Pass the iSQI AI Testing CT-AI_(v1.0)_World Questions and answers with ValidTests

A. This statement is incorrect. Current AI is true AI and there is no reason to believe that this fact will change over time.

AI is an evolving field, and the definition of what constitutes AI can change as technology advances.

B. This statement is correct. In general, what is considered AI today may change over time.

The term AI is dynamic and has evolved over the years. What is considered AI today might be viewed as standard computing in the future. Historically, as technologies become mainstream, they often cease to be considered "AI".

C. This statement is incorrect. What is considered AI today will continue to be AI even as technology evolves and changes.

This perspective does not account for the historical evolution of the definition of AI. As new technologies emerge, the boundaries of AI shift.

D. This statement is correct. In general, today the term AI is utilized incorrectly.

While some may argue this, it is not a universal truth. The term AI encompasses a broad range of technologies and applications, and its usage is generally consistent with current technological capabilities.

AI test environments have several unique characteristics that differentiate them from traditional test environments. Let’s evaluate each option:

A. Challenges resulting from low accuracy of the models.

Low accuracy is a common challenge in AI systems, especially during initial development and training phases. Ensuring the model performs accurately in varied and unpredictable scenarios is a critical aspect of AI testing.

B. The challenge of mimicking undefined scenarios generated due to self-learning.

AI systems, particularly those that involve machine learning, can generate undefined or unexpected scenarios due to their self-learning capabilities. Mimicking and testing these scenarios is a unique challenge in AI environments.

C. The challenge of providing explainability to the decisions made by the system.

Explainability, or the ability to understand and articulate how an AI system arrives at its decisions, is a significant and unique challenge in AI testing. This is crucial for trust and transparency in AI systems.

D. Challenges in the creation of scenarios of human handover for autonomous systems.

While important, the creation of scenarios for human handover in autonomous systems is not a characteristic unique to AI test environments. It is more related to the operational and deployment challenges of autonomous systems rather than the intrinsic technology-related characteristics of AI.

Given the above points, optionDis the correct answer because it describes a challenge related to operational deployment rather than a technology-related characteristic unique to AI test environments.

A. Regression

Regression is used to predict a continuous value and is not suitable for grouping people based on similar tastes.

B. Association

Association is used to find relationships between variables in large datasets, often in the form of rules (e.g., market basket analysis). It does not directly group individuals but identifies patterns of co-occurrence.

C. Clustering

Clustering is an unsupervised learning method used to group similar data points based on their features. It is ideal for identifying groups of people with similar tastes without prior knowledge of the group labels. This technique will help the company segment its customer base effectively.

D. Classification

Classification is a supervised learning method used to categorize data points into predefined classes. It requires labeled data for training, which is not the case here as we want to identify groups without predefined labels.

Therefore, the correct answer isCbecause clustering is the most suitable method for grouping people with similar tastes for targeted product marketing​​.

The question asks which combination of tests would be most appropriate to include in the strategy for optimal detection in a workflow system using multiple ML models.

Pairwise testing of combinations (I): This method is useful for testing interactions between different components in the workflow to ensure they work well together, identifying potential issues in the integration.

Testing each individual model for accuracy (II): Ensuring that each model in the workflow performs accurately on its own is crucial before integrating them into a combined workflow.

A/B testing of different sequences of models (III): This involves comparing different sequences to determine which configuration yields the best results. While useful, it might not be as fundamental as pairwise and individual accuracy testing in the initial stages.

References:

ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing and Section 9.3 on Testing ML Models emphasize the importance of testing interactions and individual model accuracy in complex ML workflows.

A. 64-bit CPUs.

While 64-bit CPUs are essential for handling large amounts of memory and performing complex computations, they are not specifically optimized for the types of operations commonly used in machine learning.

B. Hardware supporting fast matrix multiplication.

Matrix multiplication is a fundamental operation in many machine learning algorithms, especially in neural networks and deep learning. Hardware optimized for fast matrix multiplication, such as GPUs (Graphics Processing Units), is most suitable for implementing AI and ML because it can handle the parallel processing required for these operations efficiently.

C. High powered CPUs.

High powered CPUs are beneficial for general-purpose computing tasks and some aspects of ML, but they are not as efficient as specialized hardware like GPUs for matrix multiplication and other ML-specific tasks.

D. Hardware supporting high precision floating point operations.

High precision floating point operations are important for scientific computing and some specific AI tasks, but for many ML applications, fast matrix multiplication is more critical than high precision alone.

Therefore, the correct answer isBbecause hardware supporting fast matrix multiplication, such as GPUs, is most suitable for the parallel processing requirements of machine learning​​.

Prevalence Rate and Model Performance:

The cancer detection system being developed by "Splendid Healthcare" needs to account for the fact that the type of cancer has a 2% prevalence rate in the population. This indicates that the dataset is highly imbalanced with far fewer positive (cancer) cases compared to negative (normal) cases.

Importance of Recall:

Recall, also known as sensitivity or true positive rate, measures the proportion of actual positive cases that are correctly identified by the model. In medical diagnosis, especially cancer detection, recall is critical because missing a positive case (false negative) could have severe consequences for the patient. Therefore, maximizing recall ensures that most, if not all, cancer cases are detected.

Importance of Precision:

Precision measures the proportion of predicted positive cases that are actually positive. High precision reduces the number of false positives, meaning fewer people will be incorrectly diagnosed with cancer. This is also important to avoid unnecessary anxiety and further invasive testing for those who do not have the disease.

Balancing Recall and Precision:

In scenarios where both false negatives and false positives have significant consequences, it is crucial to balance recall and precision. This balance ensures that the model is not only good at detecting positive cases but also accurate in its predictions, reducing both types of errors.

Accuracy and Specificity:

While accuracy (the proportion of total correct predictions) is important, it can be misleading in imbalanced datasets. In this case, high accuracy could simply result from the model predicting the majority class (normal) correctly. Specificity (true negative rate) is also important, but for a cancer detection system, recall and precision take precedence to ensure positive cases are correctly and accurately identified.

Conclusion:

Therefore, for a cancer detection system with a low prevalence rate, maximizing both recall and precision is crucial to ensure effective and accurate detection of cancer cases.

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​​​​.

A. Feature selection

Feature selection is the process of selecting the most relevant features from the data. While important, it is not directly about handling excess data.

B. Data sampling

Data sampling involves selecting a representative subset of the data for training. When there is more data than needed, sampling can be used to create a manageable dataset that maintains the statistical properties of the full dataset.

C. Data labeling

Data labeling involves annotating data for supervised learning. It is necessary for training models but does not address the issue of having excess data.

D. Data augmentation

Data augmentation is used to increase the size of the training dataset by creating modified versions of existing data. It is useful when there is insufficient data, not when there is excess data.

Therefore, the correct answer isBbecause data sampling is the most relevant activity when dealing with an excess amount of data for training​​.

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.

Factors Affecting ML Functional Performance:The data pipeline, quality of the labeling, and biased data are all factors that significantly affect the performance of machine learning models. The number of classes, while relevant for the model structure, is not a direct factor affecting the performance metrics such as accuracy or bias.

Reference:ISTQB_CT-AI_Syllabus_v1.0, Sections on Data Quality and its Effect on the ML Model and ML Functional Performance Metrics​​​​.