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Natural Language Processing (NLP) Online Test
Clock
Test duration:
20
min
Notes
No. of questions:
10
Tie
Level of experience:
Entry/Mid/Expert

Natural Language Processing (NLP) Test

iMocha’s Natural Language Processing (NLP) online test is the preferred pre-employment test for recruiters and hiring managers to hire job-fit candidates for roles such as Software Engineer – NLP, Natural Language Processing - Technical Architect, and Natural Language Processing Engineer. Our test helps to reduce hiring time by 45%.

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About

Natural Language Processing (NLP) Online Test

The Natural Language Processing (NLP) assessment test has been designed and developed by subject matter experts (SMEs) to evaluate NLP skills of natural language processing specialists- as per industry standards. Natural Language Processing (NLP) is a computer science field along with artificial intelligence and computational linguistics concerned with the interactions between computers and human (natural) languages, and, in particular, concerned with programming computers to fruitfully process large natural language corpora.

Natural Language Processing (NLP) online test helps hiring managers and employers to validate the NLP analysis skills of an applicant before an interview. Weed out irrelevant candidates using Natural Language Processing (NLP) pre-employment test and spend time interviewing only the relevant candidates. Get the detailed reports of test results instantly.

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Test Summary

This Natural Language Processing (NLP) skill test may contain MCQ's (Multiple Choice Questions), MAQ's (Multiple Answer Questions), Fill in the Blanks, Descriptive, Whiteboard Questions, Audio/Video Questions, AI-LogicBox, Coding Simulations, True or False Questions, etc.

Online Natural Language Processing (NLP) test is designed considering EEOC guidelines, it will help you assess and hire diverse talent without any bias.

Test Duration
20
No. of Questions
10
Level of Expertise
Entry/Mid/Expert
Useful for hiring
  • Software Engineer – NLP
  • Natural Language Processing - Technical Architect
  • Natural Language Processing Engineer
Topics Covered
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Algorithms for Sentence Classification

iMocha’s Natural Language Processing (NLP) online test helps recruiters and hiring managers to evaluate candidate’s understanding about sentence classification algorithms which helps to categorize data into a category

Word2Vec

This Natural Language Processing (NLP) assessment test assesses applicant’s knowledge about word2vec technique which helps to detect and suggest synonymous words for a sentence
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Singular Value Decomposition

Our test assesses applicant’s knowledge about singular value decomposition which helps in text mining
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Optimizers

Our Natural Language Processing (NLP) pre-employment test evaluates candidate’s understanding about optimizer algorithms which are used to improve efficiency of data learning rate
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Information Retrieval

Our Natural Language Processing (NLP) skill test evaluates applicant’s understanding about information retrieval technique that is used to get some appropriate information from text data
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Dimensionality Reduction in NLP

Our assessment evaluates candidate’s knowledge about dimensionality reduction self-learning technique by using machine learning by reducing input variables in dataset
Sample Question
Choose from our 100,000+ questions library or add your own questions to make powerful custom tests.

Question types :

Multiple Option

Topic:

Difficulty:

Hard


Q 1. You have been asked to train « Naive Bayes model for spam detection dataset

Using Python command, what wil you add in the given code at XXXX to deciore, it and train your modal for the given dataset?

import numpy os np

X= nporroyll 3. 2b[1 21 [2.21.[3,21)
Y= nporray(fi.a,a.1))

XXXX

from skinamnaive_bayas import

Gaussianna

otf = Gaussianne()

ceataie(x ¥)

from skinamnaive_bayas import

Gaussianna

otf = Gaussianne()

ceataie(x ¥)

from skinamnaive_bayas import

Gaussianna

otf = Gaussianne()

ceataie(x ¥)

from skinamnaive_bayas import

Gaussianna

otf = Gaussianne()

ceataie(x ¥)

Question types :

Multiple Option

Topic:

Machine teaming

Difficulty:

Hard


Q 1. Q2 Lets assume that you have 6 Pandas DataFrome(af) in the below tole.

Index Code Open High
0 AAPL 19877 70074
1 AAPL 19877 70074
2 AAPL 19877 70074
3 AAPL 19877 70074
4 AAPL 19877 70074

When the following commend is executed, what hoppens to the DatoFrome?

df= defiinat()
Y= nporray(fi.a,a.1))

Index Code Open High
0 AAPL 19877 70074
1 AAPL 19877 70074
2 AAPL 19877 70074
3 AAPL 19877 70074
4 AAPL 19877 70074
Index Code Open High
0 AAPL 19877 70074
1 AAPL 19877 70074
2 AAPL 19877 70074
3 AAPL 19877 70074
4 AAPL 19877 70074
it gives an error becouse of the third row which has on empty string.
None of these

Question types :

Multiple Option

Topic:

Word2Vec

Difficulty:

Easy

Question:

In word vector quantization, one of the problems is that the relative position of words concerning the target word matters a lot. Which of the following seems a good weight term to multiply with each word?


Options

  • 1/relative_position<br />&nbsp;
  • Relative_position<br />&nbsp;
  • (relative_position)^2<br />&nbsp;
  • None of the mentioned<br />&nbsp;
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