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CT-AI Dumps Full Questions - Exam Study Guide
NEW QUESTION # 78
Which challenge to testing self-learning systems puts you at risk of a data attack?
- A. Inadequate specification of the operating environment
- B. Complex test environment
- C. Insufficient testing time
- D. Unexpected changes
Answer: D
Explanation:
The ISTQB CT-AI syllabus describes thatself-learning systems continuously adjust their behaviorduring operation as new data arrives. Section4.1 - Challenges of Testing AI-Based Systemshighlights that such systems are vulnerable todata attacks, particularly through adversarial inputs, poisoning, or malicious drift. The risk arises because unexpected changes in the input distribution may alter the learned model in harmful ways. Option D - Unexpected changes corresponds directly to this syllabus-defined risk.
NEW QUESTION # 79
Which ONE of the following options does NOT describe a challenge for acquiring test data in ML systems?
- A. Data for the use case is being generated at a fast pace.
- B. Test data being sourced from public sources.
- C. Nature of data constantly changes with lime.
- D. Compliance needs require proper care to be taken of input personal data.
Answer: A
Explanation:
Challenges for Acquiring Test Data in ML Systems: Compliance needs, the changing nature of data over time, and sourcing data from public sources are significant challenges. Data being generated quickly is generally not a challenge; it can actually be beneficial as it provides more data for training and testing.
NEW QUESTION # 80
Which ONE of the following options does NOT describe an Al technology related characteristic which differentiates Al test environments from other test environments?
- A. The challenge of providing explainability to the decisions made by the system.
- B. Challenges in the creation of scenarios of human handover for autonomous systems.
- C. Challenges resulting from low accuracy of the models.
- D. The challenge of mimicking undefined scenarios generated due to self-learning
Answer: B
Explanation:
AI test environments have several unique characteristics that differentiate them from traditional test environments. Let's evaluate each option:
A). Challenges resulting from low accuracy of the models.
Low accuracy is a common challenge in AI systems, especially during initial development and training phases. Ensuring the model performs accurately in varied and unpredictable scenarios is a critical aspect of AI testing.
B). The challenge of mimicking undefined scenarios generated due to self-learning.
AI systems, particularly those that involve machine learning, can generate undefined or unexpected scenarios due to their self-learning capabilities. Mimicking and testing these scenarios is a unique challenge in AI environments.
C). The challenge of providing explainability to the decisions made by the system.
Explainability, or the ability to understand and articulate how an AI system arrives at its decisions, is a significant and unique challenge in AI testing. This is crucial for trust and transparency in AI systems.
D). Challenges in the creation of scenarios of human handover for autonomous systems.
While important, the creation of scenarios for human handover in autonomous systems is not a characteristic unique to AI test environments. It is more related to the operational and deployment challenges of autonomous systems rather than the intrinsic technology-related characteristics of AI .
Given the above points, option D is the correct answer because it describes a challenge related to operational deployment rather than a technology-related characteristic unique to AI test environments.
NEW QUESTION # 81
Which option describes a reasonable application of AIB testing for a self-learning system after it has changed its behavior due to user input?
Choose ONE option (1 out of 4)
- A. Generating test cases for the system before and after the change, since neither has a test oracle
- B. Comparing outputs before and after the change using different inputs
- C. Comparing outputs of a non-self-learning system with those of the changed self-learning system
- D. Comparing outputs before and after the change using identical inputs
Answer: D
Explanation:
According to Section4.6 - AI Behaviour Testing (AIB Testing)of the ISTQB CT-AI syllabus, AIB testing is used to evaluate changes in the functional behavior of self-learning systems. The core principle iscomparing pre-change and post-change model behavior using the same test inputs, so that any difference in outputs can be attributed to the model's learning and not to differences in input data. This directly corresponds to OptionC.
Option A is incorrect because the absence of a test oracle does not justify generating new test cases; AIB relies onreusing identical inputsto detect behavioral drift. Option B is invalid because using different inputs prevents meaningful comparison. Option D is incorrect because comparing with an unrelated non-self- learning system does not allow evaluation of the same model's behavioral evolution.
Thus, OptionCaccurately represents the correct application of AIB testing: assessing model behavior changes by running identical test inputs before and after learning updates.
NEW QUESTION # 82
A wildlife conservation group would like to use a neural network to classify images of different animals. The algorithm is going to be used on a social media platform to automatically pick out pictures of the chosen animal of the month. This month's animal is set to be a wolf. The test team has already observed that the algorithm could classify a picture of a dog as being a wolf because of the similar characteristics between dogs and wolves. To handle such instances, the team is planning to train the model with additional images of wolves and dogs so that the model is able to better differentiate between the two. What test method should you use to verify that the model has improved after the additional training?
- A. Pairwise testing using combinatorics to look at a long list of photo parameters.
- B. Metamorphic testing because the application domain is not clearly understood at this point.
- C. Back-to-back testing using the version of the model before training and the new version of the model after being trained with additional images.
- D. Adversarial testing to verify that no incorrect images have been used in the training.
Answer: C
Explanation:
The syllabus defines back-to-back testing as a method to compare a modified AI system to the previous version, which is ideal in this scenario:
"Back-to-back testing is performed by comparing the outputs of two systems that are supposed to provide the same outputs, one being a known and trusted system and the other being the test system. This approach can be used to test ML systems after re-training to verify that improvements have not introduced regressions."
NEW QUESTION # 83
You are developing a "flower" ML model... Which of the following describes an objection that you can NEGLECT in your risk assessment?
Choose ONE option (1 out of 4)
- A. The classification behavior of the "flower" ML model is more difficult to understand when it is reused compared to when it is developed from scratch.
- B. The probability of misclassification of the ML model "flower" is higher when it is reused than when it is developed from scratch.
- C. The possible inputs for the 'leaf' and 'flower' ML models are so different that reuse has few advantages over new development.
- D. The possible outputs of the "leaf" and "flower" ML models are so different that reuse has few advantages over new development.
Answer: D
Explanation:
The ISTQB CT-AI syllabus explains that reusing pre-trained models is strongly related tosimilarity between the original task and the new task. Section1.8 - Pre-trained Models and Transfer Learningstates that reuse is effective when the new task is similar to the original one, such as adapting a cat-classifier to classify dog breeds. The syllabus warns about risks related toinput differences,data preparation inconsistencies, inherited shortcomings, andexplainability issues. These are legitimate objections (matching options A, B, and C) because large differences in image inputs or patterns can undermine transfer learning; misclassification risk can increase; and explainability often decreases when reusing pre-trained models .
However,output differences are NOT a valid concernhere. Both the leaf-based and flower-based ML models classifythe same plant species, meaning theiroutputs are identical. The syllabus does not identify output mismatch as a transfer-learning risk. Real risks concerninputs,bias inheritance,model transparency, andtraining differences-not output labels. Therefore, OptionDdescribes an objection that can be safely neglected, because output classes are the same and do not hinder reuse.
NEW QUESTION # 84
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. Selecting the correct data pipeline for the ML training
- B. Minimizing the amount of time spent training the algorithm
- C. Grouping similar products together before feeding them into the algorithm
- D. Labeling the data correctly
Answer: D
Explanation:
Supervised machine learning requires correctly labeled data to train an effective model. The learning process relies on input-output mappings where each training example consists of an input (features) and a correctly labeled output (target variable). Incorrect labeling can significantly degrade model performance.
* Supervised Learning Process
* The algorithm learns from labeled data, mapping inputs to correct outputs during training.
* If labels are incorrect, the model will learn incorrect relationships and produce unreliable predictions.
* Quality of Training Data
* The accuracy of any supervised ML model ishighly dependent on the quality of labels.
* Poorly labeled data leads to mislabeled training sets, resulting inbiased or underperforming models.
* Error Minimization and Model Accuracy
* Incorrectly labeled data affects theconfusion matrix, reducing precision, recall, and accuracy.
* It leads to overfitting or underfitting, which decreases the model's ability to generalize.
* Industry Standard Practices
* Many AI development teams spend a significant amount of time ondata annotation and quality controlto ensure high-quality labeled datasets.
* (B) Minimizing the amount of time spent training the algorithm#(Incorrect)
* While reducing training time is important for efficiency, the quality of training is more critical. A well-trained model takes time to process large datasets and optimize its parameters.
* (C) Selecting the correct data pipeline for the ML training#(Incorrect)
* A good data pipeline helps, butit does not directly impact learning successas much as labeling does.Even a well-optimized pipeline cannot fix incorrect labels.
* (D) Grouping similar products together before feeding them into the algorithm#(Incorrect)
* This describesclustering, which is anunsupervised learning technique. Supervised learningrequires labeled examples, not just grouping of data.
* Labeled data is necessary for supervised learning."For supervised learning, it is necessary to have properly labeled data."
* Data labeling errors can impact performance."Supervised learning assumes that the data is correctly labeled by the data annotators.However, it is rare in practice for all items in a dataset to be labeled correctly." Why Labeling is Critical?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, ascorrectly labeled data is essential for supervised machine learning success.
NEW QUESTION # 85
A neural network has been designed and created to assist day-traders improve efficiency when buying and selling commodities in a rapidly changing market. Suppose the test team executes a test on the neural network where each neuron is examined. For this network the shortest path indicates a buy, and it will only occur when the one-day predicted value of the commodity is greater than the spot price by 0.75%. The neurons are stimulated by entering commodity prices and testers verify that they activate only when the future value exceeds the spot price by at least 0.75%.
Which of the following statements BEST explains the type of coverage being tested on the neural network?
- A. Threshold coverage
- B. Neuron coverage
- C. Value-change coverage
- D. Sign-change coverage
Answer: A
Explanation:
Threshold coverageis a specific type of coverage measure used in neural network testing. It ensures that each neuron in the network achieves an activation value greater than a specified threshold. This is particularly relevant to the scenario described, where testers verify that neurons activate only when the future value of the commodity exceeds the spot price by at least0.75%.
* Threshold-based activation:The test case in the question isexplicitly verifying whether neurons activate only when a certain threshold (0.75%) is exceeded.This aligns perfectly with the definition ofthreshold coverage.
* Common in Neural Network Testing:Threshold coverage is used to measurewhether each neuron in a neural network reaches a specified activation value, ensuring that the neural network behaves as expected when exposed to different test inputs.
* Precedent in Research:TheDeepXplore frameworkused a threshold of0.75%to identify incorrect behaviors in neural networks, making this coverage criterion well-documented in AI testing research.
* (B) Neuron Coverage#
* Neuron coverageonly checks whether a neuron activates (non-zero value)at some point during testing. It does not consider specific activation thresholds, making it less precise for this scenario.
* (C) Sign-Change Coverage#
* This coverage measures whether each neuron exhibitsboth positive and negative activation values, which isnot relevant to the given scenario(where activation only matters when exceeding a specific threshold).
* (D) Value-Change Coverage#
* This coverage requires each neuron to producetwo activation values that differ by a chosen threshold, but the question focuses onwhether activation occurs beyond a fixed threshold, not changes in activation values.
* Threshold coverage ensures that neurons exceed a given activation threshold"Full threshold coverage requires that each neuron in the neural network achieves an activation value greater than a specified threshold. The researchers who created the DeepXplore framework suggested neuron coverage should be measured based on an activation value exceeding a threshold, changing based on the situation." Why is Threshold Coverage Correct?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, asthreshold coverage ensures the neural network's activation is correctly evaluated based on the required condition (0.75%).
NEW QUESTION # 86
Which ONE of the following options is a technology used to implement AI?
- A. Reinforcement learning
- B. Autonomy
- C. Genetic algorithms
- D. Classification
Answer: C
Explanation:
Genetic algorithms are a technology used in AI, particularly in optimization problems and machine learning models. They are inspired by the process of natural selection and evolve solutions over generations.
NEW QUESTION # 87
Which ONE of the following statements BEST describes a testing challenge that specifically applied to a self-learning system?
- A. External data sources might be required to ensure that the system is unbiased
- B. In can be difficult to explain the link between test inputs and outputs
- C. When systems change themselves the results of previously passing tests may change
- D. It is necessary to test whether the system will relinquish control to a human at the right time
Answer: C
Explanation:
A key challenge in testing self-learning systems is that, as the system learns and adapts over time, the results of previously passing tests may change. This is because the system's behavior evolves as it learns from new data, potentially altering how it responds to test inputs. This dynamic nature of self-learning systems makes it challenging to maintain consistent and reliable test results.
NEW QUESTION # 88
Which of the following statements about explainable AI is correct?
- A. Interpretability refers to how easily users can determine whether the result provided by the AI- based system is correct
- B. According to The Royal Society, one reason for explainable AI is to eliminate the need for risk and vulnerability assessments
- C. Explainability refers to how easily the algorithms and training data needed to create the model can be determined
- D. According to The Royal Society, one reason for explainable AI is to increase user confidence in the system
Answer: D
Explanation:
Section2.10 - Explainability and Transparency of the ISTQB CT-AI syllabus describes explain able AI as the ability of a system to provide human-understandable insight into its decisions. The syllabus references The Royal Society's reportas a foundational source explaining why explainability is important. Among the stated motivations is the need toincrease user trust and confidencein AI systems by making their decisions understandable and justifiable. Therefore, Option C directly reflects the syllabus content .
NEW QUESTION # 89
Which of the following is a technique used in machine learning?
- A. Decision trees
- B. Equivalence partitioning
- C. Decision tables
- D. Boundary value analysis
Answer: A
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."
NEW QUESTION # 90
Which statement regarding pairwise testing in an AI-based automotive lane-keeping assist system is correct?
Choose ONE option (1 out of 4)
- A. Pairwise testing can reduce testing efforts otherwise very high due to the large number of parameters.
- B. Pairwise testing reduces the test suite so much that it is typically feasible within the available time.
- C. Pairwise testing only uses parameters directly influenced by the driver, otherwise the number of test cases becomes too large.
- D. Pairwise testing is usually insufficient because most defects arise only from interactions of many parameters.
Answer: A
Explanation:
The ISTQB CT-AI syllabus (Section4.3 - Test Design for AI-Based Systems) highlights pairwise testing as an effectivetest-case reduction techniquefor systems with many input parameters. Lane-keeping assist systems typically include environmental, sensor, and vehicle-dynamic parameters, making exhaustive testing infeasible. Pairwise testing significantly reduces the number of test cases while still capturingall 2-way interactions, which are responsible for a large proportion of software defects.
OptionBaligns with this syllabus description: pairwise testing reduces otherwise extremely large parameter combinations, making test effort manageable.
Option A overstates feasibility guarantees; the syllabus never claims pairwise testing always makes testing
"typically feasible." Option C is unsupported and incorrect because pairwise testing doesnotrestrict parameters to driver-controlled ones. Option D is incorrect because, although some defects arise from higher- order interactions, pairwise testing captures many relevant defects and is widely recognized as a pragmatic compromise.
Thus,Option Bis the correct statement.
NEW QUESTION # 91
An image classification system is being trained for classifying faces of humans. The distribution of the data is 70% ethnicity A and 30% for ethnicities B, C and D. Based ONLY on the above information, which of the following options BEST describes the situation of this image classification system?
SELECT ONE OPTION
- A. This is an example of expert system bias.
- B. This is an example of algorithmic bias.
- C. This is an example of hyperparameter bias.
- D. This is an example of sample bias.
Answer: D
Explanation:
A . This is an example of expert system bias.
Expert system bias refers to bias introduced by the rules or logic defined by experts in the system, not by the data distribution.
B . This is an example of sample bias.
Sample bias occurs when the training data is not representative of the overall population that the model will encounter in practice. In this case, the over-representation of ethnicity A (70%) compared to B, C, and D (30%) creates a sample bias, as the model may become biased towards better performance on ethnicity A.
C . This is an example of hyperparameter bias.
Hyperparameter bias relates to the settings and configurations used during the training process, not the data distribution itself.
D . This is an example of algorithmic bias.
Algorithmic bias refers to biases introduced by the algorithmic processes and decision-making rules, not directly by the distribution of training data.
Based on the provided information, option B (sample bias) best describes the situation because the training data is skewed towards ethnicity A, potentially leading to biased model performance.
NEW QUESTION # 92
"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. The input data has not been tested for quality prior to use for testing.
- B. A lack of focus on non-functional requirements testing.
- C. A lack of similarity between the training and testing data.
- D. A lack of focus on choosing the right functional-performance metrics.
Answer: C
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.
NEW QUESTION # 93
Arihant Meditation is a startup using Al to aid people in deeper and better meditation based on analysis of various factors such as time and duration of the meditation, pulse and blood pressure, EEG patters etc. among others. Their model accuracy and other functional performance parameters have not yet reached their desired level.
Which ONE of the following factors is NOT a factor affecting the ML functional performance?
- A. The number of classes
- B. The data pipeline
- C. The quality of the labeling
- D. Biased data
Answer: A
Explanation:
Factors Affecting ML Functional Performance: The data pipeline, quality of the labeling, and biased data are all factors that significantly affect the performance of machine learning models.
The number of classes, while relevant for the model structure, is not a direct factor affecting the performance metrics such as accuracy or bias.
NEW QUESTION # 94
Which of the following statements about ML functional performance metrics is correct?
Choose ONE option (1 out of 4)
- A. The silhouette coefficient describes how well the regression model fits the dependent variables.
- B. The receiver operating characteristic curve shows, depending on parameters, how well the model distinguishes between different clusters.
- C. Metrics used to measure clustering include intra-cluster metrics that measure the proximity of a cluster's data points.
- D. The R-squared metric indicates how well the model distinguishes between different classes based on the ROC curve.
Answer: C
Explanation:
The ISTQB CT-AI syllabus explains ML performance metrics in Section3.2 - Evaluating ML Models. For clustering, which is an unsupervised learning method, the syllabus lists metrics such asintra-cluster distance, inter-cluster distance, and coherence measures. Intra-cluster metrics evaluate how close data points are within a cluster, which directly corresponds to Option A.
Option B is incorrect becauseR-squaredis a regression metric measuring goodness-of-fit, not classification performance, and has no connection to ROC curves. Option C is wrong because thesilhouette coefficientis also a clustering metric, measuring cohesion vs. separation-not regression accuracy. Option D is incorrect because ROC curves evaluatebinary or multiclass classification, not clustering.
Thus, OptionAis the only accurate statement based on the syllabus.
NEW QUESTION # 95
Max. Score: 2
Al-enabled medical devices are used nowadays for automating certain parts of the medical diagnostic processes. Since these are life-critical process the relevant authorities are considenng bringing about suitable certifications for these Al enabled medical devices. This certification may involve several facets of Al testing (I - V).
I.Autonomy
II.Maintainability
III.Safety
IV.Transparency
V.Side Effects
Which ONE of the following options contains the three MOST required aspects to be satisfied for the above scenario of certification of Al enabled medical devices?
SELECT ONE OPTION
- A. Aspects II, III and IV
- B. Aspects I, IV, and V
- C. Aspects I, II, and III
- D. Aspects III, IV, and V
Answer: D
Explanation:
For AI-enabled medical devices, the most required aspects for certification are safety, transparency, and side effects. Here's why:
* Safety (Aspect III): Critical for ensuring that the AI system does not cause harm to patients.
* Transparency (Aspect IV): Important for understanding and verifying the decisions made by the AI system.
* Side Effects (Aspect V): Necessary to identify and mitigate any unintended consequences of the AI system.
Why Not Other Options:
* Autonomy and Maintainability (Aspects I and II): While important, they are secondary to the immediate concerns of safety, transparency, and managing side effects in life-critical processes.
References:This explanation is aligned with the critical quality characteristics for AI-based systems as mentioned in the ISTQB CT-AI syllabus, focusing on the certification of medical devices.
NEW QUESTION # 96
A company producing consumable goods wants to identify groups of people with similar tastes for the purpose of targeting different products for each group. You have to choose and apply an appropriate ML type for this problem.
Which ONE of the following options represents the BEST possible solution for this above-mentioned task?
SELECT ONE OPTION
- A. Regression
- B. Clustering
- C. Association
- D. Classification
Answer: B
Explanation:
A . Regression
Regression is used to predict a continuous value and is not suitable for grouping people based on similar tastes.
B . Association
Association is used to find relationships between variables in large datasets, often in the form of rules (e.g., market basket analysis). It does not directly group individuals but identifies patterns of co-occurrence.
C . Clustering
Clustering is an unsupervised learning method used to group similar data points based on their features. It is ideal for identifying groups of people with similar tastes without prior knowledge of the group labels. This technique will help the company segment its customer base effectively.
D . Classification
Classification is a supervised learning method used to categorize data points into predefined classes. It requires labeled data for training, which is not the case here as we want to identify groups without predefined labels.
Therefore, the correct answer is C because clustering is the most suitable method for grouping people with similar tastes for targeted product marketing.
NEW QUESTION # 97
Which AI-specific test objective and acceptance criterion should be selected MOST LIKELY for testing GPT_Legal?
Choose ONE option (1 out of 4)
- A. Test objective: Evidence of functional safety
Acceptance criterion: The system recognizes failures in the transmission of information and data with the DPMA system and the evaluation system by means of self-tests. - B. Test objective: Evidence that the data is free from inappropriate bias Acceptance criterion: The DPMA's analysis data is statistically compared to data from other sources.
- C. Test objective: Evidence of compatibility
Acceptance criterion: The system can exchange information with the DPMA system and the evaluation system. - D. Test objective: Evidence of evolution
Acceptance criterion: The quality of the research results does not deteriorate with further training.
Answer: D
Explanation:
The ISTQB CT-AI syllabus introducesAI-specific quality characteristics, includingevolution,functional safety,compatibility, andbias-related data quality. Section5.1 - AI-Specific Test Objectivesexplains that evolutionrefers to an AI system's capability to continue improving or at least maintain performance as it undergoes additional training. GPT_Legal is explicitly described as aself-learning systemexpected to:
* continuously reduce false positives,
* achieve weekly accuracy improvements of 10%,
* reach and maintain 90% accuracy,
* adapt to new environments (patent law firm # corporate legal department).
This aligns perfectly with the syllabus definition ofevidence of evolution: ensuring the model doesnot degrade as additional training data is introduced. OptionBtherefore directly supports the described acceptance criteria for this evolving, self-learning application.
Option A (functional safety) is irrelevant because patent searching and drafting do not constitute safety- critical domains. Option C (compatibility) is necessary but not the primary AI-specific objective. Option D addresses bias, which is important but not central to the described performance and continuous-learning expectations.
Thus,Option Bis the most appropriate AI-specific test objective.
NEW QUESTION # 98
Which of the following problems would best be solved using the supervised learning category of regression?
- A. Determining if an animal is a pig or a cow based on image recognition.
- B. Determining the optimal age for a chicken's egg laying production using input data of the chicken's age and average daily egg production for one million chickens.
- C. Recognizing a knife in carry on luggage at a security checkpoint in an airport scanner.
- D. Predicting shopper purchasing behavior based on the category of shopper and the positioning of promotional displays within a store.
Answer: B
Explanation:
The syllabus states:
"Supervised learning... divides problems into two categories: classification and regression.
Regression is used when the problem requires the ML model to predict a numeric output, for example predicting the age of a person based on their habits."
NEW QUESTION # 99
Written requirements are given in text documents, which ONE of the following options is the BEST way to generate test cases from these requirements?
- A. Analyzing source code for generating test cases
- B. Machine learning on logs of execution
- C. Natural language processing on textual requirements
- D. GUI analysis by computer vision
Answer: C
Explanation:
Natural Language Processing (NLP): NLP can analyze and understand human language. It can be used to process textual requirements to extract relevant information and generate test cases.
This method is efficient in handling large volumes of textual data and identifying key elements necessary for testing.
NEW QUESTION # 100
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