Free ISTQB CT-AI Practice Exam with Questions & Answers

Name: How to Pass CT-AI Exams
Brand: Examstrack
SKU: ct-ai
Price: 42 USD
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Questions 1

A motorcycle engine repair shop owner wants to detect a leaking exhaust valve and fix it before it fails and causes catastrophic damage to the engine. The shop developed and trained a predictive model with historical data files from known healthy engines and ones which experienced a catastrophic failure due to exhaust valve failure. The shop evaluated 200 engines using this model and then disassembled the engines to assess the true state of the valves, recording the results in the confusion matrix below.

CT-AI Question 1

What is the precision of this predictive model?

Options:

90.0%

94.5%

98.9%

94.2%

ISTQB CT-AI Premium Access

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27-Jun-2025

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Andrew Wilson

18-Jun-2025

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Questions 2

The stakeholders of a machine learning model have confirmed that they understand the objective and purpose of the model, and ensured that the proposed model aligns with their business priorities. They have also selected a framework and a machine learning model that they will be using. What should be the next step to progress along the machine learning workflow?

Options:

Prepare and pre-process the data that will be used to train and test the model

Tune the machine learning algorithm based on objectives and business priorities

Agree on defined acceptance criteria for the machine learning model

Evaluate the selection of the framework and the model

Questions 3

Consider a natural language processing (NLP) algorithm that attempts to predict the next word that you would like to type in a text message. An update to the algorithm has been created that should increase the accuracy of the predictions based on user typing patterns. The old algorithm was rated for accuracy by the users. Then, after the new update was released, the users rated the updated algorithm. A statistical test was used to compare the two versions of the algorithm to see whether or not the update should remain in place.

This is an example of what type of testing?

Options:

Metamorphic testing

A/B testing

Exploratory testing

Pairwise testing

Questions 4

A tourist calls an airline to book a ticket and is connected with an automated system which is able to recognize speech, understand requests related to purchasing a ticket, and provide relevant travel options. When the tourist asks about the expected weather at the destination or potential impacts on operations because of the tight labor market, the only response from the automated system is, "I don’t understand your question."

This AI system should be categorized as?

Options:

General AI

Narrow AI

Super AI

Conventional AI

Questions 5

In a conference on artificial intelligence (Al), a speaker made the statement, "The current implementation of Al using models which do NOT change by themselves is NOT true Al*. Based on your understanding of Al, is this above statement CORRECT or INCORRECT and why?

SELECT ONE OPTION

Options:

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

This statement is correct. In general, what is considered Al today may change over time.

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

This statement is correct. In general, today the term Al is utilized incorrectly.

Questions 6

A bank wants to use an algorithm to determine which applicants should be given a loan. The bank hires a data scientist to construct a logistic regression model to predict whether the applicant will repay the loan or not. The bank has enough data on past customers to randomly split the data into a training dataset and a test/validation dataset. A logistic regression model is constructed on the training dataset using the following independent variables:

Gender

Marital status

Number of dependents

Education

Income

Loan amount

Loan term

Credit score

The model reveals that those with higher credit scores and larger total incomes are more likely to repay their loans. The data scientist has suggested that there might be bias present in the model based on previous models created for other banks.

Given this information, what is the best test approach to check for potential bias in the model?

Options:

Experience-based testing should be used to confirm that the training data set is operationally relevant. This can include applying exploratory data analysis (EDA) to check for bias within the training data set.

Back-to-back testing should be used to compare the model created using the training data set to another model created using the test data set. If the two models significantly differ, it will indicate there is bias in the original model.

Acceptance testing should be used to make sure the algorithm is suitable for the customer. The team can re-work the acceptance criteria such that the algorithm is sure to correctly predict the remaining applicants that have been set aside for the validation dataset ensuring no bias is present.

A/B testing should be used to verify that the test data set does not detect any bias that might have been introduced by the original training data. If the two models significantly differ, it will indicate there is bias in the original model.

Questions 7

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

SELECT ONE OPTION

Options:

Compliance needs require proper care to be taken of input personal data.

Nature of data constantly changes with lime.

Data for the use case is being generated at a fast pace.

Test data being sourced from public sources.

Questions 8

Which of the following is THE LEAST appropriate tests to be performed for testing a feature related to autonomy?

SELECT ONE OPTION

Options:

Test for human handover to give rest to the system.

Test for human handover when it should actually not be relinquishing control.

Test for human handover requiring mandatory relinquishing control.

Test for human handover after a given time interval.

Questions 9

"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

Options:

Maximize precision and accuracy

Maximize accuracy and recall

Maximize recall and precision

Maximize specificity number of classes

Answer:

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.

[: 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)., , , ]

Questions 10

A word processing company is developing an automatic text correction tool. A machine learning algorithm was used to develop the auto text correction feature. The testers have discovered that when they start typing “Isle of Wight” it fills in “Isle of Eight”. Several UAT testers have accepted this change without noticing. What type of bias is this?

Options:

Geographical/Locality

Automation/Complacency

Complacency/Disregard

Ignorance/Cognitive