Full-text search engine concept in Elasticsearch - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this Elasticsearch query?

Given the following Elasticsearch query, what will be the total number of hits returned?

Elasticsearch

{
  "query": {
    "match": {
      "description": "quick brown fox"
    }
  }
}

A The query returns documents only if the description field contains the word "quick" and "fox" but not "brown".

B The query returns all documents containing the exact phrase "quick brown fox".

C The query returns documents that do not contain any of the words "quick", "brown", or "fox".

D The query returns documents containing any of the words "quick", "brown", or "fox" in the description field.

💡 Hint

Remember that match queries analyze the text and look for any matching terms.

The match query analyzes the input text and searches for documents containing any of the words. It does not require the exact phrase.

🧠 Conceptual

intermediate

Which Elasticsearch component is responsible for scoring documents?

In Elasticsearch, which component calculates how relevant a document is to a search query?

A The Scoring Algorithm (e.g., BM25)

B The Inverted Index

C The Document Store

D The Query Parser

💡 Hint

Think about how Elasticsearch ranks documents by relevance.

The scoring algorithm, such as BM25, calculates a relevance score for each document based on the query terms and document content.

🔧 Debug

advanced

Why does this Elasticsearch query return zero hits?

Consider this query that returns zero hits even though documents exist with the term "Elasticsearch" in the title:

{
  "query": {
    "term": {
      "title": "Elasticsearch"
    }
  }
}

Why does it return zero hits?

A Because the <code>term</code> query only works on numeric fields, not text fields.

B Because the query syntax is invalid and causes a parsing error.

C Because the <code>term</code> query does not analyze the input and the field is analyzed, so the exact term "Elasticsearch" does not match the indexed tokens.

D Because the field "title" does not exist in the index mapping.

💡 Hint

Think about how term queries differ from match queries regarding analysis.

The term query looks for exact terms without analyzing the input. If the field is analyzed (broken into tokens and lowercased), the exact term "Elasticsearch" may not exist as is in the index.

📝 Syntax

advanced

Which option is a valid Elasticsearch bool query combining must and must_not?

Choose the correct JSON syntax for a bool query that must match "python" in the content field and must not match "java" in the content field.

A { "bool": { "must": [ { "match": { "content": "python" } } ], "must_not": [ { "match": { "content": "java" } } ] } }

B { "bool": { "must": { "match": { "content": "python" } }, "must_not": [ { "match": { "content": "java" } } ] } }

C { "bool": { "must": { "match": { "content": "python" } }, "must_not": { "match": { "content": "java" } } } }

D { "bool": { "must": [ { "match": { "content": "python" } } ], "must_not": { "match": { "content": "java" } } } }

💡 Hint

Remember that must and must_not expect arrays of queries.

The must and must_not clauses require arrays of query objects, even if there is only one query.

🚀 Application

expert

How to implement a phrase search with slop in Elasticsearch?

You want to find documents where the phrase "quick fox" appears in the text field, but you want to allow up to 2 words between "quick" and "fox". Which query achieves this?

A { "match": { "text": { "query": "quick fox", "slop": 2 } } }

B { "match_phrase": { "text": { "query": "quick fox", "slop": 2 } } }

C { "term": { "text": "quick fox" } }

D { "match_phrase": { "text": { "query": "quick fox", "boost": 2 } } }

💡 Hint

Use the query type that supports phrase matching with slop.

The match_phrase query supports the slop parameter to allow words between the phrase terms. The match query does not support slop.