Azure Machine Learning is an end-to-end data science and advanced analytics solution which enables data scientists to prepare data, develop experiments, and deploy models at the cloud scale. It fully supports open-source technologies. Azure Machine Learning Studio helps to give an interactive, visual workspace to easily build, test, and iterate on a predictive analysis model.
Azure Machine Learning skills test helps recruiters and hiring managers to assess candidates’ ability to work with Azure Machine Learning analytics. This test will evaluate a candidate’s practical knowledge and job readiness. iMocha’s Azure Machine Learning pre-employment test is created & validated by experienced subject matter experts (SMEs) to assess & hire Machine Learning Specialists as per industry standards. The candidates can take the Azure Machine Learning online test from anywhere in the comfort of their time zone.
Azure Machine Learning skill test helps to screen the candidates who possess the following traits:
iMocha’s test platform helps you to invite candidates to take the Azure Machine Learning test easily. You can get instant results and share them with hiring managers/interviewers, along with interview notes. Our powerful reporting helps you analyze candidates’ section-wise performance to gauge their strengths and weaknesses. Features like window violation and webcam proctoring help you detect malpractices used by candidates during the test.
Azure Machine Learning interview test may contain MCQs (Multiple Choice Questions), MAQs (Multiple Answer Questions), Fill in the Blanks, Descriptive Questions, Whiteboard Questions, Audio / Video Questions, AI-LogicBox (AI-based Pseudo-Coding Platform), Coding Simulations, True or False Questions, etc.
This test is designed considering EEOC guidelines; it will help you assess & hire diverse talent without any bias.
You have a dataset that is very biased towards one class (majority), 99% of the data is for class 1 and 1% data is for class2.
You want to perform the classification task and remove the bias from the data.
What should you do?
A) Perform oversampling for minority class (SMOTE)
B) Perform undersampling for majority class
C) Perform undersampling for minority class
D) Perform oversampling for majority class