Relevance score (_score) in Elasticsearch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to retrieve the relevance score (_score) for each search hit.

Elasticsearch

{
  "query": {
    "match": {
      "message": "search text"
    }
  },
  "_source": ["message"],
  "fields": ["[1]"]
}

Drag options to blanks, or click blank then click option'

Arelevance

B_score

Cscore

Drank

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'score' without underscore

Using 'relevance' or 'rank' which are not valid fields

The relevance score is stored in the _score field in Elasticsearch search hits.

2fill in blank

medium

Complete the code to sort search results by their relevance score in descending order.

Elasticsearch

{
  "query": {
    "match_all": {}
  },
  "sort": [
    {"[1]": {"order": "desc"}}
  ]
}

Drag options to blanks, or click blank then click option'

A_score

Bmessage

Ctimestamp

Did

Attempts:

3 left

💡 Hint

Common Mistakes

Sorting by fields like 'message' or 'timestamp' instead of '_score'

Using ascending order which shows least relevant first

Sorting by _score in descending order shows the most relevant results first.

3fill in blank

hard

Fix the error in the query to correctly boost documents with the term 'urgent' to increase their relevance score.

Elasticsearch

{
  "query": {
    "bool": {
      "should": [
        {"match": {"message": "[1]"}},
        {"match": {"message": {"query": "urgent", "boost": 2}}}
      ]
    }
  }
}

Drag options to blanks, or click blank then click option'

Aimportant

Bhigh

Cnormal

Durgent

Attempts:

3 left

💡 Hint

Common Mistakes

Using a different term than 'urgent' in the first match query

Not applying boost correctly

The first match query should search for the main term, here 'urgent', to apply boosting properly.

4fill in blank

hard

Fill both blanks to create a function score query that multiplies the relevance score by 3 for documents with 'priority' in the tag field.

Elasticsearch

{
  "query": {
    "function_score": {
      "query": {"match_all": {}},
      "functions": [
        {
          "filter": {"term": {"tags": "[1]"}},
          "weight": [2]
        }
      ]
    }
  }
}

Drag options to blanks, or click blank then click option'

Apriority

Burgent

C3

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong tag name in filter

Using incorrect weight value

The filter should match the tag 'priority' and the weight should be 3 to multiply the score accordingly.

5fill in blank

hard

Fill all three blanks to build a query that boosts documents containing 'error' in the message field by 5 and sorts results by relevance score descending.

Elasticsearch

{
  "query": {
    "function_score": {
      "query": {"match": {"message": "[1]"}},
      "boost": [2],
      "boost_mode": "multiply"
    }
  },
  "sort": [
    {"[3]": {"order": "desc"}}
  ]
}

Drag options to blanks, or click blank then click option'

Aerror

B5

C_score

Dmessage

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong term in match query

Incorrect boost value

Sorting by wrong field

The query matches 'error', boosts by 5, and sorts by _score descending to prioritize relevant results.

Practice

1. What does the _score field represent in an Elasticsearch search result?

easy

A. The total number of documents in the index

B. The unique identifier of the document

C. The relevance score showing how well a document matches the query

D. The timestamp when the document was indexed

Solution

1. Step 1: Understand the purpose of _score

   The _score is a numeric value assigned by Elasticsearch to each document based on how well it matches the search query.

2. Step 2: Differentiate _score from other fields

   Unlike document ID or timestamps, _score specifically measures relevance, not identity or metadata.

3. Final Answer:

   The relevance score showing how well a document matches the query -> Option C

4. Quick Check:

   _score = relevance [OK]

Hint: Remember: _score means relevance, not ID or count [OK]

Common Mistakes:

• Confusing _score with document ID
• Thinking _score is a timestamp
• Assuming _score counts documents

2. Which of the following is the correct way to access the _score in an Elasticsearch query response JSON?

easy

A. response.score

B. response._score

C. response.hits._score

D. response.hits.hits[0]._score

Solution

1. Step 1: Understand Elasticsearch response structure

   Search results are inside hits.hits array, each element representing a document with its _score.

2. Step 2: Identify correct path to _score

   The _score is a property of each hit object, so it is accessed as hits.hits[0]._score for the first result.

3. Final Answer:

   response.hits.hits[0]._score -> Option D

4. Quick Check:

   Access _score inside hits array [OK]

Hint: Remember: _score is inside each hit in hits.hits array [OK]

Common Mistakes:

• Trying to access _score directly on response
• Using hits._score which is invalid
• Using response.score which does not exist

3. Given this Elasticsearch query snippet, which document will have the highest _score?

{
  "query": {
    "match": {
      "title": "apple banana"
    }
  }
}

Documents: 1. {"title": "apple banana fruit"} 2. {"title": "banana apple"} 3. {"title": "apple orange"} 4. {"title": "fruit banana apple"}

medium

A. Document 1

B. Document 2

C. Document 3

D. Document 4

Solution

1. Step 1: Understand match query scoring

   Elasticsearch scores documents higher when query terms appear closer and in the same order in the field.

2. Step 2: Compare documents for term order and presence

   Document 2 has "banana apple" which matches both terms but reversed order; Document 1 has "apple banana fruit" matching query order exactly; Document 4 has terms but with extra word; Document 3 misses "banana".

3. Step 3: Consider term frequency and order

   Exact order and proximity increase score, so Document 1 scores highest because it matches "apple banana" exactly in order and proximity.

4. Final Answer:

   Document 1 -> Option A

5. Quick Check:

   Exact term order and proximity = highest _score [OK]

Hint: Exact term order and proximity boost _score [OK]

Common Mistakes:

• Ignoring term order effect on score
• Assuming all documents with terms score equally
• Overlooking missing query terms in documents

4. You wrote this Elasticsearch query but all documents have _score equal to 1.0, which is unexpected:

{
  "query": {
    "match_all": {}
  }
}

What is the likely reason for this behavior?

medium

A. The match_all query assigns a constant _score of 1.0 to all documents

B. The index is empty so _score defaults to 1.0

C. You forgot to specify the field in the query

D. The _score is not returned by default

Solution

1. Step 1: Understand match_all query behavior

   The match_all query matches every document equally and assigns a default _score of 1.0 to all.

2. Step 2: Check if this matches the observed behavior

   Since all documents have _score 1.0, it confirms match_all is working as designed.

3. Final Answer:

   The match_all query assigns a constant _score of 1.0 to all documents -> Option A

4. Quick Check:

   match_all = constant _score 1.0 [OK]

Hint: match_all always gives _score 1.0 [OK]

Common Mistakes:

• Thinking _score varies with match_all
• Assuming missing field causes this
• Believing _score is not returned by default

5. You want to boost documents that contain the word "premium" in the description field to increase their _score. Which Elasticsearch query correctly applies a boost to achieve this?

{
  "query": {
    "bool": {
      "should": [
        { "match": { "description": "premium" } },
        { "match": { "description": { "query": "premium", "boost": 2 } } }
      ]
    }
  }
}

hard

A. Use two match queries in should without boost

B. Use a single match with boost inside the field object

C. Use match_all with boost parameter

D. Use term query without boost

Solution

1. Step 1: Understand boosting in Elasticsearch queries

   Boosting is done by adding a boost parameter inside the query clause to increase _score for matching documents.

2. Step 2: Identify correct syntax for boosting

   The correct way is to use a single match query with boost inside the field object, like { "match": { "description": { "query": "premium", "boost": 2 } } }.

3. Step 3: Evaluate given options

   Use a single match with boost inside the field object matches this syntax; other options either lack boost or use wrong query types.

4. Final Answer:

   Use a single match with boost inside the field object -> Option B

5. Quick Check:

   Boost inside match field object increases _score [OK]

Hint: Put boost inside match field object for scoring [OK]

Common Mistakes:

• Using multiple matches without boost
• Applying boost to match_all query
• Using term query without boost