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ISTQB CT-AI study materials
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ISTQB CT-AI practice questions

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Question 1Introduction to AI and AI-based systems

Which statement best describes what is meant by artificial intelligence in the context of software systems?

  1. Software that exclusively uses deep neural networks and big data
  2. Software performing tasks that normally require human intelligence Correct answer.
  3. Software that has become conscious and self-aware
  4. Software that executes a fixed sequence of coded rules

Explanation

AI refers to systems that carry out tasks which typically need human intelligence, such as perceiving, reasoning, learning and deciding. It is a broad label covering many techniques, not a single implementation style. Consciousness is not part of the practical definition used in software engineering.

Question 2Introduction to AI and AI-based systems

A system built to perform one specific task, such as classifying images of machine parts, is best described as:

  1. Artificial general intelligence
  2. A general-purpose reasoning engine
  3. Narrow AI Correct answer.
  4. Superintelligence

Explanation

Narrow AI, also called weak AI, is designed for a limited task or domain and cannot transfer its competence to unrelated problems. Almost all deployed commercial AI systems today fall into this category. General and super intelligence refer to much broader, currently hypothetical capabilities.

Question 3Introduction to AI and AI-based systems

Which description best matches artificial general intelligence (AGI)?

  1. A knowledge-based system with many hand-coded rules
  2. A system restricted to a single domain, such as playing chess
  3. A system that outperforms human experts on every cognitive task it attempts
  4. A system able to perform any intellectual task a human can perform Correct answer.

Explanation

AGI denotes a system with human-level competence across the full range of intellectual tasks, including transferring knowledge between domains. It is distinguished from narrow AI, which is limited to specific tasks. Outperforming humans everywhere would go beyond general intelligence toward superintelligence.

Question 4Introduction to AI and AI-based systems

Which statement describes supervised learning?

  1. The model discovers hidden structure in data without labels
  2. The model follows hand-coded rules written by a domain expert
  3. The model learns a mapping from labelled input-output pairs Correct answer.
  4. The model learns by trial and error from rewards and penalties

Explanation

In supervised learning each training example is paired with the correct answer, and the model learns to map inputs to those targets. The labels are what make the learning 'supervised'. Unlabelled data and reward signals belong to other paradigms.

Question 5Introduction to AI and AI-based systems

A model is trained on historical property records to output an estimated sale price in euros for new properties. Which type of machine learning task is this?

  1. Regression Correct answer.
  2. Clustering
  3. Classification
  4. Reinforcement learning

Explanation

Regression predicts a continuous numeric value, such as a price or a temperature. Classification instead predicts a discrete category, and clustering groups unlabelled data. The euro amount is a continuous quantity, so the task is regression.

Question 6Introduction to AI and AI-based systems

Which learning type is appropriate when the available data carries no labels and the goal is to find groups of similar records?

  1. Unsupervised learning Correct answer.
  2. Supervised learning
  3. Rule-based learning
  4. Reinforcement learning

Explanation

Unsupervised learning looks for patterns, groupings or structure in data that has no target labels. Clustering is the classic example. Supervised methods require labels and reinforcement methods require a reward signal from an environment.

Question 7Introduction to AI and AI-based systems

In which learning paradigm does an agent learn by receiving a reward or penalty for the actions it takes in an environment?

  1. Supervised learning
  2. Unsupervised learning
  3. Symbolic reasoning
  4. Reinforcement learning Correct answer.

Explanation

Reinforcement learning trains an agent through interaction: actions that lead to reward are reinforced and actions that lead to penalty are discouraged. No labelled correct answer is supplied; the reward signal plays that role. This makes it suitable for sequential decision problems such as games and robot control.

Question 8Introduction to AI and AI-based systems

Deep learning is best characterized as:

  1. Learning that uses hand-crafted features and small datasets
  2. Learning performed only by decision trees
  3. Machine learning using neural networks with many layers Correct answer.
  4. Learning that requires no training data at all

Explanation

Deep learning uses artificial neural networks with multiple hidden layers, which lets the model learn feature representations directly from raw data. It still requires training data and is not tied to decision trees. Hand-crafted feature engineering is characteristic of traditional machine learning.

Question 9Introduction to AI and AI-based systems

Which pair correctly distinguishes training from inference?

  1. Training happens only in production; inference happens only in development
  2. Training applies the model to new inputs; inference builds the model from data
  3. Training builds the model from data; inference uses it on new inputs Correct answer.
  4. Training and inference are the same activity carried out at different times

Explanation

Training is the phase in which a model's parameters are estimated from data. Inference is the later phase in which the trained model processes new inputs to produce outputs. The two phases have different purposes and different risks for testing.

Question 10Introduction to AI and AI-based systems

A test runs the same image through a trained AI model twice and receives two slightly different confidence scores. What is the most likely explanation?

  1. The test environment has been configured incorrectly by the team
  2. The model has been trained with an excessive amount of data
  3. The model is probabilistic, so outputs may vary between runs Correct answer.
  4. The test data was modified between the two runs

Explanation

Many AI models are stochastic in nature, for example because of random initialisation, sampling or dropout, so identical inputs need not give byte-identical outputs. Testers must therefore allow for tolerances and repeat runs. A configuration error or changed data is a possible but less likely explanation given that only small score differences are seen.

Question 11Introduction to AI and AI-based systems

Why is defining expected results for an AI-based system harder than for a conventional system?

  1. Because testers are not permitted to execute the system
  2. Because the correct output for a given input is often unknown Correct answer.
  3. Because AI systems cannot be executed more than once
  4. Because AI systems always take longer to execute than other systems

Explanation

For many AI problems, such as image recognition or natural language understanding, there is no simple rule that yields the single expected answer for every input. This is the test oracle problem: the source of truth is difficult or expensive to establish. Testers therefore often rely on reference datasets, statistical criteria or domain experts.

Question 12Introduction to AI and AI-based systems

What mainly distinguishes an AI-based system from a conventional rule-based system?

  1. It does not need to be tested before it is released
  2. It cannot be used for business applications
  3. It always requires specialized hardware and a dedicated data centre to run
  4. Its behaviour is learned from data rather than explicitly coded rules Correct answer.

Explanation

In a conventional system the logic is written explicitly by developers, whereas an AI-based system derives its behaviour from patterns learned from training data. This is why data quality and representativeness become critical quality factors. Testing therefore focuses as much on the data and the learned model as on the code.

Question 13Introduction to AI and AI-based systems

A model produces accurate predictions, but engineers cannot explain how it reached a particular decision. Which consequence matters most for testing and defect analysis?

  1. The model cannot be executed more than once
  2. The training data will be deleted automatically after deployment
  3. The model will stop producing outputs after a period of time
  4. Root-cause analysis is harder because the decision logic is opaque Correct answer.

Explanation

When the internal decision logic cannot be inspected, tracing a wrong output back to its cause becomes difficult, which slows defect localisation and correction. This opacity is a key quality concern for AI-based systems, especially in regulated domains. It does not affect executability or data retention.

Question 14Introduction to AI and AI-based systems

A recruitment model trained on historical hiring data systematically rejects candidates from one group. What is the most likely root cause?

  1. The model was tested using too many test cases
  2. The model contains too many hidden layers in its network
  3. The training data reflected bias in past decisions Correct answer.
  4. The system lacks a user-friendly graphical interface

Explanation

A model learns the patterns present in its training data, so historical human bias can be reproduced and even amplified. This is a data quality issue rather than a testing-volume or user-interface issue. Detecting it requires analysing training data and monitoring outcomes across groups.

Question 15Introduction to AI and AI-based systems

Which combination best describes the main enablers of recent progress in AI?

  1. Stricter regulation, formal standards and more audits
  2. Cheaper displays, faster keyboards and larger screens
  3. Larger teams, more meetings and shorter release cycles
  4. Abundant data, better algorithms and more computing power Correct answer.

Explanation

Advances in machine learning have been driven largely by the availability of large datasets, improved algorithms and greatly increased computing capacity. Governance and process factors shape how AI is used but do not themselves enable the technical capability. Hardware peripherals and team size are not principal drivers.

Question 16Introduction to AI and AI-based systems

In an autonomous delivery robot, which component is responsible for interpreting camera images to detect obstacles?

  1. The actuator
  2. The perception component Correct answer.
  3. The communication module
  4. The battery management unit

Explanation

The perception component turns raw sensor signals, such as camera or lidar data, into an interpretation of the environment. The decision component then plans and the actuators carry out the movement. Power and communication modules support operation but do not interpret sensor data.

Question 17Introduction to AI and AI-based systems

An AI service is retrained every month on new data and redeployed. What is the most important testing implication?

  1. The test plan can be frozen because requirements stay the same
  2. Only the user interface needs to be retested after each update
  3. Testing can stop once the first model version has passed
  4. Regression tests must be repeated for each new model version Correct answer.

Explanation

Because the model changes with every retraining cycle, behaviour that previously passed can regress, so the existing test suites must be re-executed against each new version. Results are tied to a specific model and dataset version. This also makes version tracking and traceability essential.

Question 18Introduction to AI and AI-based systems

Which scenario is best suited to reinforcement learning?

  1. Grouping customers into segments without predefined labels
  2. Teaching an agent to play a game by rewarding winning moves Correct answer.
  3. Encoding a tax regulation as a set of if-then rules
  4. Predicting a customer's credit score from labelled past applications

Explanation

Reinforcement learning fits sequential decision problems where an agent acts in an environment and is guided by reward signals, as in game playing or robot navigation. Labelled prediction and unlabelled grouping belong to supervised and unsupervised learning. Encoding regulations as rules is a symbolic approach.

Question 19Introduction to AI and AI-based systems

An expert system that stores knowledge as if-then rules and uses an inference engine to draw conclusions is an example of:

  1. Unsupervised learning
  2. Reinforcement learning
  3. Deep learning
  4. Symbolic (knowledge-based) AI Correct answer.

Explanation

Symbolic AI represents knowledge explicitly, for example as rules or facts, and derives conclusions through logical inference. It does not learn patterns from data in the way machine learning does. Expert systems are the classic example of this approach.

Question 20Introduction to AI and AI-based systems

The observation that tasks once regarded as AI are later treated as ordinary computing is known as:

  1. The AI effect Correct answer.
  2. The oracle problem
  3. The Turing test
  4. The bias-variance trade-off

Explanation

The AI effect describes how, once a capability is solved and becomes commonplace, it stops being labelled as AI. This makes historical definitions of AI unstable and is why current definitions focus on techniques rather than on specific tasks. The other terms refer to distinct concepts in AI and testing.

Question 21Introduction to AI and AI-based systems

Which practice is most appropriate before an AI system is allowed to make final decisions on loan rejections?

  1. Removing all documentation to speed up processing
  2. Human review of the model's decisions in defined cases Correct answer.
  3. Retraining the model hourly on the same dataset
  4. Hiding the decision criteria from affected customers

Explanation

High-impact decisions call for meaningful human oversight, with people able to review, override or escalate model outputs. This mitigates harm from erroneous or unfair predictions and supports accountability. Removing documentation or hiding criteria undermines transparency and auditability.

Question 22Introduction to AI and AI-based systems

Why do AI-based systems often need statistical rather than exact expected results when they are tested?

  1. Because they never produce repeatable outputs of any kind
  2. Because quality is judged statistically over many inputs Correct answer.
  3. Because their source code is never available to testers
  4. Because testers are not allowed to read the requirements

Explanation

For many AI problems there is no single exact expected output per input, so accuracy, precision, recall or similar measures are evaluated across a representative dataset. Judgements are therefore made about aggregate behaviour and acceptable thresholds rather than individual exact values. This does not mean outputs are never repeatable at all.

Question 23Introduction to AI and AI-based systems

Which of the following is the clearest example of an AI-based system in everyday use?

  1. A calculator that divides two numbers
  2. A traffic light that cycles on a fixed timer
  3. A product recommendation engine on a shopping site Correct answer.
  4. A spreadsheet that sums a column of numbers

Explanation

A recommendation engine learns patterns from user behaviour and item data to suggest products, which is a typical AI use case. The other examples follow fixed, explicitly programmed rules with no learning component. Fixed arithmetic and timed cycling are conventional automation.

Question 24Introduction to AI and AI-based systems

How is machine learning related to artificial intelligence?

  1. Machine learning is a subset of AI Correct answer.
  2. They are two names for exactly the same thing
  3. AI is a subset of machine learning
  4. They are unrelated technical fields

Explanation

Artificial intelligence is the broad discipline of building systems that perform intelligent tasks, and machine learning is one family of techniques within it. Other AI approaches, such as symbolic reasoning, exist alongside machine learning. The two terms are therefore related by containment, not by identity.

Question 25Introduction to AI and AI-based systems

A software robot replays exactly the same recorded keystrokes every time it runs. Why is this typically not considered an AI-based system?

  1. Because it runs on a server rather than a desktop machine
  2. Because it runs much faster than a human can type
  3. Because it produces no log file during execution
  4. Because its behaviour is fixed in advance and it does not learn Correct answer.

Explanation

Such a robot follows a pre-recorded, deterministic script and does not derive behaviour from data, which is the defining feature of an AI-based system. Automation of this kind is conventional rather than intelligent. Deployment platform, speed and logging have no bearing on the classification.

Question 26Introduction to AI and AI-based systems

Which statement best describes a narrow AI system?

  1. It matches human general reasoning across all domains and tasks.
  2. It relies only on hand-coded rules and requires no data.
  3. It is built for one specific task or a small set of related tasks. Correct answer.
  4. It can transfer any learned skill to any new domain without retraining.

Explanation

Narrow AI, also called weak AI, is designed to operate within a bounded problem area and does not possess general reasoning beyond that area. Its competence comes from task-specific data, rules, or training rather than open-ended transfer.

Question 27Introduction to AI and AI-based systems

Within a typical AI-based system architecture, which component converts raw sensor input into structured representations that later stages can use?

  1. The knowledge base, which stores facts and rules
  2. The learning component, which updates model parameters
  3. The action component, which executes the selected response
  4. The perception component, which interprets sensory data Correct answer.

Explanation

Perception is the part of an AI system that interprets raw inputs such as images, sound, or text and turns them into a usable internal representation. Reasoning or decision-making then works on that representation, and action components carry out the resulting choice.

Question 28Introduction to AI and AI-based systems

Which statement best characterizes symbolic AI as used in classic rule-based systems?

  1. Knowledge is encoded only in raw pixel values from training images.
  2. Knowledge is explicit as symbols and rules that people can inspect. Correct answer.
  3. Knowledge emerges from reward-driven trial and error in an environment.
  4. Knowledge is implicit in numeric weights learned from large data sets.

Explanation

Symbolic AI represents knowledge as explicit symbols, facts, and rules, and an inference engine manipulates them to derive conclusions. Because the representation is human-readable, the reasoning can often be traced and audited.

Question 29Introduction to AI and AI-based systems

A deployed AI-based system performs well in testing but its accuracy drops sharply after several months in operation. Which characteristic of AI-based systems best explains this pattern?

  1. Its hardware clock drifts and changes the timing of its outputs.
  2. It relies on learned patterns that may stop matching current data. Correct answer.
  3. Its algorithm is fixed and cannot be changed after the system is released.
  4. Its decision rules were compiled to machine code for faster execution.

Explanation

AI-based systems often learn statistical patterns from data, so their behaviour depends on the relationship between training data and the current operating environment. When that relationship changes, performance can degrade even though the software itself has not been modified.

Question 30Quality characteristics of AI-based systems (aligned with ISO/IEC 25059)

Within the SQuaRE series (ISO/IEC 25000), which document provides the quality model specifically for AI systems?

  1. ISO/IEC 25059 Correct answer.
  2. ISO/IEC 25000
  3. ISO/IEC 25020
  4. ISO/IEC 25010

Explanation

ISO/IEC 25059 is the SQuaRE extension that defines a quality model for AI systems. ISO/IEC 25010 supplies the general product quality model that 25059 builds on, while 25020 addresses quality measurement and 25000 is the overall SQuaRE guide.

Question 31Quality characteristics of AI-based systems (aligned with ISO/IEC 25059)

How does ISO/IEC 25059 relate to the general product quality model of ISO/IEC 25010?

  1. It extends the general model with AI-specific characteristics and sub-characteristics. Correct answer.
  2. It restricts the general model to embedded software only.
  3. It defines process quality instead of product quality.
  4. It replaces the general model with a data-quality-only model.

Explanation

ISO/IEC 25059 takes the ISO/IEC 25010 characteristics and adapts them to AI systems, adding AI-specific characteristics and sub-characteristics where the general model is insufficient. It is an application-specific extension, not a replacement or a restriction.

Question 32Quality characteristics of AI-based systems (aligned with ISO/IEC 25059)

An AI system must produce outputs that are accurate with respect to the required ground truth. Which quality characteristic does this primarily describe?

  1. Maintainability
  2. Performance efficiency
  3. Functional suitability Correct answer.
  4. Compatibility

Explanation

Functional suitability covers the degree to which a product provides functions that meet stated and implied needs, including functional correctness of results. Accuracy against ground truth is therefore a functional suitability concern.

Question 33Quality characteristics of AI-based systems (aligned with ISO/IEC 25059)

A classifier for support tickets was trained on data covering only five of the twelve categories the business uses. Which functional suitability sub-characteristic is most directly violated?

  1. Interaction capability
  2. Functional completeness Correct answer.
  3. Functional appropriateness
  4. Functional correctness

Explanation

Functional completeness is the degree to which the set of functions covers all specified tasks and intended user objectives; missing categories means the required scope is not covered. Functional correctness instead concerns the accuracy of results for the functions that do exist.

Question 34Quality characteristics of AI-based systems (aligned with ISO/IEC 25059)

Which quality characteristic covers response time and throughput of an AI service under stated conditions?

  1. Reliability
  2. Flexibility
  3. Interaction capability
  4. Performance efficiency Correct answer.

Explanation

Performance efficiency addresses the performance of a system relative to the amount of resources used under stated conditions, including time behaviour such as response time and throughput.

Question 35Quality characteristics of AI-based systems (aligned with ISO/IEC 25059)

A deployed model meets its accuracy targets but consumes excessive memory and processor time per request, raising operating cost. Which characteristic is most affected?

  1. Security
  2. Maintainability
  3. Functional suitability
  4. Performance efficiency Correct answer.

Explanation

Resource utilization is a sub-characteristic of performance efficiency, covering the amounts and types of resources used when performing its functions. Accuracy being met means functional suitability is not the main concern here.

Question 36Quality characteristics of AI-based systems (aligned with ISO/IEC 25059)

An AI component must exchange prediction requests and metadata with an existing CRM platform through a shared interface and data format. Which characteristic is being exercised?

  1. Compatibility Correct answer.
  2. Flexibility
  3. Safety
  4. Reliability

Explanation

Compatibility includes interoperability, the degree to which a system can exchange information with other systems and use the information exchanged while sharing the same environment. A shared interface and format is a typical interoperability concern.

Question 37Quality characteristics of AI-based systems (aligned with ISO/IEC 25059)

ISO/IEC 25010:2023 replaced the term 'usability' with which characteristic name that the AI quality model retains?

  1. Learnability
  2. Interaction capability Correct answer.
  3. Accessibility
  4. Operability

Explanation

The revised product quality model renames usability as interaction capability, emphasising effective, efficient and satisfying interaction with the system. Accessibility, operability and learnability are lower-level concerns rather than the top-level characteristic.

Question 38Quality characteristics of AI-based systems (aligned with ISO/IEC 25059)

A recommendation model returns errors whenever its external feature store responds slowly; it must keep serving fallback recommendations rather than failing. Which characteristic addresses this need?

  1. Reliability Correct answer.
  2. Robustness
  3. Maintainability
  4. Performance efficiency

Explanation

Fault tolerance, a sub-characteristic of reliability, is the degree to which a system operates as intended despite hardware or software faults in its environment. Robustness instead concerns handling invalid or perturbed inputs rather than failing components.

Question 39Quality characteristics of AI-based systems (aligned with ISO/IEC 25059)

After a failed deployment of a retrained model, the system must restore the previous model version and its data within one hour. Which sub-characteristic applies?

  1. Recoverability Correct answer.
  2. Fault tolerance
  3. Maturity
  4. Availability

Explanation

Recoverability is the degree to which, after an interruption or failure, a system can recover the data directly affected and re-establish the desired state. Availability and fault tolerance concern normal operation rather than restoring state afterwards.

Question 40Quality characteristics of AI-based systems (aligned with ISO/IEC 25059)

Which characteristic addresses protection of stored training data so that unauthorized parties cannot read it?

  1. Maintainability
  2. Functional suitability
  3. Security Correct answer.
  4. Compatibility

Explanation

Security covers protection of information and data so that persons or systems have the degree of data access appropriate to their types and levels of authorization, including confidentiality.

Question 41Quality characteristics of AI-based systems (aligned with ISO/IEC 25059)

A team wants to replace the feature-engineering stage of its machine learning pipeline without affecting the model-serving component. Which maintainability sub-characteristic is most relevant?

  1. Modifiability Correct answer.
  2. Reusability
  3. Analysability
  4. Testability

Explanation

Modifiability is the degree to which a system can be effectively and efficiently modified without introducing defects, and it is enabled by modular structure. Testability and analysability support change but describe testing and diagnosis rather than the change itself.

Question 42Quality characteristics of AI-based systems (aligned with ISO/IEC 25059)

Why is testability particularly challenging for continuously learning AI systems?

  1. Model code is usually too large for any test tool to compile
  2. Their interfaces are generally proprietary and undocumented
  3. Retraining changes behaviour, so test oracles must be re-established Correct answer.
  4. No expected results can ever be defined for statistical output

Explanation

Testability is the degree to which test criteria can be established and tests performed to determine whether those criteria are met. When a model's behaviour shifts with each retraining, previously valid expected results and thresholds must be re-derived.

Question 43Quality characteristics of AI-based systems (aligned with ISO/IEC 25059)

A fraud model must be retrainable and redeployed for a newly observed fraud pattern without changing the surrounding architecture. Which characteristic is primarily involved?

  1. Reliability
  2. Interaction capability
  3. Flexibility Correct answer.
  4. Security

Explanation

Flexibility (formerly portability) includes adaptability, the degree to which a system can be adapted for different or evolving environments and requirements without additional measures. Reacting to a new fraud pattern is an adaptation requirement.

Question 44Quality characteristics of AI-based systems (aligned with ISO/IEC 25059)

An AI inference service must keep meeting its response-time target when request volume triples. Which sub-characteristic is this?

  1. Scalability Correct answer.
  2. Capacity
  3. Throughput
  4. Elasticity

Explanation

Scalability is the degree to which a system can handle growing or shrinking workloads, or adapt to changes in demand, without degrading agreed performance levels. Capacity and throughput are related measures rather than the modelled sub-characteristic.

Question 45Quality characteristics of AI-based systems (aligned with ISO/IEC 25059)

An AI component steers a physical robot arm, and a misclassification could injure a nearby worker. Which characteristic is most directly concerned with preventing unacceptable harm?

  1. Robustness
  2. Reliability
  3. Safety Correct answer.
  4. Security

Explanation

Safety is the degree to which a system avoids states that could cause harm to people, property or the environment under specified conditions. Robustness and reliability contribute to safety, but the harm-oriented characteristic is safety itself.

Question 46Quality characteristics of AI-based systems (aligned with ISO/IEC 25059)

Which characteristic describes how well an AI system maintains acceptable behaviour when its inputs are noisy, incomplete or outside the training distribution?

  1. Robustness Correct answer.
  2. Flexibility
  3. Maturity
  4. Analysability

Explanation

Robustness is the degree to which a system maintains correct or acceptable behaviour when confronted with invalid inputs or stressful environmental conditions, including out-of-distribution data.

Question 47Quality characteristics of AI-based systems (aligned with ISO/IEC 25059)

A team must distinguish robustness from reliability for an AI system. Which statement is the best distinction?

  1. Robustness applies only in testing; reliability applies only in production
  2. Robustness concerns only hardware faults; reliability concerns only data errors
  3. Robustness concerns behaviour with invalid or perturbed input; reliability concerns consistent operation over time Correct answer.
  4. Robustness is a sub-characteristic of security; reliability is a sub-characteristic of safety

Explanation

Robustness captures whether a system still behaves acceptably when inputs are invalid, unexpected or out of distribution. Reliability concerns the system operating consistently over a period of use, including maturity, availability, fault tolerance and recoverability.

Question 48Quality characteristics of AI-based systems (aligned with ISO/IEC 25059)

Users must be able to understand the reasons behind a credit decision produced by a model. Which AI quality characteristic covers this?

  1. Maintainability
  2. Functional correctness
  3. Transparency Correct answer.
  4. Interaction capability

Explanation

Transparency, including explainability, is the degree to which the behaviour and outputs of an AI system can be understood and explained to relevant stakeholders. It goes beyond correct results to the ability to justify them.

Question 49Quality characteristics of AI-based systems (aligned with ISO/IEC 25059)

According to ISO/IEC 25059, the evaluation of an AI system's quality characteristics depends most on which of the following?

  1. The context of use and stakeholder needs Correct answer.
  2. The number of layers in the neural network
  3. The licence under which the model is distributed
  4. The programming language used to implement the model

Explanation

Quality characteristics are evaluated relative to a defined context of use and the requirements of the stakeholders concerned. The same AI component can meet or miss quality targets depending on where and how it is used.

Question 50Quality characteristics of AI-based systems (aligned with ISO/IEC 25059)

A spoofing attack on a vehicle sensor could cause an autonomous delivery robot to brake suddenly in a crowded area. Which pair of characteristics is most relevant?

  1. Compatibility and flexibility
  2. Interaction capability and learnability
  3. Security and safety Correct answer.
  4. Maintainability and reusability

Explanation

The attack vector is a security concern because protection against unauthorized manipulation is compromised, while the potential for physical harm to people makes it a safety concern. AI quality analysis often links these two characteristics in such scenarios.