Autocomplete with edge n-gram in Elasticsearch - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this edge n-gram tokenizer example?

Given the following Elasticsearch analyzer configuration using edge n-gram tokenizer, what tokens will be generated for the input text "search"?

Elasticsearch

{
  "settings": {
    "analysis": {
      "tokenizer": {
        "edge_ngram_tokenizer": {
          "type": "edge_ngram",
          "min_gram": 2,
          "max_gram": 4,
          "token_chars": ["letter"]
        }
      },
      "analyzer": {
        "edge_ngram_analyzer": {
          "tokenizer": "edge_ngram_tokenizer"
        }
      }
    }
  }
}

A ["se", "sea", "sear"]

B ["se", "sea", "sear", "searc", "search"]

C ["se", "sea", "sear", "ea", "ear", "earc", "ar", "arc", "arch", "rc", "rch", "ch"]

D ["s", "se", "sea", "sear"]

💡 Hint

Edge n-gram tokenizer generates tokens starting from the beginning of the word with lengths between min_gram and max_gram.

The edge n-gram tokenizer with min_gram=2 and max_gram=4 generates tokens starting from the first character with lengths 2, 3, and 4. For 'search', these are 'se', 'sea', and 'sear'.

🧠 Conceptual

intermediate

Which option best describes the purpose of edge n-gram tokenizer in autocomplete?

What is the main reason to use an edge n-gram tokenizer in an autocomplete feature?

A To remove stop words and reduce index size.

B To split text into sentences for better indexing.

C To generate tokens from the middle of words to improve search accuracy.

D To generate tokens starting from the beginning of words to match prefixes quickly.

💡 Hint

Think about how autocomplete suggests words as you type from the start.

Edge n-gram tokenizer creates tokens starting from the beginning of words, which helps match user input prefixes efficiently in autocomplete.

🔧 Debug

advanced

Why does this edge n-gram analyzer not produce any tokens?

Given this analyzer configuration, why does the input text "hello" produce no tokens?

Elasticsearch

{
  "settings": {
    "analysis": {
      "tokenizer": {
        "my_edge_ngram": {
          "type": "edge_ngram",
          "min_gram": 3,
          "max_gram": 5,
          "token_chars": ["digit"]
        }
      },
      "analyzer": {
        "my_analyzer": {
          "tokenizer": "my_edge_ngram"
        }
      }
    }
  }
}

A Because the min_gram is larger than the input length.

B Because the tokenizer type is invalid.

C Because token_chars is set to 'digit' but input contains only letters.

D Because max_gram is less than min_gram.

💡 Hint

Check the token_chars setting and the input text characters.

The tokenizer only creates tokens from characters classified as digits, but the input 'hello' contains only letters, so no tokens are generated.

📝 Syntax

advanced

Which option correctly defines an edge n-gram tokenizer with min_gram 1 and max_gram 3?

Select the valid Elasticsearch tokenizer configuration for edge n-gram with min_gram 1 and max_gram 3.

A { "type": "edgeNGram", "minGram": 1, "maxGram": 3 }

B { "type": "edge_ngram", "min_gram": 1, "max_gram": 3 }

C { "type": "edge_ngram", "minGram": 1, "maxGram": 3 }

D { "type": "edge_ngram", "min_gram": "1", "max_gram": "3" }

💡 Hint

Check the correct property names and value types for edge n-gram tokenizer.

The correct property names are 'min_gram' and 'max_gram' with integer values. The type is 'edge_ngram' all lowercase with underscore.

🚀 Application

expert

How many tokens are generated by this edge n-gram tokenizer for input 'data'?

Given this tokenizer configuration, how many tokens will be generated for the input text "data"?

{
  "type": "edge_ngram",
  "min_gram": 1,
  "max_gram": 3,
  "token_chars": ["letter"]
}

A 3

B 4

C 2

D 1

💡 Hint

Count tokens starting from the first letter with lengths 1 to 3.

Tokens generated are 'd' (length 1), 'da' (length 2), and 'dat' (length 3), totaling 3 tokens.