Natural Language Processing (NLP) Test

The NLP (Natural Language Processing) Online test uses scenario-based MCQs to evaluate candidates on their knowledge of NLP concepts and techniques, such as text classification, information extraction, sentiment analysis, and named entity recognition. The test assesses a candidate's ability to apply NLP techniques to real-world problems and scenarios and design effective NLP models.

Covered skills:

Tokenization
Text Classification
Sentiment Analysis
Named Entity Recognition
Word Embeddings
Language Modeling
Machine Translation
Information Extraction
Text Summarization
Topic Modeling

Test Duration

25 mins

Difficulty Level

Moderate

Questions

12 Natural Language Processing MCQs

Availability

Ready to use

Get started for free

Preview questions

About the Natural Language Processing (NLP) Assessment Test

The Natural Language Processing (NLP) Online Test is designed to assist recruiters and hiring managers in identifying candidates with strong NLP skills from a large pool of applicants. It helps streamline the hiring process by providing an objective assessment of candidates' abilities, reducing the time spent on interviewing unqualified individuals. This test allows you to make informed hiring decisions, ensuring you bring the right talent to your organization.

This test evaluates candidates on their understanding and application of various NLP techniques. It covers areas such as text preprocessing, including tokenization and stemming, and extends to more advanced concepts like sentiment analysis, named entity recognition, and language modeling. The assessment also examines a candidate's knowledge of word embeddings, machine translation, information extraction, text summarization, and topic modeling, providing a detailed view of their NLP skill set.

1200+ customers in 80 countries

Use Adaface tests trusted by recruitment teams globally. Adaface skill assessments measure on-the-job skills of candidates, providing employers with an accurate tool for screening potential hires.

Get started for free

Preview questions

Non-googleable questions

We have a very high focus on the quality of questions that test for on-the-job skills. Every question is non-googleable and we have a very high bar for the level of subject matter experts we onboard to create these questions. We have crawlers to check if any of the questions are leaked online. If/ when a question gets leaked, we get an alert. We change the question for you & let you know.

How we design questions

These are just a small sample from our library of 15,000+ questions. The actual questions on this Natural Language Processing (NLP) Online Test will be non-googleable.

🧐 Question
Medium Hate Speech Detection Challenge Text Classification Data Imbalance Class Imbalance Handling Data Augmentation Techniques	Solve
You are working on a project to detect hate speech in social media posts. Your initial model, a basic binary classification model, has achieved high accuracy during training, but it's not performing well on the validation set. You also notice that your dataset has significantly more non-hate-speech examples than hate-speech examples. Given this situation, which of the following strategies could likely improve the performance of your model? A: Collect more data and retrain the model. B: Introduce data augmentation techniques specifically for hate-speech examples. C: Change the model architecture from binary classification to multi-class classification. D: Replace all the words in the posts with their synonyms to increase the diversity of the data. E: Remove the non-hate-speech examples from the dataset to focus on the hate-speech examples.
Easy Identifying Fake Reviews Text Classification Data Science Machine Learning Model Evaluation	Solve
You are a data scientist at an online marketplace company. Your task is to develop a solution to identify fake reviews on your platform. You have a dataset where each review is marked as either 'genuine' or 'fake'. After developing an initial model, you find that it's accurately classifying 'genuine' reviews but performing poorly with 'fake' ones. Which of the following steps can likely improve your model's performance in this context? A: Use a more complex model to capture the intricacies of 'fake' reviews. B: Obtain more data to improve the overall performance of the model. C: Implement a cost-sensitive learning approach, placing a higher penalty on misclassifying 'fake' reviews. D: Translate the reviews to another language and then back to the original language to enhance their clarity. E: Remove the 'genuine' reviews from your training set to focus on 'fake' reviews.
Medium Sentence probability N-Grams Language Models	Solve
Consider the following pseudo code for calculating the probability of a sentence using a bigram language model: Assume that the bigram and unigram counts are as follows: bigram_counts = {("i", "like"): 2, ("like", "cats"): 1, ("cats", "too"): 1} unigram_counts = {"i": 2, "like": 2, "cats": 2, "too": 1} vocabulary_size = 4 What is the probability of the sentence "I like cats too" using the bigram language model?
Easy Tokenization and Stemming Stemming Tokenization Natural Language Processing	Solve
You are working on a natural language processing project and need to preprocess the text data for further analysis. Your task is to tokenize the text and apply stemming to the tokens. Assuming you have an English text corpus, which of the following combinations of tokenizer and stemmer would most likely result in the best balance between token granularity and generalization?
Medium Word Sense Disambiguation Wsd Cosine Similarity Vector Operations	Solve
You have been provided with a pre-trained BERT model (pretrained_bert_model) and you need to perform Word Sense Disambiguation (WSD) on the word "bat" in the following sentence: "The bat flew around the room." You have also been provided with a function called cosine_similarity(vec1, vec2) that calculates the cosine similarity between two vectors. Which of the following steps should you perform to disambiguate the word "bat" in the given sentence using the BERT model and cosine similarity? 1. Tokenize the sentence and pass it through the pre-trained BERT model. 2. Extract the embeddings of the word "bat" from the sentence. 3. Calculate the cosine similarity between the "bat" embeddings and each sense's representative words. 4. Choose the sense with the highest cosine similarity. 5. Calculate the Euclidean distance between the "bat" embeddings and each sense's representative words. 6. Choose the sense with the lowest Euclidean distance.

	🧐 Question	🔧 Skill
	Medium Hate Speech Detection Challenge Text Classification Data Imbalance Class Imbalance Handling Data Augmentation Techniques	2 mins Natural Language Processing	Solve
You are working on a project to detect hate speech in social media posts. Your initial model, a basic binary classification model, has achieved high accuracy during training, but it's not performing well on the validation set. You also notice that your dataset has significantly more non-hate-speech examples than hate-speech examples. Given this situation, which of the following strategies could likely improve the performance of your model? A: Collect more data and retrain the model. B: Introduce data augmentation techniques specifically for hate-speech examples. C: Change the model architecture from binary classification to multi-class classification. D: Replace all the words in the posts with their synonyms to increase the diversity of the data. E: Remove the non-hate-speech examples from the dataset to focus on the hate-speech examples.
	Easy Identifying Fake Reviews Text Classification Data Science Machine Learning Model Evaluation	2 mins Natural Language Processing	Solve
You are a data scientist at an online marketplace company. Your task is to develop a solution to identify fake reviews on your platform. You have a dataset where each review is marked as either 'genuine' or 'fake'. After developing an initial model, you find that it's accurately classifying 'genuine' reviews but performing poorly with 'fake' ones. Which of the following steps can likely improve your model's performance in this context? A: Use a more complex model to capture the intricacies of 'fake' reviews. B: Obtain more data to improve the overall performance of the model. C: Implement a cost-sensitive learning approach, placing a higher penalty on misclassifying 'fake' reviews. D: Translate the reviews to another language and then back to the original language to enhance their clarity. E: Remove the 'genuine' reviews from your training set to focus on 'fake' reviews.
	Medium Sentence probability N-Grams Language Models	2 mins Natural Language Processing	Solve
Consider the following pseudo code for calculating the probability of a sentence using a bigram language model: Assume that the bigram and unigram counts are as follows: bigram_counts = {("i", "like"): 2, ("like", "cats"): 1, ("cats", "too"): 1} unigram_counts = {"i": 2, "like": 2, "cats": 2, "too": 1} vocabulary_size = 4 What is the probability of the sentence "I like cats too" using the bigram language model?
	Easy Tokenization and Stemming Stemming Tokenization Natural Language Processing	2 mins Natural Language Processing	Solve
You are working on a natural language processing project and need to preprocess the text data for further analysis. Your task is to tokenize the text and apply stemming to the tokens. Assuming you have an English text corpus, which of the following combinations of tokenizer and stemmer would most likely result in the best balance between token granularity and generalization?
	Medium Word Sense Disambiguation Wsd Cosine Similarity Vector Operations	2 mins Natural Language Processing	Solve
You have been provided with a pre-trained BERT model (pretrained_bert_model) and you need to perform Word Sense Disambiguation (WSD) on the word "bat" in the following sentence: "The bat flew around the room." You have also been provided with a function called cosine_similarity(vec1, vec2) that calculates the cosine similarity between two vectors. Which of the following steps should you perform to disambiguate the word "bat" in the given sentence using the BERT model and cosine similarity? 1. Tokenize the sentence and pass it through the pre-trained BERT model. 2. Extract the embeddings of the word "bat" from the sentence. 3. Calculate the cosine similarity between the "bat" embeddings and each sense's representative words. 4. Choose the sense with the highest cosine similarity. 5. Calculate the Euclidean distance between the "bat" embeddings and each sense's representative words. 6. Choose the sense with the lowest Euclidean distance.

	🧐 Question	🔧 Skill	💪 Difficulty	⌛ Time
	Hate Speech Detection Challenge Text Classification Data Imbalance Class Imbalance Handling Data Augmentation Techniques	Natural Language Processing	Medium	2 mins	Solve
You are working on a project to detect hate speech in social media posts. Your initial model, a basic binary classification model, has achieved high accuracy during training, but it's not performing well on the validation set. You also notice that your dataset has significantly more non-hate-speech examples than hate-speech examples. Given this situation, which of the following strategies could likely improve the performance of your model? A: Collect more data and retrain the model. B: Introduce data augmentation techniques specifically for hate-speech examples. C: Change the model architecture from binary classification to multi-class classification. D: Replace all the words in the posts with their synonyms to increase the diversity of the data. E: Remove the non-hate-speech examples from the dataset to focus on the hate-speech examples.
	Identifying Fake Reviews Text Classification Data Science Machine Learning Model Evaluation	Natural Language Processing	Easy	2 mins	Solve
You are a data scientist at an online marketplace company. Your task is to develop a solution to identify fake reviews on your platform. You have a dataset where each review is marked as either 'genuine' or 'fake'. After developing an initial model, you find that it's accurately classifying 'genuine' reviews but performing poorly with 'fake' ones. Which of the following steps can likely improve your model's performance in this context? A: Use a more complex model to capture the intricacies of 'fake' reviews. B: Obtain more data to improve the overall performance of the model. C: Implement a cost-sensitive learning approach, placing a higher penalty on misclassifying 'fake' reviews. D: Translate the reviews to another language and then back to the original language to enhance their clarity. E: Remove the 'genuine' reviews from your training set to focus on 'fake' reviews.
	Sentence probability N-Grams Language Models	Natural Language Processing	Medium	2 mins	Solve
Consider the following pseudo code for calculating the probability of a sentence using a bigram language model: Assume that the bigram and unigram counts are as follows: bigram_counts = {("i", "like"): 2, ("like", "cats"): 1, ("cats", "too"): 1} unigram_counts = {"i": 2, "like": 2, "cats": 2, "too": 1} vocabulary_size = 4 What is the probability of the sentence "I like cats too" using the bigram language model?
	Tokenization and Stemming Stemming Tokenization Natural Language Processing	Natural Language Processing	Easy	2 mins	Solve
You are working on a natural language processing project and need to preprocess the text data for further analysis. Your task is to tokenize the text and apply stemming to the tokens. Assuming you have an English text corpus, which of the following combinations of tokenizer and stemmer would most likely result in the best balance between token granularity and generalization?
	Word Sense Disambiguation Wsd Cosine Similarity Vector Operations	Natural Language Processing	Medium	2 mins	Solve
You have been provided with a pre-trained BERT model (pretrained_bert_model) and you need to perform Word Sense Disambiguation (WSD) on the word "bat" in the following sentence: "The bat flew around the room." You have also been provided with a function called cosine_similarity(vec1, vec2) that calculates the cosine similarity between two vectors. Which of the following steps should you perform to disambiguate the word "bat" in the given sentence using the BERT model and cosine similarity? 1. Tokenize the sentence and pass it through the pre-trained BERT model. 2. Extract the embeddings of the word "bat" from the sentence. 3. Calculate the cosine similarity between the "bat" embeddings and each sense's representative words. 4. Choose the sense with the highest cosine similarity. 5. Calculate the Euclidean distance between the "bat" embeddings and each sense's representative words. 6. Choose the sense with the lowest Euclidean distance.

Get started for free

Preview questions

With Adaface, we were able to optimise our initial screening process by upwards of 75%, freeing up precious time for both hiring managers and our talent acquisition team alike!

Brandon Lee, Head of People, Love, Bonito

It's very easy to share assessments with candidates and for candidates to use. We get good feedback from candidates about completing the tests. Adaface are very responsive and friendly to deal with.

Kirsty Wood, Human Resources, WillyWeather

We were able to close 106 positions in a record time of 45 days! Adaface enables us to conduct aptitude and psychometric assessments seamlessly. My hiring managers have never been happier with the quality of candidates shortlisted.

Amit Kataria, CHRO, Hanu

We evaluated several of their competitors and found Adaface to be the most compelling. Great library of questions that are designed to test for fit rather than memorization of algorithms.

Swayam Narain, CTO, Affable

Why you should use Pre-employment Natural Language Processing (NLP) Test?

The Natural Language Processing (NLP) Online Test provides a means of quickly evaluating a candidate's practical abilities in this rapidly growing field. Instead of relying solely on resumes or subjective interviews, this test offers objective insights into a candidate's skill level, ensuring you identify individuals who can immediately contribute to your NLP projects.

This test evaluates a candidate's knowledge of key NLP techniques, including breaking down text into smaller parts, categorizing texts, understanding emotions from text, finding important entities, representing words as vectors, creating text, converting text between languages, pulling information from text, shortening long texts, and finding common topics. The questions are designed to assess a range of abilities from text processing to high-level model development.

After the candidate completes the test, you'll receive a scorecard detailing their performance across different NLP skills. This report provides benchmarks, enabling you to easily shortlist top candidates and make data-driven hiring decisions.

What topics are covered in the Natural Language Processing (NLP) Test?

This test assesses a candidate's knowledge of Tokenization, Text Classification, and Sentiment Analysis, skills core to understanding and processing human language. Tokenization breaks down text into manageable units, while Text Classification categorizes text, and Sentiment Analysis gauges emotional tone. Testing these skills ensures candidates can derive meaningful insights from textual data and build systems that understand nuanced communication.

Furthermore, the evaluation includes Named Entity Recognition, Word Embeddings, and Language Modeling, which are important for information extraction and language generation. Named Entity Recognition identifies key pieces of information, and Word Embeddings capture semantic relationships between words. Language Modeling allows machines to predict and generate realistic text. Assessing these areas validates a candidate’s ability to create sophisticated applications that can interpret and generate language.

The exam also probes Machine Translation, Information Extraction, Text Summarization, and Topic Modeling. Machine Translation converts text between languages, while Information Extraction pulls structured data from unstructured text. Text Summarization condenses large documents, and Topic Modeling identifies prominent themes within a text collection. Measuring these skills ensures candidates are ready to tackle complex challenges in language understanding and automation.

Full list of covered topics

The actual topics of the questions in the final test will depend on your job description and requirements. However, here's a list of topics you can expect the questions for Natural Language Processing (NLP) Online Test to be based on.

Tokenization

Stop words

Stemming

Lemmatization

Part-of-speech tagging

N-grams

Bag-of-words

TF-IDF

Text classification algorithms

Naive Bayes

Support Vector Machines

Neural networks

Sentiment analysis methods

Lexicon-based approach

Machine learning-based approach

Named entity recognition techniques

Rule-based methods

Conditional random fields

Word embeddings

Word2Vec

GloVe

FastText

Language modeling techniques

N-gram models

Recurrent Neural Networks (RNN)

Seq2Seq models

Machine translation approaches

Statistical machine translation

Neural machine translation

Information extraction methods

Named entity extraction

Relation extraction

Text summarization algorithms

Extraction-based summarization

Abstractive summarization

Topic modeling algorithms

Latent Dirichlet Allocation (LDA)

Latent Semantic Analysis (LSA)

Hierarchical Dirichlet Process (HDP)

Document clustering

What roles can I use the Natural Language Processing (NLP) Test for?

NLP Engineer
Machine Learning Engineer
Artificial Intelligence Researcher
Business Analyst
NLP Research Scientist

How is the Natural Language Processing (NLP) Test customized for senior candidates?

For senior-level NLP roles, the assessment centers around the practical application of advanced techniques and a candidate's capability to solve complex, real-world problems. These questions go beyond basic definitions, focusing on algorithm implementation, hyperparameter optimization for text classification, designing and deploying sentiment analysis pipelines, or fine-tuning large language models for a specific task. Emphasis is placed on the candidate's ability to choose the right tools and techniques for a given challenge, demonstrating a deep understanding of the trade-offs involved.

Preview this test

View sample scorecard

Try the most advanced candidate assessment platform

AI Cheating Detection with Honestly

ChatGPT Protection

Non-googleable Questions

Web Proctoring

IP Proctoring

Webcam Proctoring

MCQ Questions

Coding Questions

Typing Questions

Personality Questions

Custom Questions

Ready-to-use Tests

Custom Tests

Custom Branding

Bulk Invites

Public Links

ATS Integrations

Multiple Question Sets

Custom API integrations

Role-based Access

Priority Support

GDPR Compliance

Screen candidates in 3 easy steps

Pick a test from over 500+ tests

The Adaface test library features 500+ tests to enable you to test candidates on all popular skills- everything from programming languages, software frameworks, devops, logical reasoning, abstract reasoning, critical thinking, fluid intelligence, content marketing, talent acquisition, customer service, accounting, product management, sales and more.

Invite your candidates with 2-clicks

Make informed hiring decisions

Get started for free

Preview questions

Have questions about the Natural Language Processing (NLP) Hiring Test?

What is the Natural Language Processing (NLP) Online Test?

The NLP Online Test evaluates a candidate's proficiency in various NLP skills. It is designed for recruiters to assess and identify individuals who have expertise in NLP tasks. This test is beneficial for hiring roles that require robust NLP knowledge.

Can I combine the NLP Online Test with a Python Test?

Yes, recruiters can request a custom test combining NLP with Python skills. Refer to our Python Online Test for more details on how we assess Python capabilities.

What topics are evaluated in the NLP Online Test?

The test covers Tokenization, Text Classification, Sentiment Analysis, Named Entity Recognition, Word Embeddings, Language Modeling, Machine Translation, Information Extraction, Text Summarization, and Topic Modeling.

How to use the NLP Online Test in my hiring process?

We recommend using the NLP Online Test as a pre-screening tool. Include the test link in your job post or directly invite candidates via email. This enhances your recruitment efficiency by identifying skilled candidates early.

What are the main Data Science tests?

Key tests in the Data Science category include:

Can I combine multiple skills into one custom assessment?

Yes, absolutely. Custom assessments are set up based on your job description, and will include questions on all must-have skills you specify. Here's a quick guide on how you can request a custom test.

Do you have any anti-cheating or proctoring features in place?

We have the following anti-cheating features in place:

Hidden AI Tools Detection with Honestly
Non-googleable questions
IP proctoring
Screen proctoring
Web proctoring
Webcam proctoring
Plagiarism detection
Secure browser
Copy paste protection

Read more about the proctoring features.

How do I interpret test scores?

The primary thing to keep in mind is that an assessment is an elimination tool, not a selection tool. A skills assessment is optimized to help you eliminate candidates who are not technically qualified for the role, it is not optimized to help you find the best candidate for the role. So the ideal way to use an assessment is to decide a threshold score (typically 55%, we help you benchmark) and invite all candidates who score above the threshold for the next rounds of interview.

What experience level can I use this test for?

Each Adaface assessment is customized to your job description/ ideal candidate persona (our subject matter experts will pick the right questions for your assessment from our library of 10000+ questions). This assessment can be customized for any experience level.

Does every candidate get the same questions?

Yes, it makes it much easier for you to compare candidates. Options for MCQ questions and the order of questions are randomized. We have anti-cheating/ proctoring features in place. In our enterprise plan, we also have the option to create multiple versions of the same assessment with questions of similar difficulty levels.

I'm a candidate. Can I try a practice test?

No. Unfortunately, we do not support practice tests at the moment. However, you can use our sample questions for practice.

What is the cost of using this test?

You can check out our pricing plans.

Can I get a free trial?

Yes, you can sign up for free and preview this test.

I just moved to a paid plan. How can I request a custom assessment?

Here is a quick guide on how to request a custom assessment on Adaface.

View sample scorecard

Along with scorecards that report the performance of the candidate in detail, you also receive a comparative analysis against the company average and industry standards.

View sample scorecard