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ISTQB CT-AI Sample Questions
Question # 1
You have access to the training data that was used to train an AI-based system. You can review thisinformation and use it as a guideline when creating your tests. What type of characteristic is this?
A. Autonomy B. Explorability C. Transparency D. Accessibility
Answer: C
Explanation:
AI-based systems can sometimes behave like black boxes, where the internal decision-making
process is unclear. Transparency refers to the ability to inspect and understand the training data,
algorithms, and decision-making process of the AI system.
Why is Option C Correct?
Transparency ensures that testers and stakeholders can review how an AI system was trained.
Access to training data is a key factor in transparency because it allows testers to analyze biases,
completeness, and representativeness of the dataset.
Transparency is an essential characteristic of explainable AI (XAI).
Having access to training data means that testers can investigate how data influences AI behavior.
Regulatory and ethical AI guidelines emphasize transparency.
Many AI ethics frameworks, such as GDPR and Trustworthy AI guidelines, recommend transparency
to ensure fair and explainable AI decision-making.
Why Other Options are Incorrect?
(A) Autonomy ⠌
Autonomy refers to an AI systems ability to make decisions independently without human
intervention. However, having access to training data does not relate to autonomy, which is more
about self-learning and decision-making without human control.
(B) Explorability ⠌
Explorability refers to the ability to test AI systems interactively to understand their behavior, but it
does not directly relate to accessing training data.
(D) Accessibility ⠌
Accessibility refers to the ease with which people can use the system, not the ability to inspect the
training data.
Reference from ISTQB Certified Tester AI Testing Study Guide
Transparency is the ease with which the training data and algorithm used to generate a model can be understood.
"Transparency: This is considered to be the ease with which the algorithm and training data used to generate the model can be determined."
Thus, option C is the correct answer, as transparency involves access to training data, allowing testers to understand AI decision-making processes.
Question # 2
A transportation company operates three types of delivery vehicles in its fleet. The vehicles operateat different speeds (slow, medium, and fast). The transportation company is attempting to optimizescheduling and has created an AI-based program to plan routes for its vehicles using records from themedium-speed vehicle traveling to selected destinations. The test team uses this data inmetamorphic testing to test the accuracy of the estimated travel times created by the AI routeplanner with the actual routes and times.Which of the following describes the next phase of metamorphic testing?
A. The team tests the time required for the fast and slow vehicles to travel the same route as themedium vehicle. Then, by calculating the speed difference, they then predict how much faster orslower the vehicles will travel. That information is then used to verify that the arrival time of thevehicles meets the expected result. B. The team decomposes each route into the relevant components that affect the travel time such astraffic density and vehicle power. The team then uses statistical analysis to characterize the influenceof each component to calculate the fast and slow vehicle route times. C. The team uses an AI system to select the most dissimilar routes. With this information, any of theAI routes can be metaphorically transformed into a fast or slow route. D. The team uses the same AI route planner to create routes that are longer and shorter but followthe same track. Finally, by driving the fast vehicles on the long routes and slow vehicles on the shortroutes and vice versa, the AI system will have enough information to infer travel times for all vehicleson all routes.
Answer: A
Explanation:
Metamorphic Testing (MT) is a testing technique that verifies AI-based systems by generating followup
test cases based on existing test cases. These follow-up test cases adhere to a Metamorphic
Relation (MR), ensuring that if the system is functioning correctly, changes in input should result in
predictable changes in output.
Why Option A is Correct?
Metamorphic testing works by transforming source test cases into follow-up test cases
Here, the source test case involves testing the medium-speed vehicles travel time.
The follow-up test cases are derived by extrapolating travel times for fast and slow vehicles using
predictable relationships based on speed differences.
MR states that modifying input should result in a predictable change in output
Since the speed of the vehicle is a known factor, it is possible to predict the new arrival times and
verify whether they follow expected trends.
This is a direct application of metamorphic testing principles
In route optimization systems, metamorphic testing often applies transformations to speed, distance,
or conditions to verify expected outcomes.
Why Other Options are Incorrect?
(B) Decomposing each route into traffic density and vehicle power ⠌
While useful for statistical analysis, this approach does not generate follow-up test cases based on a
defined metamorphic relation (MR).
(C) Selecting dissimilar routes and transforming them into a fast or slow route ⠌
This does not follow metamorphic testing principles, which require predictable transformations.
(D) Running fast vehicles on long routes and slow vehicles on short routes ⠌
This method does not maintain a controlled MR and introduces too many uncontrolled variables.
Reference from ISTQB Certified Tester AI Testing Study Guide
Metamorphic testing generates follow-up test cases based on a source test case.
"MT is a technique aimed at generating test cases which are based on a source test case that has
passed. One or more follow-up test cases are generated by changing (metamorphizing) the source
test case based on a metamorphic relation (MR)."
MT has been used for testing route optimization AI systems.
"In the area of AI, MT has been used for testing image recognition, search engines, route
optimization and voice recognition, among others."
Thus, option A is the correct answer, as it aligns with the principles of metamorphic testing by
modifying input speeds and verifying expected results.
Question # 3
A mobile app start-up company is implementing an AI-based chat assistant for e-commercecustomers. In the process of planning the testing, the team realizes that the specifications areinsufficient.Which testing approach should be used to test this system?
A. Exploratory testing B. Static analysis C. Equivalence partitioning D. State transition testing
Answer: A
Explanation:
When testing an AI-based chat assistant for e-commerce customers, the lack of sufficient
specifications makes it difficult to use structured test techniques. The ISTQB CT-AI Syllabus
recommends exploratory testing in such cases:
Why Exploratory Testing?
Exploratory testing is useful when specifications are incomplete or unclear.
AI-based systems, particularly those using natural language processing (NLP), may not behave
deterministically, making scripted test cases ineffective.
The tester interacts dynamically with the system, identifying unexpected behaviors not documented
in the specification .
Analysis of Answer Choices:
A (Exploratory testing) → Correct, as it is the best approach when specifications are incomplete.
B (Static analysis) → Incorrect, as static analysis checks code without execution, which is not helpful
for AI chatbots.
C (Equivalence partitioning) → Incorrect, as this technique requires well-defined inputs and outputs,
which are missing due to insufficient specifications.
D (State transition testing) → Incorrect, as state-based testing requires knowledge of valid and invalid
transitions, which is difficult with a chatbot lacking a clear specification .
Thus, Option A is the correct answer, as exploratory testing is the best approach when dealing with
insufficient specifications in AI-based systems.
Certified Tester AI Testing Study Guide Reference:
ISTQB CT-AI Syllabus v1.0, Section 7.7 (Selecting a Test Approach for an ML System)
Which of the following is correct regarding the layers of a deep neural network?
A. There is only an input and output layer B. There is at least one internal hidden layer C. There must be a minimum of five total layers to be considered deep D. The output layer is not connected with the other layers to maintain integrity
Answer: B
Explanation:
A deep neural network (DNN) is a type of artificial neural network that consists of multiple layers
between the input and output layers. The ISTQB Certified Tester AI Testing (CT-AI) Syllabus outlines
the following characteristics of a DNN:
Structure of a Deep Neural Network:
A DNN comprises at least three types of layers:
Input layer: Receives the input data.
Hidden layers: Perform complex feature extraction and transformations.
Output layer: Produces the final prediction or classification .
Analysis of Answer Choices:
A (Only input and output layers) → Incorrect, as a DNN must have at least one hidden layer.
B (At least one internal hidden layer) → Correct, as a neural network must have hidden layers to be
considered deep.
C (Minimum of five layers required) → Incorrect, as there is no strict definition that requires at least
five layers.
D (Output layer is not connected to other layers) → Incorrect, as the output layer must be connected
to the hidden layers .
Thus, Option B is the correct answer, as a deep neural network must have at least one hidden layer.
Certified Tester AI Testing Study Guide Reference:
ISTQB CT-AI Syllabus v1.0, Section 6.1 (Neural Networks and Deep Neural Networks)
ISTQB CT-AI Syllabus v1.0, Section 6.2 (Structure of Deep Neural Networks) .
Question # 5
When verifying that an autonomous AI-based system is acting appropriately, which of the followingare MOST important to include?
A. Test cases to verify that the system automatically confirms the correct classification of trainingdata B. Test cases to detect the system appropriately automating its data input C. Test cases to detect the system prompting for unnecessary human intervention D. Test cases to verify that the system automatically suppresses invalid output data
Answer: C
Explanation:
When verifying autonomous AI-based systems, a critical aspect is ensuring that they maintain an
appropriate level of autonomy while only requesting human intervention when necessary. If an AI
system unnecessarily asks for human input, it defeats the purpose of autonomy and can:
Slow down operations.
Reduce trust in the system.
Indicate improper confidence thresholds in decision-making.
This is particularly crucial in autonomous vehicles, AI-driven financial trading, and robotic process
automation, where excessive human intervention would hinder performance.
Why are the other options incorrect?
A . Test cases to verify that the system automatically confirms the correct classification of training
data → This is relevant for verifying training consistency but not for autonomy validation.
B . Test cases to detect the system appropriately automating its data input → While relevant, data
automation does not directly address the verification of autonomy.
D . Test cases to verify that the system automatically suppresses invalid output data → This focuses
on output filtering rather than decision-making autonomy.
Thus, the most critical test case for verifying autonomous AI-based systems is ensuring that it does
not unnecessarily request human intervention.
Reference from ISTQB Certified Tester AI Testing Study Guide:
Section: Section 8.2 - Testing Autonomous AI-Based Systems states that it is crucial to test whether the system
requests human intervention only when necessary and does not disrupt autonomy .
Question # 6
A beer company is trying to understand how much recognition its logo has in the market. It plans todo that by monitoring images on various social media platforms using a pre-trained neural networkfor logo detection. This particular model has been trained by looking for words, as well as matchingcolors on social media images. The company logo has a big word across the middle with a bold blueand magenta border.Which associated risk is most likely to occur when using this pre-trained model?
A. There is no risk, as the model has already been trained B. Insufficient function; the model was not trained to check for colors or words C. Improper data preparation D. Inherited bias: the model could have inherited unknown defects
Answer: D
Explanation:
A major risk when using a pre-trained neural network for logo detection is that it may inherit biases
and defects from the original dataset and training process. This means that the model could
misidentify or fail to recognize certain logos due to:
Differences in data preparation: The original training data may have used a different preprocessing
method than the new dataset, leading to inconsistencies.
Limited transparency: The exact details of the dataset and biases within it may not be known, which
can cause unexpected behavior.
Bias in logo detection: If the model was trained on a dataset with certain color or text preferences, it
may disproportionately misidentify logos with similar characteristics.
This inherited bias can result in:
False Positives: Recognizing other brand logos as the beer company's logo.
False Negatives: Failing to detect the actual logo when variations occur (e.g., different lighting or
partial visibility).
Algorithmic Bias: The model may favor certain shapes or color contrasts due to biased training data.
Thus, the most appropriate risk associated with using this pre-trained model is inherited bias.
Reference from ISTQB Certified Tester AI Testing Study Guide:
Section: Section 1.8.3 - Risks of Using Pre-Trained Models and Transfer Learning explains how pre-trained
models may inherit biases and undocumented defects that affect performance in a new
environment .
Question # 7
A local business has a mail pickup/delivery robot for their office. The robot currently uses a track tomove between pickup/drop off locations. When it arrives at a destination, the robot stops to allow ahuman to remove or deposit mail.The office has decided to upgrade the robot to include AI capabilities that allow the robot to performits 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 acceptancecriteria to be used in the testing of the robot upgrade. Which of the following test objectives will testan AI quality characteristic for this system?
A. The robot must evolve to optimize its routing B. The robot must recharge for no more than six hours a day C. The robot must record the time of each delivery which is compiled into a report D. The robot must complete 99.99% of its deliveries each day
Answer: A
Explanation:
AI-based systems have specific quality characteristics, including evolution, autonomy, and
adaptability. A test objective that evaluates whether an AI system evolves to improve performance
over time directly aligns with AI quality characteristics .
Explanation of Answer Choices:
Option A: The robot must evolve to optimize its routing.
Correct. Evolution is an AI quality characteristic that ensures the system learns from past experiences
and adapts to improve efficiency .
Option B: The robot must recharge for no more than six hours a day.
Incorrect. This is an operational constraint rather than an AI-specific quality characteristic .
Option C: The robot must record the time of each delivery which is compiled into a report.
Incorrect. Logging data does not relate to AI quality characteristics like adaptability or autonomy .
Option D: The robot must complete 99.99% of its deliveries each day.
Incorrect. This is a performance target rather than an AI quality characteristic .
ISTQB CT-AI Syllabus Reference:
Evolution as an AI Quality Characteristic: "Check how well the system learns from its own experience.
Check how well the system copes when the profile of data changes (i.e., concept drift)" .
Thus, Option A is the best choice as it directly tests an AI quality characteristic (evolution) in the
upgraded autonomous robot.
Question # 8
Which of the following is a dataset issue that can be resolved using pre-processing?
A. Insufficient data B. Invalid data C. Wanted outliers D. Numbers stored as strings
Answer: D
Explanation:
Pre-processing is an essential step in data preparation that ensures data is clean, formatted correctly,
and structured for effective machine learning (ML) model training. One common issue that can be
resolved during pre-processing is numbers stored as strings.
Explanation of Answer Choices:
Option A: Insufficient data
Incorrect. Pre-processing cannot resolve insufficient data. If data is lacking, techniques like data
augmentation or external data collection are needed .
Option B: Invalid data
Incorrect. While pre-processing can identify and handle some forms of invalid data (e.g., missing
values, duplicate entries), it does not resolve all invalid data issues. Some cases may require domain
expertise to determine validity .
Option C: Wanted outliers
Incorrect. Pre-processing usually focuses on handling unwanted outliers. Wanted outliers may need
to be preserved, which is more of a data selection decision rather than pre-processing .
Option D: Numbers stored as strings
Correct. One of the key functions of data pre-processing is data transformation, which includes
converting incorrectly formatted data types, such as numbers stored as strings, into their correct
numerical format .
ISTQB CT-AI Syllabus Reference:
Data Pre-Processing Steps: "Transformation: The format of the given data is changed (e.g., breaking
an address held as a string into its constituent parts, dropping a field holding a random identifier,
converting categorical data into numerical data, changing image formats)" .
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