DumpsMaterials CT-AI Exam Questions Real CT-AI Practice Dumps [Q54-Q72]

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DumpsMaterials CT-AI Exam Questions | Real CT-AI Practice Dumps

Verified CT-AI Exam Dumps Q&As – Provide CT-AI with Correct Answers

ISTQB CT-AI Exam Overview:

Certification Vendor:	ISTQB
Exam Name:	ISTQB Certified Tester – AI Testing
Exam Number:	CT-AI
Exam Price:	EUR 250
Passing Score:	65%
Related Certifications:	ISTQB CTFL ISTQB CTAL-TTA
Real Exam Qty:	40
Exam Format:	Multiple Choice
Exam Duration:	60 minutes
Certificate Validity Period:	Lifetime (no expiration)
Available Languages:	English
Sample Questions:	ISTQB CT-AI Sample Questions
Exam Way:	Online proctored or in-person at authorized testing centers
Pre Condition:	ISTQB CTFL (Certified Tester Foundation Level) certification is recommended but not mandatory
Official Syllabus URL:	https://www.istqb.org/certifications/artificial-intelligence-testing-certification

NEW QUESTION 54
Which ONE of the following statements BEST describes how system complexity can cause challenges when testing an AI based system?

Obtaining test data is harder

It is necessary to measure how changes in the test inputs cause changes in the test outputs

Unexpected changes in system behaviour can occur

Sometimes the system can only be tested as a black-box

NEW QUESTION 55
Which option describes a reasonable application of AIB testing for a self-learning system after it has changed its behavior due to user input?
Choose ONE option (1 out of 4)

Generating test cases for the system before and after the change, since neither has a test oracle

Comparing outputs before and after the change using different inputs

Comparing outputs before and after the change using identical inputs

Comparing outputs of a non-self-learning system with those of the changed self-learning system

NEW QUESTION 56
When verifying that an autonomous AI-based system is acting appropriately, which of the following are MOST important to include?

Test cases to verify that the system automatically confirms the correct classification of training data

Test cases to detect the system appropriately automating its data input

Test cases to detect the system prompting for unnecessary human intervention

Test cases to verify that the system automatically suppresses invalid output data

NEW QUESTION 57
Which ONE of the following statements provides the BEST description of the AI effect?

As AI systems improve, the likelihood of reaching the technological singularity increases.

AI systems get better over time as they are exposed to more data.

The systems used to play chess against human experts in the 1990’s are no longer considered AI.

AI systems increasingly behave like humans.

NEW QUESTION 58
An e-commerce developer built an application for automatic classification of online products in order to allow customers to select products faster. The goal is to provide more relevant products to the user based on prior purchases.
Which of the following factors is necessary for a supervised machine learning algorithm to be successful?

Labeling the data correctly

Minimizing the amount of time spent training the algorithm

Selecting the correct data pipeline for the ML training

Grouping similar products together before feeding them into the algorithm

NEW QUESTION 59
There is a growing backlog of unresolved defects for your project. You know the developers have an ML model that they have created which has learned which developers work on which type of software and the speed with which they resolve issues. How could you use this model to help reduce the backlog and implement more efficient defect resolution?

Use it to prioritize defects automatically based on the time expected for the fix to be made, the speed of the fix, and the likelihood of regressions.

Use it to assign defects to the best developer to resolve the problem and to load balance the defect assignments among the developers.

Use it to determine the root cause of each defect and develop a process improvement plan that can be implemented to remove the most common root causes.

Use it to review the code and determine where more defects are likely to occur so that testing can be targeted to those areas.

NEW QUESTION 60
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 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?

Geographical/Locality

Automation/Complacency

Complacency/Disregard

Ignorance/Cognitive

NEW QUESTION 61
Which of the following characteristics of AI-based systems make it more difficult to ensure they are safe?

Simplicity

Sustainability

Non-determinism

Robustness

NEW QUESTION 62
Which ONE of the following tests is MOST likely to describe a useful test to help detect different kinds of biases in ML pipeline?
SELECT ONE OPTION

Testing the distribution shift in the training data for inappropriate bias.

Test the model during model evaluation for data bias.

Testing the data pipeline for any sources for algorithmic bias.

Check the input test data for potential sample bias.

NEW QUESTION 63
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.

NEW QUESTION 64
The training of an ML model… What type of bias is LEAST important to look for when testing the model?

Inappropriate bias

Automation bias

Algorithmic bias

Sample bias

NEW QUESTION 65
Which ONE of the below is NOT likely to cause a data quality issue affecting a single ML model?

Security issues

Hardware issues

Incorrect weights

Faulty sensors

NEW QUESTION 66
“AllerEgo” is a product that uses sell-learning to predict the behavior of a pilot under combat situation for a variety of terrains and enemy aircraft formations. Post training the model was exposed to the real- world data and the model was found to be behaving poorly. A lot of data quality tests had been performed on the data to bring it into a shape fit for training and testing.
Which ONE of the following options is least likely to describes the possible reason for the fall in the performance, especially when considering the self-learning nature of the Al system?
SELECT ONE OPTION
The difficulty of defining criteria for improvement before the model can be accepted.
The fast pace of change did not allow sufficient time for testing.
The unknown nature and insufficient specification of the operating environment might have caused the poor performance.
There was an algorithmic bias in the Al system.

The difficulty of defining criteria for improvement before the model can be accepted.
Defining criteria for improvement is a challenge in the acceptance of AI models, but it is not directly related to the performance drop in real-world scenarios. It relates more to the evaluation and deployment phase rather than affecting the model’s real-time performance post-deployment.

The fast pace of change did not allow sufficient time for testing.
This can significantly affect the model’s performance. If the system is self-learning, it needs to adapt quickly, and insufficient testing time can lead to incomplete learning and poor performance.

The unknown nature and insufficient specification of the operating environment might have caused the poor performance.
This is highly likely to affect performance. Self-learning AI systems require detailed specifications of the operating environment to adapt and learn effectively. If the environment is insufficiently specified, the model may fail to perform accurately in real-world scenarios.

There was an algorithmic bias in the AI system.Algorithmic bias can significantly impact the performance of AI systems. If the model has biases, it will not perform well across different scenarios and data distributions.

NEW QUESTION 67
Which statement regarding data preparation in the ML workflow is correct?

A key challenge in data transformation is the removal or correction of erroneous data.

Since data preparation is time-consuming, all steps should be automated.

One challenge of data gathering is obtaining high-quality data from multiple sources.

Sampling is so well researched that it is no longer considered risky.

NEW QUESTION 68
Which ONE of the following options represents a technology MOST TYPICALLY used to implement Al?
SELECT ONE OPTION

Search engines

Procedural programming

Case control structures

Genetic algorithms

NEW QUESTION 69
Which of the following neural network coverage criteria can be adapted for its application?
Choose ONE option (1 out of 4)

Sign-Sign coverage

Sign-Change coverage

Threshold coverage

Neuron coverage

NEW QUESTION 70
Which statement regarding data preparation in the ML workflow is correct?
Choose ONE option (1 out of 4)

A key challenge in data transformation is the removal or correction of erroneous data.

Since data preparation is time-consuming, all steps should be automated.

One challenge of data gathering is obtaining high-quality data from multiple sources.

Sampling is so well researched that it is no longer considered risky.

NEW QUESTION 71
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

NEW QUESTION 72
Which ONE of the following describes a situation of back-to-back testing the LEAST?
SELECT ONE OPTION

Comparison of the results of a current neural network model ML model implemented in platform A (for example Pytorch) with a similar neural network model ML model implemented in platform B (for example Tensorflow), for the same data.

Comparison of the results of a home-grown neural network model ML model with results in a neural network model implemented in a standard implementation (for example Pytorch) for same data

Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.

Comparison of the results of the current neural network ML model on the current data set with a slightly modified data set.

Back-to-back testing is a method where the same set of tests are run on multiple implementations of the system to compare their outputs. This type of testing is typically used to ensure consistency and correctness by comparing the outputs of different implementations under identical conditions. Let’s analyze the options given:
A . Comparison of the results of a current neural network model ML model implemented in platform A (for example Pytorch) with a similar neural network model ML model implemented in platform B (for example Tensorflow), for the same data.
This option describes a scenario where two different implementations of the same type of model are being compared using the same dataset. This is a typical back-to-back testing situation.
B . Comparison of the results of a home-grown neural network model ML model with results in a neural network model implemented in a standard implementation (for example Pytorch) for the same data.
This option involves comparing a custom implementation with a standard implementation, which is also a typical back-to-back testing scenario to validate the custom model against a known benchmark.
C . Comparison of the results of a neural network ML model with a current decision tree ML model for the same data.
This option involves comparing two different types of models (a neural network and a decision tree). This is not a typical scenario for back-to-back testing because the models are inherently different and would not be expected to produce identical results even on the same data.
D . Comparison of the results of the current neural network ML model on the current data set with a slightly modified data set.
This option involves comparing the outputs of the same model on slightly different datasets. This could be seen as a form of robustness testing or sensitivity analysis, but not typical back-to-back testing as it doesn’t involve comparing multiple implementations.
Based on this analysis, option C is the one that describes a situation of back-to-back testing the least because it compares two fundamentally different models, which is not the intent of back-to-back testing.

ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 2	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 3	systems from those required for conventional systems.
Topic 4	Quality Characteristics for AI-Based Systems: This section covers topics covered how to explain the importance of flexibility and adaptability as characteristics of AI-based systems and describes the vitality of managing evolution for AI-based systems. It also covers how to recall the characteristics that make it difficult to use AI-based systems in safety-related applications.
Topic 5	Neural Networks and Testing: This section of the exam covers defining the structure and function of a neural network including a DNN and the different coverage measures for neural networks.
Topic 6	Introduction to AI: This exam section covers topics such as the AI effect and how it influences the definition of AI. It covers how to distinguish between narrow AI, general AI, and super AI; moreover, the topics covered include describing how standards apply to AI-based systems.
Topic 7	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.
Topic 8	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 9	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 10	Testing AI-Based Systems Overview: In this section, focus is given to how system specifications for AI-based systems can create challenges in testing and explain automation bias and how this affects testing.
Topic 11	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.