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NEW QUESTION # 27
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?
Answer: C
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 # 28
Which of the following aspects is a challenge when handling test data for an AI-based system?
Answer: C
Explanation:
Handlingtest datain AI-based systems presents numerous challenges, particularly in terms ofdata privacy and confidentiality. AI models often require vast amounts of training data, some of which may containpersonal, sensitive, or confidential information. Ensuringcompliance with data protection laws (e.g., GDPR, CCPA)and implementingsecure data-handling practicesis a major challenge in AI testing.
* Data Privacy Regulations
* AI-based systems frequently process personal data, such as images, names, and transaction details, leading toprivacy concerns.
* Compliance with regulations such asGDPR (General Data Protection Regulation)andCCPA (California Consumer Privacy Act)requiresproper anonymization, encryption, or redactionof sensitive data before using it for testing.
* Data Security Challenges
* AI models mayleak confidential informationif proper security measures are not in place.
* Protectingtraining and test data from unauthorized accessis crucial to maintainingtrust and compliance.
* Legal and Ethical Considerations
* Organizations mustobtain legal approvalbefore using certain datasets, especially those containinghealth records, financial data, or personally identifiable information (PII).
* Testers may need toemploy synthetic dataordata maskingtechniques to minimize exposure risks.
* (B) Output data or intermediate data#
* While analyzing output data is important, it does notpose a significant challengecompared to handlingpersonal or confidential test data.
* (C) Video frame speed or aspect ratio#
* These aretechnical challengesin processing AI models but do not fall underdata privacy or ethical considerations.
* (D) Data frameworks or machine learning frameworks#
* Choosing an appropriateML framework (e.g., TensorFlow, PyTorch)is important, but it is nota major challenge related to test data handling.
* Handling personal or confidential data is a critical challenge in AI testing"Personal or otherwise confidential data may need special techniques for sanitization, encryption, or redaction.Legal approval for use may also be required." Why is Option A Correct?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, asdata privacy and confidentiality are major challenges when handling test data for AI-based systems.
NEW QUESTION # 29
A mobile app start-up company is implementing an AI-based chat assistant for e-commerce customers. In the process of planning the testing, the team realizes that the specifications are insufficient.
Which testing approach should be used to test this system?
Answer: D
Explanation:
Whentesting an AI-based chat assistantfor e-commerce customers, thelack of sufficient specifications makes it difficult to use structured test techniques. TheISTQB CT-AI Syllabusrecommendsexploratory testingin such cases:
* Why Exploratory Testing?
* Exploratory testing is usefulwhen specifications are incomplete or unclear.
* AI-based systems, particularly those usingnatural language processing (NLP),may not behave deterministically, making scripted test cases ineffective.
* Thetester interacts dynamicallywith the system, identifying unexpected behaviorsnot 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 isnot helpfulfor AI chatbots.
* C (Equivalence partitioning)# Incorrect, asthis technique requires well-defined inputs and outputs, which are missing due toinsufficient specifications.
* D (State transition testing)# Incorrect, as state-based testingrequires knowledge of valid and invalid transitions, which is difficult with a chatbot lacking a clear specification.
Thus,Option A is the correct answer, asexploratory testing is the best approach when dealing with insufficient specifications in AI-based systems.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 7.7 (Selecting a Test Approach for an ML System)
* ISTQB CT-AI Syllabus v1.0, Section 9.6 (Experience-Based Testing of AI-Based Systems).
NEW QUESTION # 30
Written requirements are given in text documents, which ONE of the following options is the BEST way to generate test cases from these requirements?
SELECT ONE OPTION
Answer: A
Explanation:
When written requirements are given in text documents, the best way to generate test cases is by using Natural Language Processing (NLP). Here's why:
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.
Why Not Other Options:
Analyzing source code for generating test cases: This is more suitable for white-box testing where the code is available, but it doesn't apply to text-based requirements.
Machine learning on logs of execution: This approach is used for dynamic analysis based on system behavior during execution rather than static textual requirements.
GUI analysis by computer vision: This is used for testing graphical user interfaces and is not applicable to text-based requirements.
NEW QUESTION # 31
Which ONE of the following combinations of Training, Validation, Testing data is used during the process of learning/creating the model?
SELECT ONE OPTION
Answer: C
Explanation:
The process of developing a machine learning model typically involves the use of three types of datasets:
Training Data: This is used to train the model, i.e., to learn the patterns and relationships in the data.
Validation Data: This is used to tune the model's hyperparameters and to prevent overfitting during the training process.
Test Data: This is used to evaluate the final model's performance and to estimate how it will perform on unseen data.
Let's analyze each option:
A . Training data - validation data - test data
This option correctly includes all three types of datasets used in the process of creating and validating a model. The training data is used for learning, validation data for tuning, and test data for final evaluation.
B . Training data - validation data
This option misses the test data, which is crucial for evaluating the model's performance on unseen data after the training and validation phases.
C . Training data - test data
This option misses the validation data, which is important for tuning the model and preventing overfitting during training.
D . Validation data - test data
This option misses the training data, which is essential for the initial learning phase of the model.
Therefore, the correct answer is A because it includes all necessary datasets used during the process of learning and creating the model: training, validation, and test data.
NEW QUESTION # 32
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