[2026] Pass ISTQB CT-AI Premium Files Test Engine pdf - Free Dumps Collection [Q27-Q46]

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[2026] Pass ISTQB CT-AI Premium Files Test Engine pdf – Free Dumps Collection

New 2026 Realistic CT-AI Dumps Test Engine Exam Questions in here

ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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 4	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 5	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 6	systems from those required for conventional systems.
Topic 7	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 8	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 9	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based

Q27. “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.

Q28. Which statement about testing levels for AI-based systems is correct?
Choose ONE option (1 out of 4)

Input data testing checks whether the inputs from the data pipeline are received by the model correctly and exchanged with all system components

Acceptance testing checks non-functional requirements such as explainability

ML model testing ensures that the relevant ML functional performance criteria are met

If AI is offered as a service, system testing includes API tests of the service

Q29. 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.

Q30. Which of the following is a problem with AI-generated test cases that are generated from the requirements?

They are slow and will usually not be able to execute in the time allowed.

They are defect prone because they are unable to detect nuances in the requirements.

They make debugging more complicated because the number of steps is usually high in order to induce the target failure.

They are usually missing the expected results, so verification is difficult or must resort to only detecting significant failures.

Q31. A local business has a mail pickup/delivery robot for their office. The robot currently uses a track to move between pickup/drop-off locations. When it arrives at a destination, the robot stops to allow a human to remove or deposit mail. The office has decided to upgrade the robot to include AI capabilities that allow the robot to perform its duties without a track, without running into obstacles, and without human intervention.
The test team is creating a list of new and previously established test objectives and acceptance criteria to be used in the testing of the robot upgrade. Which of the following test objectives will test an AI quality characteristic for this system?

The robot must evolve to optimize its routing

The robot must recharge for no more than six hours a day

The robot must record the time of each delivery which is compiled into a report

The robot must complete 99.99% of its deliveries each day

Q32. Which of the following problems would best be solved using the supervised learning category of regression?

Determining the optimal age for a chicken’s egg laying production using input data of the chicken’s age and average daily egg production for one million chickens.

Recognizing a knife in carry on luggage at a security checkpoint in an airport scanner.

Determining if an animal is a pig or a cow based on image recognition.

Predicting shopper purchasing behavior based on the category of shopper and the positioning of promotional displays within a store.

Q33. Which statement regarding testing transparency, explainability, or interpretability is MOST correct?
Choose ONE option (1 out of 4)

Tests for explainability and transparency are comparable to exploratory testing and can be performed with little information about development

Since different users have different backgrounds, interpretability testing depends on the comprehensibility of the ML algorithm

Dynamic testing is one way to quantify explainability; however, each method is specific to a particular model type

LIME can precisely state the decisive reason for a change in the output

Q34. Which AI-specific test objective and acceptance criterion should be selected MOST LIKELY for testing GPT_Legal?
Choose ONE option (1 out of 4)

Test objective: Evidence of functional safety Acceptance criterion: The system recognizes failures in the transmission of information and data with the DPMA system and the evaluation system by means of self-tests.

Test objective: Evidence of evolution Acceptance criterion: The quality of the research results does not deteriorate with further training.

Test objective: Evidence of compatibility Acceptance criterion: The system can exchange information with the DPMA system and the evaluation system.

Test objective: Evidence that the data is free from inappropriate bias Acceptance criterion: The DPMA’ s analysis data is statistically compared to data from other sources.

Q35. An engine manufacturing facility wants to apply machine learning to detect faulty bolts. Which of the following would result in bias in the model?

Selecting training data purposely excluding specific faulty conditions

Selecting training data by purposely including all known faulty conditions

Selecting testing data from a different dataset than the training dataset

Selecting testing data from a boat manufacturer’s bolt longevity data

Q36. ln the near future, technology will have evolved, and Al will be able to learn multiple tasks by itself without needing to be retrained, allowing it to operate even in new environments. The cognitive abilities of Al are similar to a child of 1-2 years.
In the above quote, which ONE of the following options is the correct name of this type of Al?

Technological singularity

Narrow Al

Super Al

General Al

Q37. 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

Add 10% of the rows randomly and create another model and compare the R-Square scores of both the model.

Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.

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.

Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.

Q38. 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.

Q39. 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

Q40. You have access to the training data that was used to train an AI-based system. You can review this information and use it as a guideline when creating your tests. What type of characteristic is this?

Autonomy

Explorability

Transparency

Accessibility

Q41. 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

Q42. A company is using a spam filter to attempt to identify which emails should be marked as spam. Detection rules are created by the filter that causes a message to be classified as spam. An attacker wishes to have all messages internal to the company be classified as spam. So, the attacker sends messages with obvious red flags in the body of the email and modifies the “from” portion of the email to make it appear that the emails have been sent by company members. The testers plan to use exploratory data analysis (EDA) to detect the attack and use this information to prevent future adversarial attacks.
How could EDA be used to detect this attack?

EDA can help detect the outlier emails from the real emails

EDA cannot be used to detect the attack

EDA can restrict how many inputs can be provided by unique users

EDA can detect and remove the false emails

Q43. Which of the following is an example of overfitting?

The model is not able to generalize to accommodate new types of data.

The model is too simplistic for the data.

The model is missing relationships between the inputs and outputs.

The model discards data it considers to be noise or outliers.

Q44. 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.

Q45. Which supervised-learning classification/regression statement is correct?
Choose ONE option (1 out of 4)

Recognizing a dog from many different images is a regression problem

Deciding whether an object is a bicycle or a motorcycle is a classification problem

Predicting that diesel prices will increase by ~10% is a classification problem

In classification, objects are always assigned to exactly two classes

Q46. 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

Updated Official licence for CT-AI Certified by CT-AI Dumps PDF: https://www.trainingdump.com/ISTQB/CT-AI-practice-exam-dumps.html

[2026] Pass ISTQB CT-AI Premium Files Test Engine pdf – Free Dumps Collection [Q27-Q46]