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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:

  • Algorithms for sentence classification
  • Word2Vec
  • Dimensionality reduction in NLP
  • Singular Value Decomposition
See all covered skills

9 reasons why
9 reasons why

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



Reason #1

Tests for on-the-job skills

Natural Language Processing (NLP) online test is designed and validated by industry experts to help tech recruiters and hiring managers assess NLP skills of the candidate. Top tech companies are using our NLP test to reduce candidate screening time by 85%.

The test is designed to filter out candidates for roles like

Data Scientist NLP Engineer Junior NLP Developer Deep Learning Engineer

The test ensures candidates have following traits:

  • Ability to write reusable code
  • Ability to design NLP applications
  • Experience with training the developed model and running evaluation experiments
  • Familiarity with extending ML libraries and frameworks to apply in NLP tasks
  • Excellent knowledge of Python/ R programming languages
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.

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.

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.
🧐 Question🔧 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
🧐 Question🔧 Skill💪 Difficulty⌛ 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.

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

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


About The NLP Developer Role

Natural Language Processing, usually shortened as NLP, is a branch of artificial intelligence that deals with the interaction between computers and humans using the natural language. NLP is a way to extract meaning from human language to make decisions based on the information. This technology is still evolving, but there are already many incredible ways natural language processing is used today.

Today’s machines can analyze more language-based data than humans, without fatigue and in a consistent, unbiased way. Considering the staggering amount of unstructured data that’s generated every day, from medical records to social media, automation will be critical to fully analyze text and speech data efficiently.

NLP Engineers are responsible for the development and design of language understanding systems and for the effective use of text representation techniques.

Typical responsibilities of an NLP Developer include:

  • Analyze, design and implement strategies for search engines in Natural Language and strategies for data extraction
  • Build, train, refine and customise NLP models
  • Use knowledge in new techniques, technologies, standards and business trends to advise about potential benefits and impacts as well as to design and develop solutions
  • Define appropriate datasets for language learning
  • Train the developed model and run evaluation experiments
  • Maintain NLP libraries and frameworks

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

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

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

Word2Vec
Parsing
Word sense disambiguation
Dimensionality reduction
Parts-of-speech tagging
Stemming
Information Retrieval
Sentiment analysis
Stop Words
Text extraction
Language models
Tokenization
Algorithms for sentence classification
Dimensionality reduction in NLP
Singular Value Decomposition
Information Retrieval
Activation Functions
Optimizers
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

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.

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
  • Web proctoring
  • Webcam proctoring
  • Plagiarism detection
  • Secure browser

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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