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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 er designet og valideret af brancheeksperter til at hjælpe tech -rekrutterere og ansættelsesledere med at vurdere kandidatens NLP -færdigheder. Top tech -virksomheder bruger vores NLP -test til at reducere kandidatscreeningstiden med 85%.

Testen er designet til at filtrere kandidater til roller som

Dataforsker NLP Engineer Junior NLP -udvikler Deep Learning Engineer

Testen sikrer, at kandidater har følgende træk:

  • Evne til at skrive genanvendelig kode
  • Evne til at designe NLP -applikationer
  • Erfaring med at uddanne den udviklede model og driftsevalueringseksperimenter
  • Fortrolighed med at udvide ML -biblioteker og rammer for at anvende i NLP -opgaver
  • Fremragende viden om Python/ R -programmeringssprog
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.

Dette er kun en lille prøve fra vores bibliotek med 10.000+ spørgsmål. De faktiske spørgsmål om dette Natural Language Processing (NLP) Online Test vil være ikke-gåbart.

🧐 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

Med Adaface var vi i stand til at optimere vores indledende screeningsproces med op til 75%, hvilket frigør dyrebar tid for både ansættelsesledere og vores talentindsamlingsteam!


Brandon Lee, Leder af mennesker, 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


Om NLP -udviklerrollen

Naturlig sprogbehandling, normalt forkortet som NLP, er en gren af ​​kunstig intelligens, der beskæftiger sig med samspillet mellem computere og mennesker ved hjælp af det naturlige sprog. NLP er en måde at udtrække mening fra menneskeligt sprog for at tage beslutninger baseret på informationen. Denne teknologi udvikler sig stadig, men der er allerede mange utrolige måder, hvor naturlig sprogbehandling bruges i dag.

Dagens maskiner kan analysere flere sprogbaserede data end mennesker, uden træthed og på en konsekvent, objektiv måde. I betragtning af den svimlende mængde ustrukturerede data, der er genereret hver dag, fra medicinske poster til sociale medier, vil automatisering være kritisk for fuldt ud at analysere tekst- og taledata effektivt.

NLP -ingeniører er ansvarlige for udvikling og design af sprogforståelsessystemer og for effektiv brug af tekstrepræsentationsteknikker.

Typisk ansvar for en NLP -udvikler inkluderer:

  • Analyser, design og implementer strategier for søgemaskiner i naturligt sprog og strategier for dataekstraktion
  • Byg, tog, forfin og tilpas NLP -modeller
  • Brug viden i nye teknikker, teknologier, standarder og forretningstrends til at rådgive om potentielle fordele og påvirkninger samt til at designe og udvikle løsninger
  • Definer passende datasæt til sprogindlæring
  • Træn den udviklede model og køre evalueringseksperimenter
  • Oprethold NLP -biblioteker og rammer

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
Dimensionalitetsreduktion
Dele-af-tale-tagging
Stemming
Informationsindhentning
Sentimentanalyse
Stop ord
Tekstekstraktion
Sprogmodeller
Tokenisering
Algoritmer til sætningsklassificering
Dimensionalitetsreduktion i NLP
Enkeltværdi nedbrydning
Informationsindhentning
Aktiveringsfunktioner
Optimerere
Singapore government logo

De ansættelsesledere mente, at de gennem de tekniske spørgsmål, som de stillede under panelinterviewene, kunne fortælle, hvilke kandidater der havde bedre scoringer, og differentierede med dem, der ikke scorede så godt. De er meget tilfreds med kvaliteten af ​​kandidater, der er på listen med Adaface -screeningen.


85%
Reduktion i screeningstid

FAQS

Kan jeg kombinere flere færdigheder i en brugerdefineret vurdering?

Ja absolut. Brugerdefinerede vurderinger er oprettet baseret på din jobbeskrivelse og vil omfatte spørgsmål om alle must-have-færdigheder, du angiver.

Har du nogen anti-cheating eller proctoring-funktioner på plads?

Vi har følgende anti-cheating-funktioner på plads:

  • Ikke-gåbare spørgsmål
  • IP Proctoring
  • Webproctoring
  • Webcam Proctoring
  • Detektion af plagiering
  • Sikker browser

Læs mere om Proctoring Features.

Hvordan fortolker jeg testresultater?

Den primære ting at huske på er, at en vurdering er et elimineringsværktøj, ikke et udvælgelsesværktøj. En færdighedsvurdering er optimeret for at hjælpe dig med at eliminere kandidater, der ikke er teknisk kvalificerede til rollen, den er ikke optimeret til at hjælpe dig med at finde den bedste kandidat til rollen. Så den ideelle måde at bruge en vurdering på er at beslutte en tærskelværdi (typisk 55%, vi hjælper dig med benchmark) og inviterer alle kandidater, der scorer over tærsklen for de næste interviewrunder.

Hvilken oplevelsesniveau kan jeg bruge denne test til?

Hver Adaface -vurdering tilpasses til din jobbeskrivelse/ ideel kandidatperson (vores emneeksperter vælger de rigtige spørgsmål til din vurdering fra vores bibliotek på 10000+ spørgsmål). Denne vurdering kan tilpasses til ethvert erfaringsniveau.

Får hver kandidat de samme spørgsmål?

Ja, det gør det meget lettere for dig at sammenligne kandidater. Valgmuligheder for MCQ -spørgsmål og rækkefølgen af ​​spørgsmål randomiseres. Vi har anti-cheating/proctoring funktioner på plads. I vores virksomhedsplan har vi også muligheden for at oprette flere versioner af den samme vurdering med spørgsmål om lignende vanskelighedsniveauer.

Jeg er kandidat. Kan jeg prøve en øvelsestest?

Nej. Desværre understøtter vi ikke praksisforsøg i øjeblikket. Du kan dog bruge vores eksempler på spørgsmål til praksis.

Hvad er omkostningerne ved at bruge denne test?

Du kan tjekke vores prisplaner.

Kan jeg få en gratis prøve?

Ja, du kan tilmelde dig gratis og forhåndsvise denne test.

Jeg flyttede lige til en betalt plan. Hvordan kan jeg anmode om en brugerdefineret vurdering?

Her er en hurtig guide til hvordan man anmoder om en brugerdefineret vurdering på adaface.

customers across world
Join 1200+ companies in 75+ countries.
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40 min tests.
No trick questions.
Accurate shortlisting.
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