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Machine Learning Assessment to Evaluated Machine Learning Skills

This Machine Learning assessment aims to measure work readiness skills in core and advanced concepts of the Machine Learning algorithm. Along with assessing domain knowledge, this Machine Learning test also evaluates hands-on programming skills of the applicant.

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Inside This Assessment

This Machine Learning assessment by Mercer | Mettl intends to assess the data analytics skills of the candidate in Python along with measuring the level of their knowledge on core and advanced Machine Learning concepts. The 60-minutes online assessment contains a unique set of 19 MCQs that evaluate domain knowledge of the candidate on three difficulty levels, basic, intermediate and advanced.

The Front-end Developer assessment also measures the hands-on prgramming skills of an applicant through a simulator based coding problem. The test is thoughtfully crafted and validated by Mercer | Mettl's team of subject matter experts which ensures that it is comprehensive and the questions are framed as per current industry standards. The evaluation reports offer a detailed insight to the strength and development areas of the applicant enabling evaluators to make the best decision.

Key profiles this Machine Learning assessment is useful for hiring:

  • Machine Learning Engineers
  • Machine Learning Specialists
  • Machine Learning Consultants


This Assessment is a part of following Skills Libraries

Machine Learning Competency Framework

Get a detailed look inside the test

Machine Learning Competencies Under Scanner

Machine Learning Engineer with experience in Python


Hands-On Programming

This Machine Learning test assesses a candidate's coding skills on a robust coding simulator by giving real-world problems and evaluates their hands-on experience and capability to code in a desired language.

Core Concepts

This Machine Learning assessment test evaluates the knowledge of core concepts such as Data Preprocessing, Dimensionality Reduction, Regression and its types along with Data Science Algorithms.

Advanced Concepts

This Machine Learning assessment test evaluates the knowledge of advanced concepts such as Neural Network, Natural Language Processing, Deep Learning and Deep Learning Artificial Neural Networks.

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Step 4: Insightful Report

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Frequently Asked Questions (FAQs)

Yes, it is possible. Please contact Mercer | Mettl for assistance.

Yes, it can be done. In case of further information, please write to Mercer | Mettl.

Sometimes it seems quite challenging to come to terms with the stressful interview environment. Very few participants would fancy the idea of getting bombarded by complex questions during an interview. But interviewees have to prove their mettle and succeed in the recruitment process to get their dream jobs. As machine learning engineers, you need to make an excellent first impression during the interview. So, given below are some sample questions that are frequently asked at an interview:
 Machine learning interview questions are generally based on real-world challenges and look at the problem-solving abilities of candidates. Question types may vary depending upon the nature of the business. Let’s elucidate the statement with some examples:
 A digital image processing company may seek a relevant answer for this question - “What steps will you follow to figure out all the images that constitute part of a landscape?’’ A speech processing company could ask in an interview, “Amidst the pile of voicemails, how can you find the file containing the voice of an old woman?’’ A video processing company may be more interested in this question: “In a soccer video, can you mark all the times when a specific footballer is in the view?” A leading NLP company may ask a candidate, “How would you devise suggestions for the next word in an unfinished sentence?’’  There could be multiple use cases and a wide array of questions that may be asked. It is advisable to go through some case studies that relate to the company’s nature of business.
 Also, given below are some questions that candidates should be prepared to answer:
 What is the difference between labeled and unlabeled data?
 How to use labeled data, and what if you don’t have any labeled data?
 How do you explain skewed data?
 How can you detect overfitting and underfitting?
 How do you stop overfitting?
 How do you make predictions faster?
 Moreover, it’s common for interviewers to ask candidates about their previous machine learning projects that they have accomplished in a detailed manner. Interviewees need to concisely explain what challenges they faced in the past and how did they address those problems.

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