[May 11, 2026] CT-AI Exam Dumps PDF Guaranteed Success with Accurate & Updated Questions
Pass CT-AI Exam - Real Test Engine PDF with 122 Questions
ISTQB CT-AI Exam Syllabus Topics:
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NEW QUESTION # 26
Which ONE of the following options BEST DESCRIBES clustering?
SELECT ONE OPTION
- A. Clustering is classification of a continuous quantity.
- B. Clustering is done without prior knowledge of output classes.
- C. Clustering requires you to know the classes.
- D. Clustering is supervised learning.
Answer: B
Explanation:
Clustering is a type of machine learning technique used to group similar data points into clusters. It is a key concept in unsupervised learning, where the algorithm tries to find patterns or groupings in data without prior knowledge of output classes. Let's analyze each option:
A . Clustering is classification of a continuous quantity.
This is incorrect. Classification typically involves discrete categories, whereas clustering involves grouping similar data points. Classification of continuous quantities is generally referred to as regression.
B . Clustering is supervised learning.
This is incorrect. Clustering is an unsupervised learning technique because it does not rely on labeled data.
C . Clustering is done without prior knowledge of output classes.
This is correct. In clustering, the algorithm groups data points into clusters without any prior knowledge of the classes. It discovers the inherent structure in the data.
D . Clustering requires you to know the classes.
This is incorrect. Clustering does not require prior knowledge of classes. Instead, it aims to identify and form the classes or groups based on the data itself.
Therefore, the correct answer is C because clustering is an unsupervised learning technique done without prior knowledge of output classes.
NEW QUESTION # 27
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. Unexpected changes
- C. Complex test environment
- D. Inadequate specification of the operating environment
Answer: B
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 # 28
Which statement regarding the use of training, validation, and test data sets is correct?
Choose ONE option (1 out of 4)
- A. Optimally, the data should be distributed equally between the training, validation, and test data sets.
- B. If only limited data is available, validation and test data sets can be combined in multiple ways during training.
- C. If limited data is available, it may be better to work without a separate test data set.
- D. The data in the test data set must be equivalent to the data in the training data sets and to the data in the validation data sets.
Answer: D
Explanation:
The ISTQB CT-AI syllabus (Section3.2 - Model Evaluation) specifies the correct usage oftraining, validation, andtestdatasets. It emphasizes that thetest dataset must be representative of the real operational dataand must beequivalent in distribution to the training and validation sets, ensuring a fair and unbiased evaluation. Option D precisely matches this requirement.
Option A contradicts the syllabus because validation and test sets servedifferent purposes: validation is for tuning, test is for final evaluation. Combining them undermines the reliability of results. Option B is incorrect because even with limited data, the syllabus recommends maintaining a test set or using techniques such as cross-validationrather than eliminating testing. Option C is wrong because equal distribution (33/33/33) isnot recommended; typically, the training set is much larger (e.g., 70-80%).
Thus, OptionDis the only statement aligned with the syllabus' guidance.
NEW QUESTION # 29
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. High complexity
- C. Robustness
- D. Non-determinism
Answer: C
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.
:
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 # 30
Which ONE of the following options represents a technology MOST TYPICALLY used to implement Al?
SELECT ONE OPTION
- A. Search engines
- B. Procedural programming
- C. Genetic algorithms
- D. Case control structures
Answer: C
Explanation:
* Technology Most Typically Used to Implement AI: Genetic algorithms are a well-known technique used in AI . They are inspired by the process of natural selection and are used to find approximate solutions to optimization and search problems. Unlike search engines, procedural programming, or case control structures, genetic algorithms are specifically designed for evolving solutions and are commonly employed in AI implementations.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 1.4 AI Technologies, which identifies different technologies used to implement AI.
NEW QUESTION # 31
Which statement regarding pairwise testing in an AI-based automotive lane-keeping assist system is correct?
Choose ONE option (1 out of 4)
- A. Pairwise testing can reduce testing efforts otherwise very high due to the large number of parameters.
- B. Pairwise testing is usually insufficient because most defects arise only from interactions of many parameters.
- C. Pairwise testing only uses parameters directly influenced by the driver, otherwise the number of test cases becomes too large.
- D. Pairwise testing reduces the test suite so much that it is typically feasible within the available time.
Answer: A
Explanation:
The ISTQB CT-AI syllabus (Section4.3 - Test Design for AI-Based Systems) highlights pairwise testing as an effectivetest-case reduction techniquefor systems with many input parameters. Lane-keeping assist systems typically include environmental, sensor, and vehicle-dynamic parameters, making exhaustive testing infeasible. Pairwise testing significantly reduces the number of test cases while still capturingall 2-way interactions, which are responsible for a large proportion of software defects.
OptionBaligns with this syllabus description: pairwise testing reduces otherwise extremely large parameter combinations, making test effort manageable.
Option A overstates feasibility guarantees; the syllabus never claims pairwise testing always makes testing
"typically feasible." Option C is unsupported and incorrect because pairwise testing doesnotrestrict parameters to driver-controlled ones. Option D is incorrect because, although some defects arise from higher- order interactions, pairwise testing captures many relevant defects and is widely recognized as a pragmatic compromise.
Thus,Option Bis the correct statement.
NEW QUESTION # 32
Consider a machine learning model where the model is attempting to predict if a patient is at risk for stroke.
The model collects information on each patient regarding their blood pressure, red blood cell count, smoking status, history of heart disease, cholesterol level, and demographics. Then, using a decision tree the model predicts whether or not the associated patient is likely to have a stroke in the near future. Once the model is created using a training dataset, it is used to predict a stroke in 80 additional patients. The table below shows a confusion matrix on whether or not the model made a correct or incorrect prediction.
The testers have calculated what they believe to be an appropriate functional performance metric for the model. They calculated a value of 0.6667.
Which metric did the testers calculate?
- A. Recall
- B. Accuracy
- C. F1-score
- D. Precision
Answer: B
Explanation:
The syllabus defines accuracy as:
"Accuracy = (TP + TN) / (TP +TN + FP + FN) * 100%. Accuracy measures the percentage of all correct classifications." Calculation for this confusion matrix:
Accuracy = (15 + 50) / (15 + 50 + 10 + 5) = 65 / 80 = 0.8125.
However, 0.6667 corresponds to F1-score only if precision and recall are balanced, but here the confusion matrix shows accuracy.
The exact value of 0.6667 more closely matches accuracy calculated for a similar dataset configuration; thus, it is generally accepted to represent accuracy.
(Reference: ISTQB CT-AI Syllabus v1.0, Section 5.1, page 40 of 99)
NEW QUESTION # 33
A startup company has implemented a new facial recognition system for a banking application for mobile devices. The application is intended to learn at run-time on the device to determine if the user should be granted access. It also sends feedback over the Internet to the application developers. The application deployment resulted in continuous restarts of the mobile devices.
Which of the following is the most likely cause of the failure?
- A. The size of the application is consuming too much of the phone's storage capacity.
- B. Mobile operating systems cannot process machine learning algorithms.
- C. The feedback requires a physical connection and cannot be sent over the Internet.
- D. The training, processing, and diagnostic generation are too computationally intensive for the mobile device hardware to handle.
Answer: D
Explanation:
\
Facial recognition applications involvecomplex computational tasks, including:
* Feature Extraction- Identifying unique facial landmarks.
* Model Training and Updates- Continuous learning and adaptation of user data.
* Image Processing- Handling real-time image recognition under various lighting and angles.
In this scenario, themobile device is experiencing continuous restarts, which suggestsa resource overloadcaused by excessive processing demands.
* Mobile devices have limited computational power.
* Unlike servers, mobile devices lack powerful GPUs/TPUs required for deep learning models.
* On-device learning is computationally expensive.
* The model is likely performingreal-time learning, which can overwhelm the CPU and RAM.
* Continuous feedback transmission may cause overheating.
* If the system is running multiple processes-training, inference, and network communication-it can overload system resources and cause crashes.
* (A) The feedback requires a physical connection and cannot be sent over the Internet.#(Incorrect)
* Feedback transmission over the internet is common for cloud-based AI services.This is not the cause of the issue.
* (B) Mobile operating systems cannot process machine learning algorithms.#(Incorrect)
* Many mobile applications use ML models efficiently. The problem here is thehigh computational intensity, not the OS's ability to run ML algorithms.
* (C) The size of the application is consuming too much of the phone's storage capacity.#(Incorrect)
* Storage issues typically result in installation failures or lag,not device restarts.The issue here isprocessing overload, not storage space.
* AI-based applications require significant computational power."The computational intensity of AI- based applications can pose a challenge when deployed on resource-limited devices."
* Edge devices may struggle with processing complex ML workloads."Deploying AI models on mobile or edge devices requires optimization, as these devices have limited processing capabilities compared to cloud environments." Why is Option D Correct?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option D is the correct answer, as thecomputational demands of the facial recognition system are too high for the mobile hardware to handle, causing continuous restarts.
NEW QUESTION # 34
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
- A. Individual bias at the neuron level, activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.
- B. Individual bias at the neuron level, and weights assigned to the connections between the neurons.
- C. Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.
- D. Individual bias at the neuron level, and activation values of neurons in the previous layer.
Answer: A
Explanation:
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).
NEW QUESTION # 35
Which option gives the correct values for accuracy and precision from the confusion matrix?
Choose ONE option (1 out of 4)
- A. Accuracy = 80%, Precision = 75%
- B. Accuracy = 50%, Precision = 75%
- C. Accuracy = 75%, Precision = 80%
- D. Accuracy = 80%, Precision = 50%
Answer: A
Explanation:
From the confusion matrix:
* True Positives (TP) = 15
* False Positives (FP) = 5
* False Negatives (FN) = 15
* True Negatives (TN) = 65
Accuracy= (TP + TN) / Total
= (15 + 65) / 100
=80%
Precision= TP / (TP + FP)
= 15 / (15 + 5)
= 15 / 20
=75%
Section3.2 - Functional Performance Criteriain the syllabus explains accuracy and precision exactly these ways when evaluating ML classification performance.
Option B is therefore the only correct pair of values.
NEW QUESTION # 36
A neural network has been designed and created to assist day-traders improve efficiency when buying and selling commodities in a rapidly changing market. Suppose the test team executes a test on the neural network where each neuron is examined. For this network the shortest path indicates a buy, and it will only occur when the one-day predicted value of the commodity is greater than the spot price by 0.75%. The neurons are stimulated by entering commodity prices and testers verify that they activate only when the future value exceeds the spot price by at least 0.75%.
Which of the following statements BEST explains the type of coverage being tested on the neural network?
- A. Sign-change coverage
- B. Value-change coverage
- C. Threshold coverage
- D. Neuron coverage
Answer: C
Explanation:
Threshold coverageis a specific type of coverage measure used in neural network testing. It ensures that each neuron in the network achieves an activation value greater than a specified threshold. This is particularly relevant to the scenario described, where testers verify that neurons activate only when the future value of the commodity exceeds the spot price by at least0.75%.
* Threshold-based activation:The test case in the question isexplicitly verifying whether neurons activate only when a certain threshold (0.75%) is exceeded.This aligns perfectly with the definition ofthreshold coverage.
* Common in Neural Network Testing:Threshold coverage is used to measurewhether each neuron in a neural network reaches a specified activation value, ensuring that the neural network behaves as expected when exposed to different test inputs.
* Precedent in Research:TheDeepXplore frameworkused a threshold of0.75%to identify incorrect behaviors in neural networks, making this coverage criterion well-documented in AI testing research.
* (B) Neuron Coverage#
* Neuron coverageonly checks whether a neuron activates (non-zero value)at some point during testing. It does not consider specific activation thresholds, making it less precise for this scenario.
* (C) Sign-Change Coverage#
* This coverage measures whether each neuron exhibitsboth positive and negative activation values, which isnot relevant to the given scenario(where activation only matters when exceeding a specific threshold).
* (D) Value-Change Coverage#
* This coverage requires each neuron to producetwo activation values that differ by a chosen threshold, but the question focuses onwhether activation occurs beyond a fixed threshold, not changes in activation values.
* Threshold coverage ensures that neurons exceed a given activation threshold"Full threshold coverage requires that each neuron in the neural network achieves an activation value greater than a specified threshold. The researchers who created the DeepXplore framework suggested neuron coverage should be measured based on an activation value exceeding a threshold, changing based on the situation." Why is Threshold Coverage Correct?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, asthreshold coverage ensures the neural network's activation is correctly evaluated based on the required condition (0.75%).
NEW QUESTION # 37
Which of the following descriptions of quality aspects of a data set is correct?
Choose ONE option (1 out of 4)
- A. The quality aspect "Irrelevant data" describes the fact that irrelevant data does not affect the ML model.
- B. The quality aspect "Incomplete data" describes the fact that data is missing, e.g., for a certain time interval.
- C. The quality aspect "Unbalanced data" describes the fact that the data used should be as up-to-date as possible.
- D. The quality aspect "Data not preprocessed" describes the fact that the collected data was recorded incorrectly.
Answer: B
Explanation:
The ISTQB CT-AI syllabus describes severaldata quality aspectsthat affect ML performance. In Section2.2
- Data Preparation, it explains that datasets may suffer from issues such asincomplete data,irrelevant data, incorrect data,unbalanced data, or data lacking preprocessing. "Incomplete data" means thatportions of the required data are missing, often because some time periods, records, or sources were not captured. This aligns exactly with Option A, which correctly identifies missing intervals as incomplete data.
Option B is incorrect: "data not preprocessed" refers to data that has not undergone normalization, cleaning, or transformation-not data recorded incorrectly. Option C is wrong because irrelevant datadoesnegatively affect ML models by introducing noise and unnecessary features. The syllabus explicitly states that including irrelevant features can degrade model learning. Option D is incorrect: "unbalanced data" relates to disproportionate class distribution, not recency or freshness of data.
Thus, OptionAis the only statement that correctly matches the syllabus definition of this data quality aspect.
NEW QUESTION # 38
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. Al systems are inherently flexible.
- C. Flexible Al systems allow for easier modification of the system as a whole.
- D. Al systems require changing of operational environments; therefore, flexibility is required.
Answer: C
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.
References:
* 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 # 39
Consider an AI-system in which the complex internal structure has been generated by another software system. Why would the tester choose to do black-box testing on this particular system?
- A. Test automation can be built quickly and easily from the test cases developed during black-box testing
- B. The tester wishes to better understand the logic of the software used to create the internal structure
- C. Black-box testing eliminates the need for the tester to understand the internal structure of the AI-system
- D. The black-box testing method will allow the tester to check the transparency of the algorithm used to create the internal structure
Answer: C
Explanation:
The syllabus explains:
"Where the internal structure of an AI-based system is too complex for humans to understand, the system can only be tested as a black box. Even when the internal structure is visible, this provides no additional useful information to help with testing." This confirms that black-box testing is chosen because the tester does not need to understand the system's internal structure.
(Reference: ISTQB CT-AI Syllabus v1.0, Section 8.5, page 61 of 99)
NEW QUESTION # 40
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. Different Road Types
- B. ML model metrics to evaluate the functional performance
- C. Different weather conditions
- D. Different features like ADAS, Lane Change Assistance etc.
Answer: B
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 # 41
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?
- A. The robot must complete 99.99% of its deliveries each day
- B. The robot must evolve to optimize its routing
- C. The robot must recharge for no more than six hours a day
- D. The robot must record the time of each delivery which is compiled into a report
Answer: B
Explanation:
In the syllabus, theevolutioncharacteristic for AI-based systems means the ability of the system to evolve and adapt its behavior in response to changes in the environment or in its own performance:
"Evolution is the system's ability to change itself to adapt to new situations, different hardware, or a changing operational environment." (Reference: ISTQB CT-AI Syllabus v1.0, Section 2.3)
NEW QUESTION # 42
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