Relevance score (_score) in Elasticsearch - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the _score of a match query with exact term match?

Given the following Elasticsearch query, what will be the _score of the matched document relative to a document that does not contain the term?

Elasticsearch

{
  "query": {
    "match": {
      "title": "Elasticsearch"
    }
  }
}

A Null because _score is not returned by default

B -1 because the term is matched negatively

C A positive float value indicating relevance, higher than zero

D Always zero because match queries do not calculate _score

💡 Hint

Think about how Elasticsearch scores documents based on term frequency and inverse document frequency.

Elasticsearch assigns a positive _score to documents matching the query. The score reflects how well the document matches the query terms. Documents without the term get no score or zero relevance.

🧠 Conceptual

intermediate

How does Elasticsearch calculate the _score for a multi-match query?

In Elasticsearch, when you use a multi-match query across multiple fields, how is the _score for each document computed?

A It ignores field matches and assigns a fixed _score

B It sums the individual _scores from each field where the term matches

C It averages the _scores from all fields regardless of matches

D It takes the maximum _score from any single field match

💡 Hint

Consider how relevance from multiple fields contributes to the overall score.

The multi-match query calculates the _score by summing the scores from each field where the query matches, reflecting combined relevance.

🔧 Debug

advanced

Why does this bool query always return zero _score?

Examine the following Elasticsearch query. Why does it always return documents with _score equal to zero?

Elasticsearch

{
  "query": {
    "bool": {
      "filter": [
        { "term": { "status": "active" } },
        { "term": { "category": "books" } }
      ]
    }
  }
}

A Because filters do not contribute to _score, so all scores are zero

B Because term queries inside filter always return zero hits

C Because bool queries require must clauses to calculate _score

D Because the index is empty, so no scores are calculated

💡 Hint

Think about the difference between filter and must clauses in bool queries.

Filters are used for yes/no matching and do not calculate relevance scores, so documents matched by filters have _score zero.

📝 Syntax

advanced

Which query syntax correctly boosts the _score of documents matching 'python'?

Select the Elasticsearch query syntax that correctly boosts the _score of documents containing the term 'python' in the 'description' field.

A { "match": { "description": { "boost": 2.0, "query": "python" } } }

B { "match": { "description": "python", "boost": 2.0 } }

C { "match": { "description": { "query": "python" }, "boost": 2.0 } }

D { "match": { "description": { "query": "python", "boost": 2.0 } } }

💡 Hint

Check the correct placement of the boost parameter inside the match query object.

The boost parameter must be inside the field object along with the query key. Option D correctly places boost inside the description object.

🚀 Application

expert

How to combine function_score with match query to boost recent documents?

You want to boost the _score of documents matching 'data science' in the 'content' field, but also boost documents published in the last 30 days. Which Elasticsearch query achieves this?

A

{
  "function_score": {
    "query": { "match": { "content": "data science" } },
    "functions": [
      {
        "filter": {
          "range": { "publish_date": { "gte": "now-30d/d" } }
        },
        "weight": 3
      }
    ],
    "boost_mode": "multiply"
  }
}

B

{
  "query": {
    "bool": {
      "must": { "match": { "content": "data science" } },
      "should": {
        "range": { "publish_date": { "gte": "now-30d/d" } }
      }
    }
  }
}

C

{
  "function_score": {
    "query": { "match": { "content": "data science" } },
    "boost": 3,
    "filter": {
      "range": { "publish_date": { "gte": "now-30d/d" } }
    }
  }
}

D

{
  "query": {
    "match": {
      "content": {
        "query": "data science",
        "boost": 3
      }
    }
  }
}

💡 Hint

Use function_score to combine query relevance with custom boosting based on a filter.

Option A uses function_score with a match query and a function that boosts documents published in the last 30 days by weight 3, multiplying the scores.