A Fully Updated 2025 CT-AI Exam Dumps - PDF Questions and Testing Engine [Q43-Q64]

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A Fully Updated 2025 CT-AI Exam Dumps – PDF Questions and Testing Engine

Easy Success ISTQB CT-AI Exam in First Try

ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 4	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 5	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 6	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 7	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 8	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 9	systems from those required for conventional systems.
Topic 10	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 11	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.

Q43. 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

Q44. 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.

Q45. 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 by 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

Q46. 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.

Q47. “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.

Q48. The activation value output for a neuron in a neural network is obtained by applying computation to the neuron.
Which ONE of the following options BEST describes the inputs used to compute the activation value?
SELECT ONE OPTION

Individual bias at the neuron level, activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.

Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.

Individual bias at the neuron level, and weights assigned to the connections between the neurons.

Individual bias at the neuron level, and activation values of neurons in the previous layer.

In a neural network, the activation value of a neuron is determined by a combination of inputs from the previous layer, the weights of the connections, and the bias at the neuron level. Here’s a detailed breakdown:
* Inputs for Activation Value:
* Activation Values of Neurons in the Previous Layer:These are the outputs from neurons in the preceding layer that serve as inputs to the current neuron.
* Weights Assigned to the Connections:Each connection between neurons has an associated weight, which determines the strength and direction of the input signal.
* Individual Bias at the Neuron Level:Each neuron has a bias value that adjusts the input sum, allowing the activation function to be shifted.
* Calculation:
* The activation value is computed by summing the weighted inputs from the previous layer and adding the bias.
* Formula: z=#(wi#ai)+bz = \sum (w_i \cdot a_i) + bz=#(wi#ai)+b, where wiw_iwi are the weights, aia_iai are the activation values from the previous layer, and bbb is the bias.
* The activation function (e.g., sigmoid, ReLU) is then applied to this sum to get the final activation value.
* Why Option A is Correct:
* Option A correctly identifies all components involved in computing the activation value: the individual bias, the activation values of the previous layer, and the weights of the connections.
* Eliminating Other Options:
* B. Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons: This option misses the bias, which is crucial.
* C. Individual bias at the neuron level, and weights assigned to the connections between the neurons: This option misses the activation values from the previous layer.
* D. Individual bias at the neuron level, and activation values of neurons in the previous layer: This option misses the weights, which are essential.
References:
* ISTQB CT-AI Syllabus, Section 6.1, Neural Networks, discusses the components and functioning of neurons in a neural network.
* “Neural Network Activation Functions” (ISTQB CT-AI Syllabus, Section 6.1.1).

Q49. 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

Q50. 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 make debugging more complicated because the number of steps is usually high in order to induce the target failure

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

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

Q51. 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.

Q52. “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.

Q53. You are using a neural network to train a robot vacuum to navigate without bumping into objects. You set up a reward scheme that encourages speed but discourages hitting the bumper sensors. Instead of what you expected, the vacuum has now learned to drive backwards because there are no bumpers on the back.
This is an example of what type of behavior?

Error-shortcircuiting

Reward-hacking

Transparency

Interpretability

Q54. 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

Q55. Which of the following is an example of an input change where it would be expected that the AI system should be able to adapt?

It has been trained to recognize cats and is given an image of a dog.

It has been trained to recognize human faces at a particular resolution and it is given a human face image captured with a higher resolution.

It has been trained to analyze mathematical models and is given a set of landscape pictures to classify.

It has been trained to analyze customer buying trend data and is given information on supplier cost data.

AI systems, particularly machine learning models, need to exhibit adaptability and flexibility to handle slight variations in input data without requiring retraining. The ISTQB CT-AI syllabus outlines adaptability as a crucial feature of AI systems, especially when the system is exposed to variations in its operational environment.
* Option A:”It has been trained to recognize cats and is given an image of a dog.”
* This scenario introduces an entirely new class (dogs), which is outside the AI system’s expected scope. If the AI was only trained to recognize cats, it would not be expected to recognize dogs correctly without retraining. This does not demonstrate adaptability as expected from an AI system.
* Option B:”It has been trained to recognize human faces at a particular resolution and it is given a human face image captured with a higher resolution.”
* This is an example of an AI system encountering a variation of its training data rather than entirely new data. Most AI-based image processing models can adapt to different resolutions by applying downsampling or other pre-processing techniques. Since the data remains within the domain of human faces, the model should be able to process the higher-resolution image without significant issues.
* Option C:”It has been trained to analyze mathematical models and is given a set of landscape pictures to classify.”
* This represents a complete shift in the data type from structured numerical data to unstructured image data. The AI system is unlikely to adapt effectively, as it has not been trained on image classification tasks.
* Option D:”It has been trained to analyze customer buying trend data and is given information on supplier cost data.”
* This introduces a significant domain shift. Customer buying trends focus on consumer behavior, while supplier cost data relates to pricing structures and logistics. The AI system would likely require retraining to process the new data meaningfully.
* Adaptability Requirements:The syllabus discusses that AI-based systems must be able to adapt to changes in their operational environment and constraints, including minor variations in input quality (such as resolution changes).
* Autonomous Learning & Evolution:AI systems are expected to improve and handle evolving inputs based on prior experience.
* Challenges in Testing Self-Learning Systems:AI systems should be tested to ensure they function correctly when encountering new but related data, such as different resolutions of the same object.
Analysis of the Answer Options:ISTQB CT-AI Syllabus References:Thus,option Bis the best choice as it aligns with the adaptability characteristics expected from AI-based systems.

Q56. 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.

Q57. Which of the following is a technique used in machine learning?

Decision trees

Equivalence partitioning

Boundary value analysis

Decision tables

Q58. 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?

Geographical/Locality

Automation/Complacency

Complacency/Disregard

Ignorance/Cognitive

Q59. 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 can detect and remove the false emails.

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

EDA cannot be used to detect the attack.

Exploratory Data Analysis (EDA) is an essential technique for examining datasets to uncover patterns, trends, and anomalies, including outliers. In this case, the attacker manipulates the spam filter by injecting emails with red flags and masking them as internal company emails. The primary goal of EDA here is to detect these adversarial modifications.
* Detecting Outliers:
* EDA techniques such as statistical analysis, clustering, and visualization can reveal patterns in email metadata (e.g., sender details, email content, frequency).
* Outlier detection methods like Z-score, IQR (Interquartile Range), or machine learning-based anomaly detection can identify emails that significantly deviate from typical internal communications.
* Identifying Distribution Shifts:
* By analyzing the frequency and characteristics of emails flagged as spam, testers can detect if the attack has introduced unusual patterns.
* If a surge of internal emails is suddenly classified as spam, EDA can help verify whether these classifications are consistent with historical data.
* Feature Analysis for Adversarial Patterns:
* EDA enables visualization techniques such as scatter plots or histograms to distinguish normal emails from manipulated ones.
* Examining email metadata (e.g., changes in headers, unusual wording in email bodies) can reveal adversarial tactics.
* Counteracting Adversarial Attacks:
* Once anomalies are identified, the spam filter’s detection rules can be improved by retraining the model on corrected datasets.
* The adversarial examples can be added to the training data to enhance the robustness of the filter against future attacks.
* Exploratory Data Analysis (EDA) is used to detect outliers and adversarial attacks.”EDA is where data are examined for patterns, relationships, trends, and outliers. It involves the interactive, hypothesis-driven exploration of data.”
* EDA can identify poisoned or manipulated data by detecting anomalies and distribution shifts.
“Testing to detect data poisoning is possible using EDA, as poisoned data may show up as outliers.”
* EDA helps validate ML models and detect potential vulnerabilities.”The use of exploratory techniques, primarily driven by data visualization, can help validate the ML algorithm being used, identify changes that result in efficient models, and leverage domain expertise.” References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, as EDA is specifically useful for detecting outliers, which can help identify manipulated spam emails.

Q60. A beer company is trying to understand how much recognition its logo has in the market. It plans to do that by monitoring images on various social media platforms using a pre-trained neural network for logo detection.
This particular model has been trained by looking for words, as well as matching colors on social media images. The company logo has a big word across the middle with a bold blue and magenta border.
Which associated risk is most likely to occur when using this pre-trained model?

There is no risk, as the model has already been trained

Insufficient function: the model was not trained to check for colors or words

Improper data preparation

Inherited bias: the model could have inherited unknown defects

Q61. A mobile app start-up company is implementing an AI-based chat assistant for e-commerce customers. In the process of planning the testing, the team realizes that the specifications are insufficient.
Which testing approach should be used to test this system?

Exploratory testing

Equivalence partitioning

State transition testing

Static analysis

Q62. 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.

Q63. 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

Q64. 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

CT-AI Study Material, Preparation Guide and PDF Download: https://www.trainingdump.com/ISTQB/CT-AI-practice-exam-dumps.html

A Fully Updated 2025 CT-AI Exam Dumps – PDF Questions and Testing Engine [Q43-Q64]