CT-AI Online Practice Questions

Explanation:
The syllabus describes exploratory testing as especially useful when there are poor specifications or test oracle problems:
“Exploratory testing is especially useful when there are poor specifications or test oracle problems, which is often the case for AI-based systems.”
(Reference: ISTQB CT-AI Syllabus v1.0, Section 9.6, page 70 of 99)

Explanation:
The syllabus states:
"Transparency: This is considered to be the ease with which the algorithm and training data used to generate the model can be determined."
Access to the training data is an example of transparency. (Reference: ISTQB CT-AI Syllabus v1.0, Section 2.7, page 24 of 99)

Explanation:
For AI-enabled medical devices, the most required aspects for certification are safety, transparency, and side effects.
Here’s why:
Safety (Aspect III): Critical for ensuring that the AI system does not cause harm to patients.
Transparency (Aspect IV): Important for understanding and verifying the decisions made by the AI system.
Side Effects (Aspect V): Necessary to identify and mitigate any unintended consequences of the AI system.
Why Not Other Options:
Autonomy and Maintainability (Aspects I and II): While important, they are secondary to the immediate concerns of safety, transparency, and managing side effects in life-critical processes.
Reference: This explanation is aligned with the critical quality characteristics for AI-based systems as mentioned in the ISTQB CT-AI syllabus, focusing on the certification of medical devices​.

Explanation:
The process of developing a machine learning model typically involves the use of three types of datasets:
Training Data: This is used to train the model, i.e., to learn the patterns and relationships in the data.
Validation Data: This is used to tune the model’s hyperparameters and to prevent overfitting during the training process.
Test Data: This is used to evaluate the final model’s performance and to estimate how it will perform on unseen data.
Let's analyze each option:
A. Training data - validation data - test data
This option correctly includes all three types of datasets used in the process of creating and validating a model. The training data is used for learning, validation data for tuning, and test data for final evaluation.
B. Training data - validation data
This option misses the test data, which is crucial for evaluating the model’s performance on unseen data after the training and validation phases.
C. Training data - test data
This option misses the validation data, which is important for tuning the model and preventing overfitting during training.
D. Validation data - test data
This option misses the training data, which is essential for the initial learning phase of the model.
Therefore, the correct answer is A because it includes all necessary datasets used during the process of learning and creating the model: training, validation, and test data​.

Explanation:
From the confusion matrix:
True Positives (TP) = 15
False Positives (FP) = 5
False Negatives (FN) = 15
True Negatives (TN) = 65
Accuracy= (TP + TN) / Total
= (15 + 65) / 100 =80%
Precision= TP / (TP + FP)
= 15/(15+5)
= 15/20 =75%
Section3.2 C Functional Performance Criteriain the syllabus explains accuracy and precision exactly these ways when evaluating ML classification performance.
Option B is therefore the only correct pair of values.