Free ISTQB CT-AI Practice Exams - Page 2 of 5

Question #11

Which ONE of the following statements is a CORRECT adversarial example in the context of machine learning systems that are working on image classifiers. SELECT ONE OPTION

A . Black box attacks based on adversarial examples create an exact duplicate model of the original.
B . These attack examples cause a model to predict the correct class with slightly less accuracy even though they look like the original image.
C . These attacks can’t be prevented by retraining the model with these examples augmented to the training data.
D . These examples are model specific and are not likely to cause another model trained on same task to fail.

Reveal Solution Hide Solution

Question #12

A ML engineer is trying to determine the correctness of the new open-source implementation *X", of a supervised regression algorithm implementation. R-Square is one of the functional performance metrics used to determine the quality of the model.

Which ONE of the following would be an APPROPRIATE strategy to achieve this goal? SELECT ONE OPTION

A . Add 10% of the rows randomly and create another model and compare the R-Square scores of both the model.
B . Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.
C . Compare the R-Square score of the model obtained using two different implementations that utilize two different programming languages while using the same algorithm and the same training and testing data.
D . Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.

Reveal Solution Hide Solution

Question #13

Which ONE of the following hardware is MOST suitable for implementing Al when using ML? SELECT ONE OPTION

A . 64-bit CPUs.
B . Hardware supporting fast matrix multiplication.
C . High powered CPUs.
D . Hardware supporting high precision floating point operations.

Reveal Solution Hide Solution

Question #14

Which ONE of the following statements correctly describes the importance of flexibility for Al systems? SELECT ONE OPTION

A . Al systems are inherently flexible.
B . Al systems require changing of operational environments; therefore, flexibility is required.
C . Flexible Al systems allow for easier modification of the system as a whole.
D . Self-learning systems are expected to deal with new situations without explicitly having to program for it.

Reveal Solution Hide Solution

Question #15

An image classification system is being trained for classifying faces of humans. The distribution of the data is 70% ethnicity A and 30% for ethnicities B, C and D.

Based ONLY on the above information, which of the following options BEST describes the situation of this image classification system? SELECT ONE OPTION

A . This is an example of expert system bias.
B . This is an example of sample bias.
C . This is an example of hyperparameter bias.
D . This is an example of algorithmic bias.

Reveal Solution Hide Solution

Question #16

Which ONE of the following activities is MOST relevant when addressing the scenario where you have more than the required amount of data available for the training? SELECT ONE OPTION

A . Feature selection
B . Data sampling
C . Data labeling
D . Data augmentation

Reveal Solution Hide Solution

Question #17

Which ONE of the following options does NOT describe a challenge for acquiring test data in ML systems? SELECT ONE OPTION

A . Compliance needs require proper care to be taken of input personal data.
B . Nature of data constantly changes with lime.
C . Data for the use case is being generated at a fast pace.
D . Test data being sourced from public sources.

Reveal Solution Hide Solution

Question #18

"Splendid Healthcare" has started developing a cancer detection system based on ML. The type of cancer they plan on detecting has 2% prevalence rate in the population of a particular geography. It is required that the model performs well for both normal and cancer patients.

Which ONE of the following combinations requires MAXIMIZATION? SELECT ONE OPTION

A . Maximize precision and accuracy
B . Maximize accuracy and recall
C . Maximize recall and precision
D . Maximize specificity number of classes

Reveal Solution Hide Solution

Correct Answer: C
C

Explanation:

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.

Reference: This explanation aligns with the principles outlined in the ISTQB CT-AI Syllabus, particularly sections on performance metrics for ML models and handling imbalanced datasets (Chapter 5: ML Functional Performance Metrics).

Question #19

Which ONE of the following combinations of Training, Validation, Testing data is used during the process of learning/creating the model? SELECT ONE OPTION

A . Training data – validation data – test data
B . Training data – validation data
C . Training data • test data
D . Validation data – test data

Reveal Solution Hide Solution

Question #20

Written requirements are given in text documents, which ONE of the following options is the BEST way to generate test cases from these requirements? SELECT ONE OPTION

A . Natural language processing on textual requirements
B . Analyzing source code for generating test cases
C . Machine learning on logs of execution
D . GUI analysis by computer vision

Reveal Solution Hide Solution