[Jun 05, 2026] Get Unlimited Access to CT-AI Certification Exam Cert Guide
Reliable Study Materials for CT-AI Exam Success For Sure
ISTQB CT-AI Exam Syllabus Topics:
| Topic | Details |
|---|---|
| Topic 1 |
|
| Topic 2 |
|
| Topic 3 |
|
| Topic 4 |
|
| Topic 5 |
|
| Topic 6 |
|
| Topic 7 |
|
| Topic 8 |
|
| Topic 9 |
|
NEW QUESTION # 24
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?
- A. State transition testing
- B. Equivalence partitioning
- C. Static analysis
- D. Exploratory testing
Answer: D
NEW QUESTION # 25
Which ONE of the following statements is a CORRECT adversarial example in the context of machine learning systems that are working on image classifiers.
SELECT ONE OPTION
- A. These examples are model specific and are not likely to cause another model trained on same task to fail.
- B. These attack examples cause a model to predict the correct class with slightly less accuracy even though they look like the original image.
- C. These attacks can't be prevented by retraining the model with these examples augmented to the training data.
- D. Black box attacks based on adversarial examples create an exact duplicate model of the original.
Answer: A
Explanation:
A . Black box attacks based on adversarial examples create an exact duplicate model of the original.
Black box attacks do not create an exact duplicate model. Instead, they exploit the model by querying it and using the outputs to craft adversarial examples without knowledge of the internal workings.
B . These attack examples cause a model to predict the correct class with slightly less accuracy even though they look like the original image.
Adversarial examples typically cause the model to predict the incorrect class rather than just reducing accuracy. These examples are designed to be visually indistinguishable from the original image but lead to incorrect classifications.
C . These attacks can't be prevented by retraining the model with these examples augmented to the training data.
This statement is incorrect because retraining the model with adversarial examples included in the training data can help the model learn to resist such attacks, a technique known as adversarial training.
D . These examples are model specific and are not likely to cause another model trained on the same task to fail.
Adversarial examples are often model-specific, meaning that they exploit the specific weaknesses of a particular model. While some adversarial examples might transfer between models, many are tailored to the specific model they were generated for and may not affect other models trained on the same task.
Therefore, the correct answer is D because adversarial examples are typically model-specific and may not cause another model trained on the same task to fail.
NEW QUESTION # 26
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?
- A. Geographical/Locality
- B. Complacency/Disregard
- C. Automation/Complacency
- D. Ignorance/Cognitive
Answer: C
Explanation:
The syllabus describes automation bias as:
"A type of bias caused by a person favoring the recommendations of an automated decision-making system over other sources." This is also known as complacency bias, where testers accept automated system outputs without questioning them.
(Reference: ISTQB CT-AI Syllabus v1.0, Glossary, Page 89 of 99)
NEW QUESTION # 27
Which ONE of the following characteristics is the least likely to cause safety related issues for an Al system?
SELECT ONE OPTION
- A. Self-learning
- B. Robustness
- C. Non-determinism
- D. High complexity
Answer: B
Explanation:
The question asks which characteristic is least likely to cause safety-related issues for an AI system. Let's evaluate each option:
Non-determinism (A): Non-deterministic systems can produce different outcomes even with the same inputs, which can lead to unpredictable behavior and potential safety issues.
Robustness (B): Robustness refers to the ability of the system to handle errors, anomalies, and unexpected inputs gracefully. A robust system is less likely to cause safety issues because it can maintain functionality under varied conditions.
High complexity (C): High complexity in AI systems can lead to difficulties in understanding, predicting, and managing the system's behavior, which can cause safety-related issues.
Self-learning (D): Self-learning systems adapt based on new data, which can lead to unexpected changes in behavior. If not properly monitored and controlled, this can result in safety issues.
Reference:
ISTQB CT-AI Syllabus Section 2.8 on Safety and AI discusses various factors affecting the safety of AI systems, emphasizing the importance of robustness in maintaining safe operation.
NEW QUESTION # 28
Which ONE of the following types of coverage SHOULD be used if test cases need to cause each neuron to achieve both positive and negative activation values?
SELECT ONE OPTION
- A. Neuron coverage
- B. Threshold coverage
- C. Value coverage
- D. Sign change coverage
Answer: D
Explanation:
* Coverage for Neuron Activation Values: Sign change coverage is used to ensure that test cases cause each neuron to achieve both positive and negative activation values. This type of coverage ensures that the neurons are thoroughly tested under different activation states.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 6.2 Coverage Measures for Neural Networks, which details different types of coverage measures, including sign change coverage.
NEW QUESTION # 29
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.
What is the precision of this predictive model?
- A. 94.2%
- B. 98.9%
- C. 94.5%
- D. 90.0%
Answer: C
Explanation:
The syllabus defines precision as:
"Precision = TP / (TP + FP) * 100%. Precision measures the proportion of positives that were correctly predicted." Using the confusion matrix:
* TP = 90
* FP = 10Thus: Precision = (90 / (90 + 10)) * 100% = 90 / 100 * 100% = 90%However, the confusion matrix totals suggest that the calculation should be done in the form:Precision = 90 / (90 + 10) * 100%
= 90%Since the given answers do not include exactly 90%, the closest approximation and the correct answer, as described in the syllabus, would be 90%.(Reference: ISTQB CT-AI Syllabus v1.0, Section
5.1, page 40 of 99)
NEW QUESTION # 30
Which of the following is a problem with AI-generated test cases that are generated from the requirements?
- A. They are usually missing the expected results, so verification is difficult or must resort to only detecting significant failures
- B. They are defect-prone because they are unable to detect nuances in the requirements
- C. They are slow and will usually not be able to execute in the time allowed
- D. They make debugging more complicated because the number of steps is usually high in order to induce the target failure
Answer: A
Explanation:
The syllabus mentions a drawback of AI-generated test cases:
"AI-based test generation tools can generate test cases... However, unless a test model that defines required behaviors is used as the basis of the tests, this form of test generation generally suffers from a test oracle problem because the AI-based tool does not know what the expected results should be." (Reference: ISTQB CT-AI Syllabus v1.0, Section 11.3, page 78 of 99)
NEW QUESTION # 31
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
- A. ML model metrics to evaluate the functional performance
- B. Different weather conditions
- C. Different Road Types
- D. Different features like ADAS, Lane Change Assistance etc.
Answer: A
Explanation:
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 isleast likelyto 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, theleast likelyreason for the incredible growth in the number of parameters isC. ML model metrics to evaluate the functional performance.
References:
* 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.
NEW QUESTION # 32
A system was developed for screening the X-rays of patients for potential malignancy detection (skin cancer). A workflow system has been developed to screen multiple cancers by using several individually trained ML models chained together in the workflow.
Testing the pipeline could involve multiple kind of tests (I - III):
I . Pairwise testing of combinations
II . Testing each individual model for accuracy
III . A/B testing of different sequences of models
Which ONE of the following options contains the kinds of tests that would be MOST APPROPRIATE to include in the strategy for optimal detection?
SELECT ONE OPTION
- A. I and II
- B. I and III
- C. Only III
- D. Only II
Answer: A
Explanation:
The question asks which combination of tests would be most appropriate to include in the strategy for optimal detection in a workflow system using multiple ML models.
Pairwise testing of combinations (I): This method is useful for testing interactions between different components in the workflow to ensure they work well together, identifying potential issues in the integration.
Testing each individual model for accuracy (II): Ensuring that each model in the workflow performs accurately on its own is crucial before integrating them into a combined workflow.
A/B testing of different sequences of models (III): This involves comparing different sequences to determine which configuration yields the best results. While useful, it might not be as fundamental as pairwise and individual accuracy testing in the initial stages.
Reference:
ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing and Section 9.3 on Testing ML Models emphasize the importance of testing interactions and individual model accuracy in complex ML workflows.
NEW QUESTION # 33
Which ONE of the following statements correctly describes the importance of flexibility for Al systems?
SELECT ONE OPTION
- A. Self-learning systems are expected to deal with new situations without explicitly having to program for it.
- B. Flexible Al systems allow for easier modification of the system as a whole.
- C. Al systems are inherently flexible.
- D. Al systems require changing of operational environments; therefore, flexibility is required.
Answer: B
Explanation:
Flexibility in AI systems is crucial for various reasons, particularly because it allows for easier modification and adaptation of the system as a whole.
* AI systems are inherently flexible (A): This statement is not correct. While some AI systems may be designed to be flexible, they are not inherently flexible by nature. Flexibility depends on the system's design and implementation.
* AI systems require changing operational environments; therefore, flexibility is required (B):
While it's true that AI systems may need to operate in changing environments, this statement does not directly address the importance of flexibility for the modification of the system.
* Flexible AI systems allow for easier modification of the system as a whole (C): This statement correctly describes the importance of flexibility. Being able to modify AI systems easily is critical for their maintenance, adaptation to new requirements, and improvement.
* Self-learning systems are expected to deal with new situations without explicitly having to program for it (D): This statement relates to the adaptability of self-learning systems rather than their overall flexibility for modification.
Hence, the correct answer isC. Flexible AI systems allow for easier modification of the system as a whole.
:
ISTQB CT-AI Syllabus Section 2.1 on Flexibility and Adaptability discusses the importance of flexibility in AI systems and how it enables easier modification and adaptability to new situations.
Sample Exam Questions document, Question #30 highlights the importance of flexibility in AI systems.
NEW QUESTION # 34
A team of software testers is attempting to create an AI algorithm to assist in software testing. This particular team has gone through over 40 iterations of testing and cannot afford to spend as much time as it takes to run the full regression test suite. They are hoping to have the algorithm reduce the amount of testing required, thus reducing the time needed for each testing cycle.
How can an AI-based tool be expected to assist in this reduction?
- A. By performing Bayesian analysis to estimate the types of human interactions that are expected to be seen in the system and then selecting those test cases
- B. By using a clustering method to quantify the relationships between test cases and then assigning each test case to a category
- C. By using A/B testing to compare the last update with the newest change and compare metrics between the two
- D. By performing optimization of the data from past iterations to see where the most common defects occurred and select the corresponding test cases
Answer: D
Explanation:
The syllabus mentions that AI can help optimize regression test suites:
"An AI-based tool can perform optimization of the regression test suite by analyzing... the information from previous test results, associated defects, and the latest changes that have been made, such as features which are broken more frequently and which tests exercise code impacted by recent changes." (Reference: ISTQB CT-AI Syllabus v1.0, Section 11.4, page 79 of 99)
NEW QUESTION # 35
Which challenge to testing self-learning systems puts you at risk of a data attack?
Choose ONE option (1 out of 4)
- A. Insufficient testing time
- B. Inadequate specification of the operating environment
- C. Unexpected changes
- D. Complex test environment
Answer: C
Explanation:
The ISTQB CT-AI syllabus describes thatself-learning systems continuously adjust their behaviorduring operation as new data arrives. Section4.1 - Challenges of Testing AI-Based Systemshighlights that such systems are vulnerable todata attacks, particularly through adversarial inputs, poisoning, or malicious drift.
The risk arises because unexpected changes in the input distribution may alter the learned model in harmful ways. OptionD - Unexpected changescorresponds directly to this syllabus-defined risk.
Option A refers to system specification issues but does not relate to data attacks. Option B discusses environment complexity, which makes testing difficult but is not tied to adversarial threats. Option C (insufficient testing time) affects quality but does not specifically increase vulnerability to malicious data manipulation.
Unexpected changes-including data drift, poisoned samples, or maliciously constructed training data-pose the greatest risk. When a self-learning system adapts to altered data patterns, it may unknowingly learn incorrect associations, causing model degradation or manipulation. Therefore,Option Dcorrectly identifies the challenge that increases exposure to data attacks.
NEW QUESTION # 36
Which ONE of the following options is the MOST APPROPRIATE stage of the ML workflow to set model and algorithm hyperparameters?
SELECT ONE OPTION
- A. Tuning the model
- B. Evaluating the model
- C. Deploying the model
- D. Data testing
Answer: A
Explanation:
Setting model and algorithm hyperparameters is an essential step in the machine learning workflow, primarily occurring during the tuning phase.
Evaluating the model (A): This stage involves assessing the model's performance using metrics and does not typically include the setting of hyperparameters.
Deploying the model (B): Deployment is the stage where the model is put into production and used in real-world applications. Hyperparameters should already be set before this stage.
Tuning the model (C): This is the correct stage where hyperparameters are set. Tuning involves adjusting the hyperparameters to optimize the model's performance.
Data testing (D): Data testing involves ensuring the quality and integrity of the data used for training and testing the model. It does not include setting hyperparameters.
Hence, the most appropriate stage of the ML workflow to set model and algorithm hyperparameters is C. Tuning the model.
Reference:
ISTQB CT-AI Syllabus Section 3.2 on the ML Workflow outlines the different stages of the ML process, including the tuning phase where hyperparameters are set.
Sample Exam Questions document, Question #31 specifically addresses the stage in the ML workflow where hyperparameters are configured.
NEW QUESTION # 37
An engine manufacturing facility wants to apply machine learning to detect faulty bolts. Which of the following would result in bias in the model?
- A. Selecting testing data from a different dataset than the training dataset
- B. Selecting training data by purposely including all known faulty conditions
- C. Selecting training data purposely excluding specific faulty conditions
- D. Selecting testing data from a boat manufacturer's bolt longevity data
Answer: C
Explanation:
The syllabus defines bias as:
"Bias is the systematic difference in treatment of certain objects, people or groups in comparison to others." It also discusses:
"Sample bias can occur if the data used for training the model does not represent the operational environment, or if some relevant faulty conditions are excluded deliberately." (Reference: ISTQB CT-AI Syllabus v1.0, Section 7.6 and 8.3)
NEW QUESTION # 38
Which of the following aspects is a challenge when handling test data for an AI-based system?
- A. Data frameworks or machine learning frameworks
- B. Video frame speed or aspect ratio
- C. Personal data or confidential data
- D. Output data or intermediate data
Answer: C
Explanation:
The syllabus explicitly mentions challenges of handling personal data and ensuring privacy when testing AI- based systems:
"The management of personal data and sensitive data is often a concern during testing, as testing typically requires realistic data and it is difficult to fully anonymize data." (Reference: ISTQB CT-AI Syllabus v1.0, Section 7.3, page 52 of 99)
NEW QUESTION # 39
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
- A. ML model metrics to evaluate the functional performance
- B. Different weather conditions
- C. Different Road Types
- D. Different features like ADAS, Lane Change Assistance etc.
Answer: A
Explanation:
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 isleast likelyto 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, theleast likelyreason for the incredible growth in the number of parameters isC. ML model metrics to evaluate the functional performance.
:
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.
NEW QUESTION # 40
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
- A. Data augmentation
- B. Feature selection
- C. Data labeling
- D. Data sampling
Answer: D
Explanation:
A . Feature selection
Feature selection is the process of selecting the most relevant features from the data. While important, it is not directly about handling excess data.
B . Data sampling
Data sampling involves selecting a representative subset of the data for training. When there is more data than needed, sampling can be used to create a manageable dataset that maintains the statistical properties of the full dataset.
C . Data labeling
Data labeling involves annotating data for supervised learning. It is necessary for training models but does not address the issue of having excess data.
D . Data augmentation
Data augmentation is used to increase the size of the training dataset by creating modified versions of existing data. It is useful when there is insufficient data, not when there is excess data.
Therefore, the correct answer is B because data sampling is the most relevant activity when dealing with an excess amount of data for training.
NEW QUESTION # 41
Which statement about automation bias is correct?
Choose ONE option (1 out of 4)
- A. Automation bias is tested with representative users, but human input quality is irrelevant
- B. Automation bias affects the testing of AI-based systems that support users in their actions or decisions
- C. When testing AI-based systems, automation bias does not play a role in supporting test activities such as boundary value analysis
- D. Automation bias particularly affects testing of autonomous systems
Answer: B
Explanation:
Automation bias is defined in Section4.4 - Human Factors in AI Testingof the ISTQB CT-AI syllabus. It refers to the human tendency to overly trust, rely on, or defer to automated system outputs. The syllabus explains that this bias arises especially indecision-support systems, where humans may accept AI judgments without adequate verification. This aligns directly with Option B.
Option A is incorrect: automation biasdoesinfluence testing, especially when testers rely excessively on AI outputs. The syllabus cautions about testers adopting the same cognitive biases as end users. Option C is incorrect because autonomous systems are not the primary context; rather,systems supporting human decisionsare most impacted. Option D is incorrect because the quality of human inputmatters significantly, and poorly designed user studies can mask or distort automation bias.
Thus,Option Bis the syllabus-accurate description of automation bias.
NEW QUESTION # 42
......
New ISTQB CT-AI Dumps & Questions: https://www.pass4leader.com/ISTQB/CT-AI-exam.html
100% Latest Most updated CT-AI Questions and Answers: https://drive.google.com/open?id=1dWs_4-h8cPVRS2XIixEBwN08gKJs9ZV3