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

9 reasons why
9 reasons why

Adaface Natural Language Processing (NLP) Assessment Test is the most accurate way to shortlist NLP Engineers



Reason #1

Tests for on-the-job skills

The Natural Language Processing (NLP) Test helps recruiters and hiring managers identify qualified candidates from a pool of resumes, and helps in taking objective hiring decisions. It reduces the administrative overhead of interviewing too many candidates and saves time by filtering out unqualified candidates at the first step of the hiring process.

The test screens for the following skills that hiring managers look for in candidates:

  • Ability to tokenize text effectively
  • Skill in classifying text into different categories
  • Capability to analyze sentiment in text
  • Proficiency in recognizing named entities in text
  • Expertise in utilizing word embeddings
  • Proficiency in building language models
  • Skill in translating text from one language to another
  • Ability to extract information from text
  • Expertise in generating text summaries
  • Skill in performing topic modeling
Reason #2

No trick questions

no trick questions

Traditional assessment tools use trick questions and puzzles for the screening, which creates a lot of frustration among candidates about having to go through irrelevant screening assessments.

View sample questions

The main reason we started Adaface is that traditional pre-employment assessment platforms are not a fair way for companies to evaluate candidates. At Adaface, our mission is to help companies find great candidates by assessing on-the-job skills required for a role.

Why we started Adaface
Reason #3

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 10,000+ 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
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
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:
 image
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
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
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.
🧐 QuestionπŸ”§ Skill

Medium

Hate Speech Detection Challenge
Text Classification
Data Imbalance

2 mins

Natural Language Processing
Solve

Easy

Identifying Fake Reviews
Text Classification

2 mins

Natural Language Processing
Solve

Medium

Sentence probability
N-Grams
Language Models

2 mins

Natural Language Processing
Solve

Easy

Tokenization and Stemming
Stemming

2 mins

Natural Language Processing
Solve

Medium

Word Sense Disambiguation

2 mins

Natural Language Processing
Solve
🧐 QuestionπŸ”§ SkillπŸ’ͺ DifficultyβŒ› Time
Hate Speech Detection Challenge
Text Classification
Data Imbalance
Natural Language Processing
Medium2 mins
Solve
Identifying Fake Reviews
Text Classification
Natural Language Processing
Easy2 mins
Solve
Sentence probability
N-Grams
Language Models
Natural Language Processing
Medium2 mins
Solve
Tokenization and Stemming
Stemming
Natural Language Processing
Easy2 mins
Solve
Word Sense Disambiguation
Natural Language Processing
Medium2 mins
Solve
Reason #4

1200+ customers in 75 countries

customers in 75 countries
Brandon

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

Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment Natural Language Processing (NLP) Online Test in your hiring process is that it is an elimination tool, not a selection tool. In other words: you want to use the test to eliminate the candidates who do poorly on the test, not to select the candidates who come out at the top. While they are super valuable, pre-employment tests do not paint the entire picture of a candidate’s abilities, knowledge, and motivations. Multiple easy questions are more predictive of a candidate's ability than fewer hard questions. Harder questions are often "trick" based questions, which do not provide any meaningful signal about the candidate's skillset.

Science behind Adaface tests
Reason #6

1 click candidate invites

Email invites: You can send candidates an email invite to the Natural Language Processing (NLP) Online Test from your dashboard by entering their email address.

Public link: You can create a public link for each test that you can share with candidates.

API or integrations: You can invite candidates directly from your ATS by using our pre-built integrations with popular ATS systems or building a custom integration with your in-house ATS.

invite candidates
Reason #7

Detailed scorecards & benchmarks

View sample scorecard
Reason #8

High completion rate

Adaface tests are conversational, low-stress, and take just 25-40 mins to complete.

This is why Adaface has the highest test-completion rate (86%), which is more than 2x better than traditional assessments.

test completion rate
Reason #9

Advanced Proctoring


Learn more

About the Natural Language Processing (NLP) Online Test

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

The Natural Language Processing (NLP) Online Test makes use of scenario-based questions to test for on-the-job skills as opposed to theoretical knowledge, ensuring that candidates who do well on this screening test have the relavant skills. The questions are designed to covered following on-the-job aspects:

  • Understanding and applying tokenization techniques
  • Implementing text classification algorithms
  • Analyzing and interpreting sentiment in text
  • Identifying and extracting named entities
  • Utilizing word embeddings for natural language tasks
  • Building language models for text generation
  • Translating text between languages using machine translation
  • Extracting valuable information from unstructured text
  • Creating concise summaries of textual data
  • Discovering topics and patterns in text through topic modeling

Once the test is sent to a candidate, the candidate receives a link in email to take the test. For each candidate, you will receive a detailed report with skills breakdown and benchmarks to shortlist the top candidates from your pool.

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

  • Tokenization

    Tokenization is the process of splitting a text or sentence into individual tokens or words. It is an essential step in NLP tasks as it provides a structured representation of textual data, making it easier for further processing and analysis.

  • Text Classification

    Text classification involves assigning pre-defined categories or labels to textual data based on its content. This skill is important in NLP to automatically categorize large volumes of text, enabling efficient information retrieval and organization.

  • Sentiment Analysis

    Sentiment analysis aims to determine the emotional tone or sentiment expressed in a piece of text, whether it is positive, negative, or neutral. This skill is valuable for understanding consumer opinions, social media sentiment, and customer feedback.

  • Named Entity Recognition

    Named Entity Recognition involves identifying and classifying named entities, such as names, dates, locations, and organizations, within a text. This skill helps extract valuable information and relations from unstructured text, aiding in tasks like information extraction and knowledge graph generation.

  • Word Embeddings

    Word embeddings are vector representations of words that capture semantic and syntactic relationships. This skill enables the encoding of text into numerical vectors, facilitating machine learning algorithms to understand the meaning and context of words.

  • Language Modeling

    Language modeling involves predicting the next word in a sequence based on the previous words. It is essential in applications like speech recognition, machine translation, and autocomplete, as it helps generate coherent and contextually appropriate text.

  • Machine Translation

    Machine translation refers to the automatic translation of text or speech from one language to another. This skill is crucial for breaking down language barriers, enabling communication and information exchange across different cultures and regions.

  • Information Extraction

    Information extraction involves automatically extracting structured information from unstructured text. This skill aids in tasks like extracting personal details from resumes, extracting facts from news articles, and organizing information for knowledge graph construction.

  • Text Summarization

    Text summarization is the process of condensing a large amount of text into a shorter and concise summary while preserving the essential information. This skill is useful for generating executive summaries, providing a quick overview of lengthy documents or articles.

  • Topic Modeling

    Topic modeling is a statistical method that identifies latent topics within a collection of documents. This skill helps discover hidden patterns and themes in text data, enabling tasks like content recommendation, document clustering, and trend analysis.

  • 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 intermediate/ experienced candidates, we customize the assessment questions to include advanced topics and increase the difficulty level of the questions. This might include adding questions on topics like

  • Designing and developing NLP-based applications
  • Applying advanced techniques for text preprocessing
  • Optimizing NLP models for performance and scalability
  • Handling large-scale text datasets
  • Building and deploying NLP pipelines
  • Developing algorithms for text similarity and clustering
  • Improving model accuracy through data augmentation
  • Implementing deep learning models for NLP
  • Performing data cleaning and preprocessing for NLP tasks
  • Analyzing and understanding linguistic features in text
Singapore government logo

The hiring managers felt that through the technical questions that they asked during the panel interviews, they were able to tell which candidates had better scores, and differentiated with those who did not score as well. They are highly satisfied with the quality of candidates shortlisted with the Adaface screening.


85%
reduction in screening time

Natural Language Processing (NLP) Hiring Test FAQs

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:

  • 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.

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