Testing analyzers (_analyze API) in Elasticsearch - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output tokens from this _analyze request?

Given the following Elasticsearch _analyze API request using the standard analyzer, what tokens will be produced?

Elasticsearch

{
  "analyzer": "standard",
  "text": "Quick Brown Foxes"
}

A ["quick", "brown", "foxes"]

B ["Quick", "Brown", "Foxes"]

C ["quick brown foxes"]

D ["quick", "brown", "fox"]

💡 Hint

The standard analyzer lowercases and splits on whitespace and punctuation.

The standard analyzer breaks text into tokens by whitespace and punctuation, then lowercases all tokens. So 'Quick Brown Foxes' becomes ['quick', 'brown', 'foxes'].

❓ Predict Output

intermediate

What tokens does the whitespace analyzer produce?

Using the _analyze API with the whitespace analyzer on the text "Hello, World! Welcome to Elasticsearch.", what tokens are returned?

Elasticsearch

{
  "analyzer": "whitespace",
  "text": "Hello, World! Welcome to Elasticsearch."
}

A ["hello", "world", "welcome", "to", "elasticsearch"]

B ["Hello", "World", "Welcome", "to", "Elasticsearch"]

C ["Hello,", "World!", "Welcome", "to", "Elasticsearch."]

D ["Hello,", "World", "Welcome", "to", "Elasticsearch"]

💡 Hint

Whitespace analyzer splits only on whitespace and does not lowercase or remove punctuation.

The whitespace analyzer splits text only on spaces and does not lowercase or remove punctuation, so tokens keep original casing and punctuation.

❓ Predict Output

advanced

What is the output tokens with a custom analyzer using lowercase and stop filters?

Given this _analyze request with a custom analyzer that uses the standard tokenizer, lowercase filter, and stop filter with stopwords ["the", "is"], what tokens are produced for the text "The quick brown fox is fast"?

Elasticsearch

{
  "tokenizer": "standard",
  "filter": ["lowercase", "stop"],
  "text": "The quick brown fox is fast"
}

A ["quick", "brown", "fox", "fast"]

B ["the", "quick", "brown", "fox", "is", "fast"]

C ["quick", "brown", "fox", "is", "fast"]

D ["The", "quick", "brown", "fox", "fast"]

💡 Hint

Stop filter removes common stopwords after lowercasing.

The standard tokenizer splits words, lowercase filter lowercases all tokens, and stop filter removes 'the' and 'is'. So tokens are ['quick', 'brown', 'fox', 'fast'].

❓ Predict Output

advanced

What error does this _analyze request produce?

What error will Elasticsearch return when running this _analyze request with an invalid tokenizer name?

Elasticsearch

{
  "tokenizer": "nonexistent_tokenizer",
  "text": "Test text"
}

A 500 Internal Server Error

B 400 Bad Request with error 'tokenizer [nonexistent_tokenizer] not found'

C 200 OK with tokens ["test", "text"]

D 404 Not Found

💡 Hint

Elasticsearch returns a 400 error when a requested tokenizer is missing.

If the tokenizer name is invalid, Elasticsearch returns a 400 Bad Request with a message indicating the tokenizer was not found.

🧠 Conceptual

expert

How many tokens are produced by this _analyze request with a pattern tokenizer?

Using the _analyze API with a pattern tokenizer that splits on commas and spaces (pattern: '[,\s]+') on the text "apple, banana orange,pear", how many tokens are produced?

Elasticsearch

{
  "tokenizer": {
    "type": "pattern",
    "pattern": "[,\s]+"
  },
  "text": "apple, banana orange,pear"
}

A 2

B 3

C 5

D 4

💡 Hint

Count tokens split by commas or spaces.

The pattern splits on commas or spaces, so the text splits into ['apple', 'banana', 'orange', 'pear'], which is 4 tokens.