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Core Corporate Functions>NLP Engineer Test

NLP Engineer Test to help you screen and find top engineering talent

The Natural Language Processing (NLP) Engineer Recruitment Test is a pre-employment test to assess the candidates on skills such as data preprocessing, text matching, entity parsing, lexicon normalization, etc. This NLP Engineer Test is the preferred pre-hire assessment for tech recruiters and hiring managers to help them screen and select the most suitable candidate for a given position.

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Inside this NLP Engineer Online Test

The NLP Engineer Test is a pre-employment assessment used by recruiting managers to gauge candidates' employability skills by assessing their on-the-job skills and domain knowledge before an interview. This test comprises 30 carefully selected multiple-choice questions (MCQs) and takes 45 minutes to complete. Then, using comprehensive reporting, employers can analyze the NLP test results in-depth to arrive at an informed hiring decision and determine if someone is an ideal candidate for the given position. 

Seasoned subject matter experts have designed and validated this NLP Engineer Recruitment Test to help employers understand candidates' working skills and job readiness in this crucial domain of artificial intelligence (AI). This assessment aims to understand how well-versed candidates are in natural language processing and helps to predict performance for distinct roles and responsibilities. 

The test helps to screen candidates for the following roles:  

  • Natural Language Processing (NLP) developer  
  • Machine learning engineer - NLP 
  • Data scientist - NLP 
  • NLP software engineer 

Overview

Natural Language Processing (NLP) is a subdivision of artificial intelligence (AI). It lends itself to being used to enable machines to process, understand, and manipulate human language and help them automate repetitive tasks. Even though NLP technology has continued to evolve, it has already been helpful in many incredible ways. Examples include grammar checks, machine translation, automatic ticket classification, etc.

NLP is increasingly becoming necessary for businesses because it helps them interpret vast volumes of text data (online reviews, social media comments, etc.) to gain insights into metrics such as customer satisfaction and brand performance. All this data contains a plethora of valuable insights, which NLP tools process in real-time and ensure an accurate representation by allowing machines to analyze and make sense of human language meticulously and consistently. After uncovering such valuable insights, businesses can prioritize and arrange their data per their requirements.

NLP engineering continues to be a highly sought-after skill at major tech organizations. Engineers hired for Natural Language Processing roles could oversee the implementation of tools for personalized SMS marketing, chatbots, search recommendations, sentiment analysis, etc. While this role may be highly coveted by all companies who want to leverage the power of NLP, the hiring process is still challenging and competitive due to complications around building a healthy talent pool.

Moreover, resumes do not offer a clear understanding of a candidate's technical abilities, nor can they predict someone's on-the-job performance and work readiness. That is where the NLP Engineer Recruitment Test comes in handy in evaluating the NLP skills of candidates by gauging their technical competencies and skills against some pre-defined parameters. This online pre-employment assessment allows hiring managers to filter out candidates who are not an ideal match while moving forward with those individuals with the required skillsets for the next stage of the recruiting process. Undoubtedly, the NLP Engineer Test is the surefire way for organizations to screen candidates efficiently and gain an in-depth understanding of their domain expertise.

SKILL LIBRARY

This NLP Engineer Test is a part of following Skills Libraries

NLP Engineer Test competency framework

Get a detailed look inside the test

NLP Engineer Recruitment Test Competencies Under Scanner

NLP

The following subskills are assessed in this section: Data preprocessing, text matching, entity parsing, lexicon normalization, word embedding, word2vec algorithm, TF-IDF, syntactic parsing, semantic analysis, applications, parts-of-speech tagging, N-gram language models, sentiment analysis.

Customize This NLP Engineer Online Test

Flexible customization options to suit your needs

Set difficulty level of test

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

Combine multiple skills into one test

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Add your own questions to the test

Add, edit or bulk upload your own coding questions, MCQ, whiteboarding questions & more.

Request a tailor-made test

Get a tailored assessment created with the help of our subject matter experts to ensure effective screening.

The Mercer | Mettl NLP Engineer Test Advantage

The Mercer | Mettl Edge
  • Industry Leading 24/7 Support
  • State of the art examination platform
  • Inbuilt Cutting Edge AI-Driven Proctoring
  • Simulators designed by developers
  • Tests Tailored to Your business needs
  • Support for 20+ Languages in 80+ Countries Globally

NLP Engineer Recruitment Test Can Be Setup in 4 Steps

Step 1: Add test

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Step 2: Share link

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Step 3: Test View

Candidate take the test

Step 4: Insightful Report

You get their tests report

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Frequently Asked Questions (FAQs)

The Mercer | Mettl NLP Engineer Hiring Test is tailored for recruitment. However, we can do benchmarking for custom sample sets as well. Please write to us about your request; we will be glad to assist you.

We have previously customized Online LP Engineer Hiring Test reports based on the client’s requirements. So please write to us with your request, and we will be glad to provide you with a solution. 

The following are some typical questions on which interviewees are assessed:

  • What are the most viable approaches used to solve NLP problems?
  • Can you please explain the stages involved in the NLP project lifecycle?
  • How would you do data augmentation in NLP projects?
  • How would you preprocess data for NLP?
  • What are NLP components used in conversational agents?
  • What metrics would you use to evaluate NLP models?
  • Can you please describe the information extraction (IE) pipeline?
  • Why do we use autoencoders?
  • Can you please explain the following NLP terms: perplexity, N-gram, and pragmatic analysis?
  • How would you differentiate between Natural Language Understanding (NLU) and Natural Language Processing (NLP)?