CT-AI Dumps with Practice Exam Questions Answers

 Challenges for Acquiring Test Data in ML Systems: Compliance needs, the changing nature of data over time, and sourcing data from public sources are significant challenges. Data being generated quickly is generally not a challenge; it can actually be beneficial as it provides more data for training and testing.

 Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Data Preparation and Data Quality Issues​​​​.

 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.

 Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Autonomy and Testing Autonomous AI-Based Systems​​​​.

The question asks which deficiency is most likely to be discovered by the test expert given the scenario of poor real-world performance despite good isolated accuracy.

A lack of similarity between the training and testing data (A): This is a common issue in ML where the model performs well on training data but poorly on real-world data due to a lack of representativeness in the training data. This leads to poor generalization to new, unseen data.

The input data has not been tested for quality prior to use for testing (B): While data quality is important, this option is less likely to be the primary reason for the described issue compared to the representativeness of training data.

A lack of focus on choosing the right functional-performance metrics (C): Proper metrics are crucial, but the issue described seems more related to the data mismatch rather than metric selection.

A lack of focus on non-functional requirements testing (D): Non-functional requirements are important, but the scenario specifically mentions issues with detecting real cancer cases, pointing more towards data issues.

References:

ISTQB CT-AI Syllabus Section 4.2 on Training, Validation, and Test Datasets emphasizes the importance of using representative datasets to ensure the model generalizes well to real-world data.

Sample Exam Questions document, Question #40 addresses issues related to data representativeness and model generalization.

A. Black box attacks based on adversarial examples create an exact duplicate model of the original.

Black box attacks do not create an exact duplicate model. Instead, they exploit the model by querying it and using the outputs to craft adversarial examples without knowledge of the internal workings.

B. These attack examples cause a model to predict the correct class with slightly less accuracy even though they look like the original image.

Adversarial examples typically cause the model to predict the incorrect class rather than just reducing accuracy. These examples are designed to be visually indistinguishable from the original image but lead to incorrect classifications.

C. These attacks can't be prevented by retraining the model with these examples augmented to the training data.

This statement is incorrect because retraining the model with adversarial examples included in the training data can help the model learn to resist such attacks, a technique known as adversarial training.

D. These examples are model specific and are not likely to cause another model trained on the same task to fail.

Adversarial examples are often model-specific, meaning that they exploit the specific weaknesses of a particular model. While some adversarial examples might transfer between models, many are tailored to the specific model they were generated for and may not affect other models trained on the same task.

Therefore, the correct answer is D because adversarial examples are typically model-specific and may not cause another model trained on the same task to fail​​.

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.

References:

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.