NLP Scientist
Role: NLP Scientist
Tests knowledge of language models, the transformer architecture, RAG, fine-tuning and evaluation of NLP systems, along with the reasoning behind technical decisions. Intended for companies building products with generative AI and text processing.
Sample questions
Taken from the template itself. Questions with a recorded answer key are left out, so reading this page cannot help anyone pass the test.
As the candidate sees it
NLP Scientist
- 1.Short text
What is the name of the subword tokenization algorithm, based on iteratively merging the most frequent symbol pairs, used in models such as GPT?
Answer with the name of the algorithm.
- 2.Long text
A RAG system in production is returning answers that cite irrelevant passages or fabricate information. Describe how you would diagnose the problem, separating retrieval failures from generation failures, and which improvements you would try at each stage.
Mention specific metrics and techniques.
- Shown once you copy the template
- Shown once you copy the template
- Shown once you copy the template
5 more questions in the full template.
How this template was made, and what you can check
Are these the real questions?
Yes. The questions on this page are the questions a candidate gets. They are not samples written for the site: they come from the template itself, word for word.
Why are some questions missing from this page?
Any question with a recorded answer key is left out, so that reading this page cannot help anyone pass. The count of what is missing is shown; the questions themselves appear once you copy the template.
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Every template exists in English and Portuguese. The candidate answers in theirs and you read in yours.
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What comes back to you
Short text
What is the name of the subword tokenization algorithm, based on iteratively merging the most frequent symbol pairs, used in models such as GPT?
Answer
Score
Why this score
The question is real. The answer and the score are drawn as an example of what a review looks like.