100% PASS RATE ISTQB AI Testing CT-AI Certified Exam DUMP with 40 Questions [Q21-Q43]

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100% PASS RATE ISTQB AI Testing CT-AI Certified Exam DUMP with 40 Questions

Updates For the Latest CT-AI Free Exam Study Guide!

ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	Machine Learning ML: This section includes the classification and regression as part of supervised learning, explaining the factors involved in the selection of ML algorithms, and demonstrating underfitting and overfitting.
Topic 2	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 3	Testing AI-Specific Quality Characteristics: In this section, the topics covered are about the challenges in testing created by the self-learning of AI-based systems.
Topic 4	Methods and Techniques for the Testing of AI-Based Systems: In this section, the focus is on explaining how the testing of ML systems can help prevent adversarial attacks and data poisoning.
Topic 5	ML: Data: This section of the exam covers explaining the activities and challenges related to data preparation. It also covers how to test datasets create an ML model and recognize how poor data quality can cause problems with the resultant ML model.
Topic 6	ML Functional Performance Metrics: In this section, the topics covered include how to calculate the ML functional performance metrics from a given set of confusion matrices.

NO.21 Data used for an object detection ML system was found to have been labelled incorrectly in many cases.
Which ONE of the following options is most likely the reason for this problem?
SELECT ONE OPTION

Security issues

Accuracy issues

Privacy issues

Bias issues

NO.22 Which ONE of the following options does NOT describe an Al technology related characteristic which differentiates Al test environments from other test environments?
SELECT ONE OPTION

Challenges resulting from low accuracy of the models.

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

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

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

NO.23 Which ONE of the following options BEST DESCRIBES clustering?
SELECT ONE OPTION

Clustering is classification of a continuous quantity.

Clustering is supervised learning.

Clustering is done without prior knowledge of output classes.

Clustering requires you to know the classes.

NO.24 Max. Score: 2
Al-enabled medical devices are used nowadays for automating certain parts of the medical diagnostic processes. Since these are life-critical process the relevant authorities are considenng bringing about suitable certifications for these Al enabled medical devices. This certification may involve several facets of Al testing (I – V).
I . Autonomy
II . Maintainability
III . Safety
IV . Transparency
V . Side Effects
Which ONE of the following options contains the three MOST required aspects to be satisfied for the above scenario of certification of Al enabled medical devices?
SELECT ONE OPTION

Aspects II, III and IV

Aspects I, II, and III

Aspects III, IV, and V

Aspects I, IV, and V

NO.25 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

Feature selection

Data sampling

Data labeling

Data augmentation

NO.26 Which ONE of the following tests is LEAST likely to be performed during the ML model testing phase?
SELECT ONE OPTION

Testing the accuracy of the classification model.

Testing the API of the service powered by the ML model.

Testing the speed of the training of the model.

Testing the speed of the prediction by the model.

NO.27 Which ONE of the following combinations of Training, Validation, Testing data is used during the process of learning/creating the model?
SELECT ONE OPTION

Training data – validation data – test data

Training data – validation data

Training data * test data

Validation data – test data

NO.28 Which of the following is THE LEAST appropriate tests to be performed for testing a feature related to autonomy?
SELECT ONE OPTION

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.

NO.29 Which ONE of the following options is the MOST APPROPRIATE stage of the ML workflow to set model and algorithm hyperparameters?
SELECT ONE OPTION

Evaluating the model

Deploying the model

Tuning the model

Data testing

NO.30 Which ONE of the following is the BEST option to optimize the regression test selection and prevent the regression suite from growing large?
SELECT ONE OPTION

Identifying suitable tests by looking at the complexity of the test cases.

Using of a random subset of tests.

Automating test scripts using Al-based test automation tools.

Using an Al-based tool to optimize the regression test suite by analyzing past test results

NO.31 Which ONE of the following statements correctly describes the importance of flexibility for Al systems?
SELECT ONE OPTION

Al systems are inherently flexible.

Al systems require changing of operational environments; therefore, flexibility is required.

Flexible Al systems allow for easier modification of the system as a whole.

Self-learning systems are expected to deal with new situations without explicitly having to program for it.

NO.32 Which ONE of the following options does NOT describe a challenge for acquiring test data in ML systems?
SELECT ONE OPTION

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.

NO.33 Which ONE of the following options is an example that BEST describes a system with Al-based autonomous functions?
SELECT ONE OPTION

A system that utilizes human beings for all important decisions.

A fully automated manufacturing plant that uses no software.

A system that utilizes a tool like Selenium.

A system that is fully able to respond to its environment.

NO.34 Pairwise testing can be used in the context of self-driving cars for controlling an explosion in the number of combinations of parameters.
Which ONE of the following options is LEAST likely to be a reason for this incredible growth of parameters?
SELECT ONE OPTION

Different Road Types

Different weather conditions

ML model metrics to evaluate the functional performance

Different features like ADAS, Lane Change Assistance etc.

Pairwise testing is used to handle the large number of combinations of parameters that can arise in complex systems like self-driving cars. The question asks which of the given options is least likely to be a reason for the explosion in the number of parameters.
Different Road Types (A): Self-driving cars must operate on various road types, such as highways, city streets, rural roads, etc. Each road type can have different characteristics, requiring the car’s system to adapt and handle different scenarios. Thus, this is a significant factor contributing to the growth of parameters.
Different Weather Conditions (B): Weather conditions such as rain, snow, fog, and bright sunlight significantly affect the performance of self-driving cars. The car’s sensors and algorithms must adapt to these varying conditions, which adds to the number of parameters that need to be considered.
ML Model Metrics to Evaluate Functional Performance (C): While evaluating machine learning (ML) model performance is crucial, it does not directly contribute to the explosion of parameter combinations in the same way that road types, weather conditions, and car features do. Metrics are used to measure and assess performance but are not themselves variable conditions that the system must handle.
Different Features like ADAS, Lane Change Assistance, etc. (D): Advanced Driver Assistance Systems (ADAS) and other features add complexity to self-driving cars. Each feature can have multiple settings and operational modes, contributing to the overall number of parameters.
Hence, the least likely reason for the incredible growth in the number of parameters is C. ML model metrics to evaluate the functional performance.
Reference:
ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing discusses the application of this technique to manage the combinations of different variables in AI-based systems, including those used in self-driving cars.
Sample Exam Questions document, Question #29 provides context for the explosion in parameter combinations in self-driving cars and highlights the use of pairwise testing as a method to manage this complexity.

NO.35 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

Natural language processing on textual requirements

Analyzing source code for generating test cases

Machine learning on logs of execution

GUI analysis by computer vision

NO.36 Which ONE of the following characteristics is the least likely to cause safety related issues for an Al system?
SELECT ONE OPTION

Non-determinism

Robustness

High complexity

Self-learning

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100% PASS RATE ISTQB AI Testing CT-AI Certified Exam DUMP with 40 Questions [Q21-Q43]

ISTQB CT-AI Exam Syllabus Topics:

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