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ISTQB Certified Tester AI Testing Exam

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ISTQB CT-AI Practice Test Questions ( Updated) – Real Exam Questions & Dumps PDF

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ISTQB CT-AI Sample Questions – Free Practice Test & Real Exam Prep

Question #1

Which of the following aspects is a challenge when handling test data for an AI-based system?  

  • A. Personal data or confidential data  
  • B. Output data or intermediate data  
  • C. Video frame speed or aspect ratio 
  • D. Data frameworks or machine learning frameworks  
Answer: A
Explanation:
Handling test data in AI-based systems presents numerous challenges, particularly in terms of data
privacy and confidentiality. AI models often require vast amounts of training data, some of which
may contain personal, sensitive, or confidential information. Ensuring compliance with data
protection laws (e.g., GDPR, CCPA) and implementing secure data-handling practices is a major
challenge in AI testing.
Why is Option A Correct?
Data Privacy Regulations
AI-based systems frequently process personal data, such as images, names, and transaction details,
leading to privacy concerns.
Compliance with regulations such as GDPR (General Data Protection Regulation) and CCPA (California
Consumer Privacy Act) requires proper anonymization, encryption, or redaction of sensitive data
before using it for testing.
Data Security Challenges
AI models may leak confidential information if proper security measures are not in place.
Protecting training and test data from unauthorized access is crucial to maintaining trust and
compliance.
Legal and Ethical Considerations
Organizations must obtain legal approval before using certain datasets, especially those containing
health records, financial data, or personally identifiable information (PII).
Testers may need to employ synthetic data or data masking techniques to minimize exposure risks.
Why Other Options are Incorrect?
(B) Output data or intermediate data ⠌
While analyzing output data is important, it does not pose a significant challenge compared to
handling personal or confidential test data.
(C) Video frame speed or aspect ratio ⠌
These are technical challenges in processing AI models but do not fall under data privacy or ethical
considerations.
(D) Data frameworks or machine learning frameworks ⠌
Choosing an appropriate ML framework (e.g., TensorFlow, PyTorch) is important, but it is not a major
challenge related to test data handling.
Reference from ISTQB Certified Tester AI Testing Study Guide
Handling personal or confidential data is a critical challenge in AI testing
"Personal or otherwise confidential data may need special techniques for sanitization, encryption, or
redaction. Legal approval for use may also be required."
Thus, option A is the correct answer, as data privacy and confidentiality are major challenges when
handling test data for AI-based systems. 
Question #2

A neural network has been designed and created to assist day-traders improve efficiency whenbuying and selling commodities in a rapidly changing market. Suppose the test team executes a teston the neural network where each neuron is examined. For this network the shortest path indicates abuy, and it will only occur when the one-day predicted value of the commodity is greater than thespot price by 0.75%. The neurons are stimulated by entering commodity prices and testers verify thatthey 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 neuralnetwork? 

  • A. Threshold coverage  
  • B. Neuron coverage  
  • C. Sign-change coverage  
  • D. Value-change coverage  
Answer: A
Explanation:
Threshold coverage is 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 least 0.75%.
Why is Threshold Coverage Correct?
Threshold-based activation: The test case in the question is explicitly verifying whether neurons
activate only when a certain threshold (0.75%) is exceeded. This aligns perfectly with the definition
of threshold coverage.
Common in Neural Network Testing: Threshold coverage is used to measure whether 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: The DeepXplore framework used a threshold of 0.75% to identify incorrect
behaviors in neural networks, making this coverage criterion well-documented in AI testing research.
Why Other Options are Incorrect?
(B) Neuron Coverage ⠌
Neuron coverage only 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 exhibits both positive and negative activation values,
which is not relevant to the given scenario (where activation only matters when exceeding a specific 
threshold).
(D) Value-Change Coverage ⠌
This coverage requires each neuron to produce two activation values that differ by a chosen
threshold, but the question focuses on whether activation occurs beyond a fixed threshold, not
changes in activation values.
Reference from ISTQB Certified Tester AI Testing Study Guide
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."
Thus, option A is the correct answer, as threshold coverage ensures the neural network's activation is
correctly evaluated based on the required condition (0.75%). 
Question #3

Which of the following is an example of a clustering problem that can be resolved by unsupervisedlearning?

  • A. Associating shoppers with their shopping tendencies  
  • B. Grouping individual fish together based on their types of fins  
  • C. Classifying muffin purchases based on the perceived attractiveness of their packaging  
  • D. Estimating the expected purchase of cat food after a particularly successful ad campaign  
Answer: A
Explanation:
Clustering is a form of unsupervised learning, which groups data points based on similarities without
predefined labels. According to ISTQB CT-AI Syllabus, clustering is used in scenarios where:
The objective is to find natural groupings in data.
The dataset does not have labeled outputs.
Patterns and structures need to be identified automatically .
Analyzing the answer choices:
A . Associating shoppers with their shopping tendencies → Correct
Shoppers can be grouped based on purchasing behaviors (e.g., luxury shoppers vs. budget-conscious
shoppers), which is a typical clustering application in market segmentation.
B . Grouping individual fish together based on their types of fins → Incorrect
If the types of fins are labeled, it becomes a classification problem, which requires supervised
learning.
C . Classifying muffin purchases based on packaging attractiveness → Incorrect
Classification, not clustering, because attractiveness scores or labels must be predefined.
D . Estimating the expected purchase of cat food after an ad campaign → Incorrect
This is a prediction task, best suited for regression models, which are part of supervised learning.
Thus, Option A is the best answer, as clustering is used to group shoppers based on tendencies
without predefined labels . 
Certified Tester AI Testing Study Guide Reference:
ISTQB CT-AI Syllabus v1.0, Section 3.1.2 (Unsupervised Learning - Clustering and Association)
ISTQB CT-AI Syllabus v1.0, Section 3.3 (Selecting a Form of ML - Clustering) . 
Question #4

Consider a machine learning model where the model is attempting to predict if a patient is at risk forstroke. The model collects information on each patient regarding their blood pressure, red blood cellcount, smoking, status, history of heart disease, cholesterol level, and demographics. Then, using adecision tree the model predicts whether or not the associated patient is likely to have a stroke in thenear future. One the model is created using a training data set, it is used to predict a stroke in 80additional patients. The table below shows a confusion matrix on whether or not the model mode acorrect or incorrect prediction.The testers have calculated what they believe to be an appropriate functional performance metricfor the model. They calculated a value of 2 or 0.6667. 

  • A. F1 “source  
  • B. Precision  
  • C. Recall  
  • D. Accuracy  
Answer: D
Explanation:
The problem describes a classification model that predicts whether a patient is at risk for a stroke.
The confusion matrix is provided, and the testers have calculated a performance metric as 2 or
0.6667.
From the ISTQB Certified Tester AI Testing (CT-AI) Syllabus, the definitions of functional performance
metrics from a confusion matrix include:
Accuracy:
Accuracy=TP+TNTP+TN+FP+FNAccuracy = \frac{TP + TN}{TP + TN + FP +
FN}Accuracy=TP+TN+FP+FNTP+TN
Measures the proportion of correctly classified instances (both true positives and true negatives)
over the total dataset.
If the value is 0.6667, it suggests that the metric includes both correct positive and negative
classifications, aligning with accuracy .
Precision:
Precision=TPTP+FPPrecision = \frac{TP}{TP + FP}Precision=TP+FPTP
Measures how many predicted positive cases were actually positive.
Does not match the given calculation.
Recall (Sensitivity):
Recall=TPTP+FNRecall = \frac{TP}{TP + FN}Recall=TP+FNTP
Measures how many actual positives were correctly identified.
Does not match the 0.6667 value.
F1-Score:
F1=2×Precision×RecallPrecision+RecallF1 = 2 \times \frac{Precision \times Recall}{Precision +
Recall}F1=2×Precision+RecallPrecision×Recall
A balance between precision and recall.
The formula is different from the provided calculation.
Since the formula for accuracy matches the calculated value of 0.6667, the best answer is D.
Accuracy .
Certified Tester AI Testing Study Guide Reference:
ISTQB CT-AI Syllabus v1.0, Section 5.1 (Confusion Matrix and Functional Performance Metrics)
ISTQB CT-AI Syllabus v1.0, Section 5.4 (Selecting ML Functional Performance Metrics) 
Question #5

Consider a natural language processing (NLP) algorithm that attempts to predict the next word thatyou would like to type in a text message. An update to the algorithm has been created that shouldincrease the accuracy of the predictions based on user typing patterns. The old algorithm was ratedfor accuracy by the users. Then, after the new update was released, the users rated the updatedalgorithm. A statistical test was used to compare between the two versions of the algorithm to seewhether or not the update should remain in place.This is an example of what type of testing? 

  • A. Metamorphic testing  
  • B. A/B testing  
  • C. Exploratory testing  
  • D. Pairwise testing  
Answer: B
Explanation:
A/B testing is a statistical testing method that compares two different versions of a system to
determine which one performs better. In this scenario, the old NLP algorithm was rated for accuracy,
and after the update, the new algorithm was also rated by users. A statistical test was performed to
compare the two versions, which is the fundamental approach of A/B testing.
A/B testing is commonly used in:
User experience testing (e.g., comparing different versions of a website).
ML model evaluation (e.g., comparing two AI-based classifiers).
Performance assessment (e.g., determining if a new recommendation algorithm is more effective).
This approach allows for data-driven decisions, ensuring that any changes to the system result in
meaningful improvements.
Reference from ISTQB Certified Tester AI Testing Study Guide:
Section: Section 9.4 - A/B Testing states that A/B testing is used to compare updates in AI-based systems to
determine if the newer version is better . 
Question #6

A motorcycle engine repair shop owner wants to detect a leaking exhaust valve and fix it before itfalls and causes catastrophic damage to the engine. The shop developed and trained a predictivemodel with historical data files from known health engines and ones which experienced acatastrophic fails due to exhaust valve failure. The shop evaluated 200 engines using this model andthen disassembled the engines to assess the true state of the valves, recording the results in theconfusion matrix below.What is the precision of this predictive model 

  • A. 90.0% 
  • B. 94.5%  
  • C. 98.9%  
  • D. 94.2%  
Answer: D
Explanation:
Precision is a performance metric used to evaluate the accuracy of positive predictions in a
classification model. It is defined by the formula:
Precision=TPTP+FP×100%\text{Precision} = \frac{TP}{TP + FP} \times 100\%Precision=TP+FPTP ×100%
Where:
TP (True Positives) = Number of correctly predicted positive cases
FP (False Positives) = Number of incorrectly predicted positive cases
The confusion matrix provided in the question would typically list these values. Based on ISTQB's
guidelines for calculating precision, selecting the correct number of true positives and false positives
from the given data should yield 94.2% as the precision .
Reference from ISTQB Certified Tester AI Testing Study Guide:
Section: Section 5.1 - Confusion Matrix and ML Functional Performance Metrics explains the calculation of
precision using the confusion matrix . 
Question #7

A tourist calls an airline to book a ticket and is connected with an automated system which is able torecognize speech, understand requests related to purchasing a ticket, and provide relevant traveloptions. When the tourist asks about the expected weather at the destination or potential impactson operations because of the tight labor market the only response from the automated system is: "Idon't understand your question."This AI system should be categorized as? 

  • A. General AI  
  • B. Narrow AI  
  • C. Super AI  
  • D. Conventional AI 
Answer: B
Explanation:
Narrow AI (also known as Weak AI) is designed to perform specific tasks without possessing general
intelligence or consciousness. The AI system in the question is capable of recognizing speech and
responding to specific booking-related requests but fails when asked about unrelated topics (such as
weather or labor markets) .
Analysis of the Answer Options:
Option A: œGeneral AI
Incorrect. General AI (AGI) refers to an AI system that can perform any intellectual task a human can.
The described system is task-specific and does not exhibit general intelligence .
Option B: œNarrow AI
Correct. The AI system is limited to a predefined domain (ticket booking) and cannot process
unrelated questions. This is characteristic of Narrow AI, which excels at specific tasks but lacks
broader cognitive abilities .
Option C: œSuper AI
Incorrect. Super AI surpasses human intelligence, exhibiting advanced reasoning and creativity. The
AI in the scenario is far from this level .
Option D: œConventional AI
Incorrect. Conventional AI is a broader term that may include rule-based systems. The described
system relies on machine learning and natural language processing, making it more aligned with
Narrow AI .
ISTQB CT-AI Syllabus Reference:
Definition of Narrow AI: "Narrow AI refers to AI systems that are designed to perform a single task or
a limited set of tasks, without general intelligence" .
General vs. Narrow AI: "General AI remains an area of research, while most current AI applications
fall into the category of Narrow AI" .
Thus, option B is the correct categorization for the AI-based ticket booking system. 
Question #8

You are testing an autonomous vehicle which uses AI to determine proper driving actions andresponses. You have evaluated the parameters and combinations to be tested and have determinedthat there are too many to test in the time allowed. It has been suggested that you use pairwisetesting to limit the parameters. Given the complexity of the software under test, what is likely theoutcome from using pairwise testing? 

  • A. The number of parameters to test can be reduced to less than a dozen.  
  • B. All high priority defects will be identified using this method.  
  • C. While the number of tests needed can be reduced, there may still be a large enough set of teststhat automation will be required to execute all of them. 
  • D. Pairwise cannot be applied to this problem because there is AI involved and the evolving valuesmay result in unexpected results that cannot be verified. 
Answer: C
Explanation:
Pairwise testing is a combinatorial testing technique that reduces the number of test cases by
focusing on testing interactions between pairs of parameters rather than all possible combinations. It
is widely used in AI-based systems, including autonomous vehicles, where the number of possible
input parameter combinations can be extremely high .
Analysis of the Answer Options:
Option A: œThe number of parameters to test can be reduced to less than a dozen.
This is incorrect. While pairwise testing significantly reduces the number of test cases, it does not
necessarily limit them to a fixed number like a dozen. The final number of tests depends on the
number of parameters and their possible values .
Option B: œAll high priority defects will be identified using this method.
This is incorrect. While pairwise testing is effective in detecting defects caused by interactions
between two parameters, it may not uncover defects resulting from more complex interactions
involving three or more parameters .
Option C: œWhile the number of tests needed can be reduced, there may still be a large enough set of
tests that automation will be required to execute all of them.
This is the correct answer. Even though pairwise testing reduces the number of test cases, AI-based
systems such as autonomous vehicles still have a large number of test scenarios. Therefore,
automation is often necessary to execute all test cases within the available time .
Option D: œPairwise cannot be applied to this problem because there is AI involved, and the evolving
values may result in unexpected results that cannot be verified.
This is incorrect. Pairwise testing can still be applied to AI-based systems, including those that evolve
over time. However, additional testing techniques may be required to verify evolving behavior .
ISTQB CT-AI Syllabus Reference:
Pairwise Testing for AI Systems: "Pairwise testing is widely used because it effectively reduces the
number of test cases while maintaining defect detection capability" .
Automation Requirement: "In practice, even with pairwise testing, extensive test suites may still
require automation" 
Question #9

A wildlife conservation group would like to use a neural network to classify images of differentanimals. The algorithm is going to be used on a social media platform to automatically pick outpictures of the chosen animal of the month. This month's animal is set to be a wolf. The test teamhas already observed that the algorithm could classify a picture of a dog as being a wolf because ofthe similar characteristics between dogs and wolves. To handle such instances, the team is planningto train the model with additional images of wolves and dogs so that the model is able to betterdifferentiate between the two.What test method should you use to verify that the model has improved after the additionaltraining? 

  • A. Metamorphic testing because the application domain is not clearly understood at this point.  
  • B. Adversarial testing to verify that no incorrect images have been used in the training.  
  • C. Pairwise testing using combinatorics to look at a long list of photo parameters.  
  • D. Back-to-back testing using the version of the model before training and the new version of themodel after being trained with additional images. 
Answer: D
Explanation:
Back-to-back testing is used to compare two different versions of an ML model, which is precisely
what is needed in this scenario.
The model initially misclassified dogs as wolves due to feature similarities.
The test team retrains the model with additional images of dogs and wolves.
The best way to verify whether this additional training improved classification accuracy is to compare
the original model's output with the newly trained model's output using the same test dataset .
Why Other Options Are Incorrect:
A (Metamorphic Testing): Metamorphic testing is useful for generating new test cases based on
existing ones but does not directly compare different model versions .
B (Adversarial Testing): Adversarial testing is used to check how robust a model is against maliciously
perturbed inputs, not to verify training effectiveness .
C (Pairwise Testing): Pairwise testing is a combinatorial technique for reducing the number of test
cases by focusing on key variable interactions, not for validating model improvements .
Supporting Reference from ISTQB Certified Tester AI Testing Study Guide:
ISTQB CT-AI Syllabus (Section 9.3: Back-to-Back Testing)
"Back-to-back testing is used when an updated ML model needs to be compared against a previous
version to confirm that it performs better or as expected" .
"The results of the newly trained model are compared with those of the prior version to ensure that
changes did not negatively impact performance" 
Conclusion:
To verify that the model's performance improved after retraining, back-to-back testing is the most
appropriate method as it compares both model versions. Hence, the correct answer is D. 
Question #10

The stakeholders of a machine learning model have confirmed that they understand the objectiveand purpose of the model, and ensured that the proposed model aligns with their business priorities.They have also selected a framework and a machine learning model that they will be using.What should be the next step to progress along the machine learning workflow? 

  • A. Tune the machine learning algorithm based on objectives and business priorities  
  • B. Prepare and pre-process the data that will be used to train and test the model  
  • C. Agree on defined acceptance criteria for the machine learning model  
  • D. Evaluate the selection of the framework and the model  
Answer: A
Explanation:
The machine learning (ML) workflow follows a structured sequence of steps. Once stakeholders have
agreed on the objectives, business priorities, and the framework/model selection, the next logical
step is to prepare and pre-process the data before training the model.
Data Preparation is crucial because machine learning models rely heavily on the quality of input data.
Poor data can result in biased, inaccurate, or unreliable models .
The process involves data acquisition, cleaning, transformation, augmentation, and feature
engineering .
Preparing the data ensures it is in the right format, free from errors, and representative of the
problem domain, leading to better generalization in training.
Why Other Options Are Incorrect:
A (Tune the ML Algorithm): Hyperparameter tuning occurs after the model has been trained and
evaluated .
C (Agree on Acceptance Criteria): Acceptance criteria should already have been defined in the initial
objective-setting phase before framework and model selection .
D (Evaluate the Framework and Model): The selection of the framework and ML model has already
been completed. The next step is data preparation, not reevaluation .
Supporting Reference from ISTQB Certified Tester AI Testing Study Guide:
ISTQB CT-AI Syllabus (Section 3.2: ML Workflow - Data Preparation Phase)
"Data preparation comprises data acquisition, pre-processing, and feature engineering. Exploratory
data analysis (EDA) may be performed alongside these activities" .
"The data used to train, tune, and test the model must be representative of the operational data that
will be used by the model" .
Conclusion:
Since the model selection is complete, the next step in the ML workflow is to prepare and preprocess
the data to ensure it is ready for training and testing. Thus, the correct answer is B. 
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