CT-AI 시험 - ISTQB실제시험문제와 답 - 40문항

Question No : 1

You have been developing test automation for an e-commerce system. One of the problems you are seeing is that object recognition in the GUI is having frequent failures. You have determined this is because the developers are changing the identifiers when they make code updates.
How could AI help make the automation more reliable?

A.It could identify the objects multiple ways and then determine the most commonly used and stable identification for each object
B.It could modify the automation code to ignore unrecognizable objects to avoid failures
C.It could dynamically name the objects, altering the source code, so the object names will match the object names used in the automation
D.It could generate a model that will anticipate developer changes and pre-alter the test automation code accordingly

정답:
Explanation:
The syllabus discusses using AI-based tools to reduce GUI test brittleness:
"AI can be used to reduce the brittleness of this approach, by employing AI-based tools to identify the correct objects using various criteria (e.g., XPath, label, id, class, X/Y coordinates), and to choose the historically most stable identification criteria."
(Reference: ISTQB CT-AI Syllabus v1.0, Section 11.6.1)

Question No : 2

Which of the following is a technique used in machine learning?

A.Decision trees
B.Equivalence partitioning
C.Boundary value analysis
D.Decision tables

정답:
Explanation:
Decision trees are a foundational algorithm used in supervised machine learning. The syllabus describes:
"A decision tree is a tree-like ML model whose nodes represent decisions and whose branches represent possible outcomes."
(Reference: ISTQB CT-AI Syllabus v1.0, Section 3.4)

Question No : 3

An engine manufacturing facility wants to apply machine learning to detect faulty bolts.
Which of the following would result in bias in the model?

A.Selecting training data purposely excluding specific faulty conditions
B.Selecting training data by purposely including all known faulty conditions
C.Selecting testing data from a different dataset than the training dataset
D.Selecting testing data from a boat manufacturer's bolt longevity data

정답:
Explanation:
The syllabus defines bias as:
"Bias is the systematic difference in treatment of certain objects, people or groups in comparison to others."
It also discusses:
"Sample bias can occur if the data used for training the model does not represent the operational environment, or if some relevant faulty conditions are excluded deliberately."
(Reference: ISTQB CT-AI Syllabus v1.0, Section 7.6 and 8.3)

Question No : 4

Which of the following is an example of an input change where it would be expected that the AI system should be able to adapt?

A.It has been trained to recognize cats and is given an image of a dog
B.It has been trained to recognize human faces at a particular resolution and it is given a human face image captured with a higher resolution
C.It has been trained to analyze mathematical models and is given a set of landscape pictures to classify
D.It has been trained to analyze customer buying trend data and is given information on supplier cost data

정답:
Explanation:
The syllabus explains that input changes that arein the same domainas what was used for training are expected to be handled with adaptability:
"Adaptability refers to the ability of a system to adjust its behavior in response to changes in its environment or inputs. This includes changes to the inputs which are still within the expected operational range of the system, such as resolution changes in images or sensor data."
(Reference: ISTQB CT-AI Syllabus v1.0, Section 7.6 and 8.2)

Question No : 5

The stakeholders of a machine learning model have confirmed that they understand the objective and 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.Prepare and pre-process the data that will be used to train and test the model
B.Tune the machine learning algorithm based on objectives and business priorities
C.Agree on defined acceptance criteria for the machine learning model
D.Evaluate the selection of the framework and the model

정답:
Explanation:
The ML workflow typically involves iterative steps, beginning with data preparation once the model and framework are selected.
The syllabus explains:
"The steps shown in Figure 1 (the ML workflow) do not include the integration of the ML model with the non-ML parts of the overall system. Typically, ML models cannot be deployed in isolation and need to be integrated with the non-ML parts... The next step would be data preparation as part of the ML workflow to provide input data to support training by an ML algorithm or prediction by an ML model."
(Reference: ISTQB CT-AI Syllabus v1.0, Sections 3.2 & 4.1)

Question No : 6

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

A.General AI
B.Narrow AI
C.Super AI
D.Conventional AI

정답:
Explanation:
According to the syllabus, conventional AIsystems are limited to specific, pre-defined tasks and do not have generalized intelligence:
"Conventional AI systems are limited in their scope and typically only perform specific tasks within the domain for which they have been designed. They do not exhibit general AI behavior."
(Reference: ISTQB CT-AI Syllabus v1.0, Section 1.2)

Question No : 7

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 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

정답:
Explanation:
In the syllabus, the evolution characteristic 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)

Question No : 8

Before deployment of an AI-based system, a developer is expected to demonstrate in a test environment how decisions are made.
Which of the following characteristics does decision making fall under?

A.Explainability
B.Autonomy
C.Self-learning
D.Non-determinism

정답:
Explanation:
The syllabus definesexplainabilityas the ability to understand how the AI-based system comes up with a particular result:
"Explainability is considered to be the ease with which users can determine how the AI-based system comes up with a particular result."
(Reference: ISTQB CT-AI Syllabus v1.0, Section 2.7)

Question No : 9

An e-commerce developer built an application for automatic classification of online products in order to allow customers to select products faster. The goal is to provide more relevant products to the user based on prior purchases.
Which of the following factors is necessary for a supervised machine learning algorithm to be successful?

A.Labeling the data correctly
B.Minimizing the amount of time spent training the algorithm
C.Selecting the correct data pipeline for the ML training
D.Grouping similar products together before feeding them into the algorithm

정답:
Explanation:
The syllabus explains that supervised learning requires correctly labeled data so the algorithm can learn the relationship between input features and output labels:
"In supervised learning, the algorithm creates the ML model from labeled data during the training phase. The labeled data is used to infer the relationship between the input data and output labels."
(Reference: ISTQB CT-AI Syllabus v1.0, Section 3.1.1)

Question No : 10

A beer company is trying to understand how much recognition its logo has in the market. It plans to do that by monitoring images on various social media platforms using a pre-trained neural network for logo detection. This particular model has been trained by looking for words, as well as matching colors on social media images. The company logo has a big word across the middle with a bold blue and 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

정답:
Explanation:
According to the syllabus, pre-trained models often inherit biases and limitations from the data and processes used in their original training, which may not align with the new use case. Specifically, the syllabus states:
"When using a pre-trained model, the training data and process cannot be fully controlled or known by the user of the model. As a result, the model can inherit biases or inaccuracies that were part of its original development and training process."
(Reference: ISTQB CT-AI Syllabus v1.0, Section 1.8.3)

Question No : 11

“BioSearch” is creating an Al model used for predicting cancer occurrence via examining X-Ray
images. The accuracy of the model in isolation has been found to be good. However, the users of the model started complaining of the poor quality of results, especially inability to detect real cancer cases, when put to practice in the diagnosis lab, leading to stopping of the usage of the model.
A testing expert was called in to find the deficiencies in the test planning which led to the above scenario.
Which ONE of the following options would you expect to MOST likely be the reason to be discovered by the test expert? SELECT ONE OPTION

A.A lack of similarity between the training and testing data.
B.The input data has not been tested for quality prior to use for testing.
C.A lack of focus on choosing the right functional-performance metrics.
D.A lack of focus on non-functional requirements testing.

정답:
Explanation:
The question asks which deficiency is most likely to be discovered by the test expert given the scenario of poor real-world performance despite good isolated accuracy.
A lack of similarity between the training and testing data (A): This is a common issue in ML where the model performs well on training data but poorly on real-world data due to a lack of representativeness in the training data. This leads to poor generalization to new, unseen data.
The input data has not been tested for quality prior to use for testing (B): While data quality is important, this option is less likely to be the primary reason for the described issue compared to the representativeness of training data.
A lack of focus on choosing the right functional-performance metrics (C): Proper metrics are crucial, but the issue described seems more related to the data mismatch rather than metric selection.
A lack of focus on non-functional requirements testing (D): Non-functional requirements are important, but the scenario specifically mentions issues with detecting real cancer cases, pointing more towards data issues.
: ISTQB CT-AI Syllabus Section 4.2 on Training, Validation, and Test Datasets emphasizes the importance of using representative datasets to ensure the model generalizes well to real-world data.
Sample Exam Questions document, Question #40 addresses issues related to data representativeness and model generalization.

Question No : 12

A system was developed for screening the X-rays of patients for potential malignancy detection (skin cancer). A workflow system has been developed to screen multiple cancers by using several individually trained ML models chained together in the workflow.
Testing the pipeline could involve multiple kind of tests (I - III):
I. Pairwise testing of combinations
II. Testing each individual model for accuracy
III. A/B testing of different sequences of models
Which ONE of the following options contains the kinds of tests that would be MOST APPROPRIATE to include in the strategy for optimal detection? SELECT ONE OPTION

A.Only III
B.I and II
C.I and III
D.Only II

정답:
Explanation:
The question asks which combination of tests would be most appropriate to include in the strategy for optimal detection in a workflow system using multiple ML models.
Pairwise testing of combinations (I): This method is useful for testing interactions between different components in the workflow to ensure they work well together, identifying potential issues in the integration.
Testing each individual model for accuracy (II): Ensuring that each model in the workflow performs accurately on its own is crucial before integrating them into a combined workflow.
A/B testing of different sequences of models (III): This involves comparing different sequences to determine which configuration yields the best results. While useful, it might not be as fundamental as pairwise and individual accuracy testing in the initial stages.
: ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing and Section 9.3 on Testing ML Models emphasize the importance of testing interactions and individual model accuracy in complex ML workflows.

Question No : 13

Which ONE of the following characteristics is the least likely to cause safety related issues for an Al system? SELECT ONE OPTION

A.Non-determinism
B.Robustness
C.High complexity
D.Self-learning

정답:
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.

Question No : 14

Which ONE of the following tests is LEAST likely to be performed during the ML model testing phase? SELECT ONE OPTION

A.Testing the accuracy of the classification model.
B.Testing the API of the service powered by the ML model.
C.Testing the speed of the training of the model.
D.Testing the speed of the prediction by the model.

정답:
Explanation:
The question asks which test is least likely to be performed during the ML model testing phase.
Let's consider each option:
Testing the accuracy of the classification model (A): Accuracy testing is a fundamental part of the ML model testing phase. It ensures that the model correctly classifies the data as intended and meets the required performance metrics.
Testing the API of the service powered by the ML model (B): Testing the API is crucial, especially if the ML model is deployed as part of a service. This ensures that the service integrates well with other systems and that the API performs as expected.
Testing the speed of the training of the model (C): This is least likely to be part of the ML model testing phase. The speed of training is more relevant during the development phase when optimizing and tuning the model. During testing, the focus is more on the model's performance and behavior rather than how quickly it was trained.
Testing the speed of the prediction by the model (D): Testing the speed of prediction is important to ensure that the model meets performance requirements in a production environment, especially for real-time applications.
: ISTQB CT-AI Syllabus Section 3.2 on ML Workflow and Section 5 on ML Functional Performance Metrics discuss the focus of testing during the model testing phase, which includes accuracy and prediction speed but not the training speed.

Question No : 15

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.Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.
C.Individual bias at the neuron level, 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.

정답:
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=∑(wiai)+b, where wiw_iwiare the weights, aia_iaiare 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.
Reference: 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).

ISTQB CT-AI 시험