HomeMachine Learning Tests
Supervised Learning Skills Test
Test duration:
40
min
No. of questions:
20
Level of experience:
Mid/Senior

Supervised Learning Skills Test

This skills test helps talent professionals test candidates and employees on all the skills required for expertise in Machine Learning. It helps evaluate individuals on NLP, Data Processing, and more. The test helps in talent development and reduces the overall technical screening time by 80%.

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What is Supervised Learning?

It refers to a type of Machine Learning in which machines are trained with well-labelled training data. It is considered a subcategory of Machine Learning and Artificial Intelligence and can be used to classify data or predict specific outcomes. This concept is often used for Risk Assessment, detecting fraud, filtering spam, and image classification.

Why Choose iMocha’s Assessment?

The Skills Test offers an objective way of testing candidates and existing employees on skills such as data processing, hyperparameter tuning, neural networks, and more. It can easily reduce the interview-to-hire ratio by 80%, identify skill gaps, and help you benchmark talent on the industry scale. The test uses a combination of Multiple Choice Questions, True or False statements, and more to test a candidate's aptitude in Supervised Learning.

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How it works

Test Summary

This test assesses candidates for the following skills:

  • Their knowledge and understanding of Applied Mathematics
  • Knowledge of the fundamentals of computer sciences and programming
  • An understanding of various Machine Learning algorithms necessary for the role
  • Knowledge of Neural Networks and how they function
  • Fundamentals of Natural Language Processing (NLP) and the role it plays in making Machine Learning models
  • Data processing abilities to preprocess data before applying Machine Learning algorithms
  • Understanding of Hyperparameter Tuning, which can help improve the performance of Machine Learning models
  • Knowledge of model selection to choose the right ML model to solve a given problem

The test can also be coupled with features that prevent individuals from being able to cheat, such as video proctoring and window violation. It also comes with the ability to restrict access to different members of the hiring team using the role-based access feature.

Useful for hiring
  • Deep Learning Engineer
  • Natural Language Processing (NLP) Engineer
  • Computer Vision Engineer
  • Machine Learning Engineer
  • ML Software Engineer
  • ML Data Analyst
  • ML Project Manager
  • Data Scientist
Test Duration
40
min
No. of Questions
20
Level of Expertise
Mid/Senior
Topics Covered
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Sample Question
Choose from our 100,000+ questions library or add your own questions to make powerful custom tests.
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Question:

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You can customize this test by

Setting the difficulty level of the test

Choose easy, medium, or tricky questions from our skill libraries to assess candidates of different experience levels.

Combining multiple skills into one test

Add multiple skills in a single test to create an effective assessment and assess multiple skills together.

Adding your own
questions to the test

Add, edit, or bulk upload your coding, MCQ, and whiteboard questions.

Requesting a tailor-made test

Receive a tailored assessment created by our subject matter experts to ensure adequate screening.
FAQ
How is this skill test customized?
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iMocha's Skills Test is customized to assess individuals based on the key skills they need to be hired for the role of a Machine Learning Engineer. Hiring Managers can choose from various difficulty levels and also customize the test with a combination of different types of questions.

Talent professionals can also use features such as the Coding Simulators and AI-EnglishPro to assess candidates on various skills.

What are the most common interview questions related to supervised learning?
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Some of the most common interview questions related to this skill include the following:

  • What are the two major types of problems solved by Supervised Learning?
  • How do you differentiate between Supervised and Unsupervised Learning? Could you give us an example?
  • What is the k-Nearest Neighbors algorithm?
  • What is overfitting and underfitting in Machine Learning?
  • How is Gradient Boosting typically used to improve Machine Learning?