system. You can review this information and use it as a guideline when creating your tests. What type of characteristic is this?
Answer: A
Explanation:
AI-based systems can sometimes behave like black boxes, where the internal decision-making process is unclear. Transparency refers to the ability to inspect and understand the training data, algorithms, and decision-making process of the AI system.
Transparency ensures that testers and stakeholders can review how an AI system was trained.
Access to training data is a key factor in transparency because it allows testers to analyze biases, completeness, and representativeness of the dataset.
Transparency is an essential characteristic of explainable AI (XAI). Having access to training data means that testers can investigate how data influences AI behavior.
Regulatory and ethical AI guidelines emphasize transparency. Many AI ethics frameworks, such as GDPR and Trustworthy AI guidelines, recommend transparency to ensure fair and explainable AI decision-making.
NEW QUESTION # 47
The following confusion matrix represents the functional performance of a classifier.
Which ONE of the following is the correct calculation for the accuracy of the classifier?
Answer: D
Explanation:
The correct answer is D . In the confusion matrix, the values are: true positive TP = 60 , false positive FP = 20
, false negative FN = 9 , and true negative TN = 11 . Accuracy measures the percentage of all classifications that are correct. The CT-AI syllabus defines the formula as: Accuracy = (TP + TN) / (TP + TN + FP + FN)
ร 100% .
Substituting the given values gives: (60 + 11) / (60 + 11 + 20 + 9) ร 100% = 71 / 100 ร 100% = 71% .
Option A is mathematically invalid and does not apply the confusion matrix formula. Option B calculates recall-like behaviour using TP / (TP + FN) rather than accuracy. Option C calculates the F1-score using precision and recall, not accuracy. Option D is therefore the only option that correctly counts both forms of correct prediction: correctly predicted positives and correctly predicted negatives.
References/topics: CT-AI Syllabus Chapter 5, Section 5.1 "Confusion Matrix."
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NEW QUESTION # 48
A beer company is trying to understand how much recognition its logo has in the market. It plans to do that by monitoring images on various social media platforms using a pre-trained neural network for logo detection.
This particular model has been trained by looking for words, as well as matching colors on social media images. The company logo has a big word across the middle with a bold blue and magenta border.
Which associated risk is most likely to occur when using this pre-trained model?
Answer: D
Explanation:
A major risk when using apre-trained neural networkfor logo detection is that it mayinherit biases and defectsfrom the original dataset and training process. This means that the model could misidentify or fail to recognize certain logos due to:
* Differences in data preparation:The original training data may have used a different preprocessing method than the new dataset, leading to inconsistencies.
* Limited transparency:The exact details of the dataset and biases within it may not be known, which can cause unexpected behavior.
* Bias in logo detection:If the model was trained on a dataset with certain color or text preferences, it may disproportionately misidentify logos with similar characteristics.
This inherited bias can result in:
* False Positives:Recognizing other brand logos as the beer company's logo.
* False Negatives:Failing to detect the actual logo when variations occur (e.g., different lighting or partial visibility).
* Algorithmic Bias:The model may favor certain shapes or color contrasts due to biased training data.
Thus,the most appropriate risk associated with using this pre-trained model is inherited bias.
* Section 1.8.3 - Risks of Using Pre-Trained Models and Transfer Learningexplains how pre-trained models may inheritbiases and undocumented defectsthat affect performance in a new environment.
Reference from ISTQB Certified Tester AI Testing Study Guide:
NEW QUESTION # 49
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