Why relevance scoring ranks results in Elasticsearch - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to specify the field to search in Elasticsearch.

Elasticsearch

{
  "query": {
    "match": {
      "[1]": "search text"
    }
  }
}

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

Aauthor

Bscore

Cdate

Dtitle

Attempts:

3 left

💡 Hint

Common Mistakes

Using a numeric or date field instead of a text field.

The match query requires the field name to search. Here, title is the correct field to search text in.

2fill in blank

medium

Complete the code to add a boost factor to increase relevance of the field.

Elasticsearch

{
  "query": {
    "match": {
      "title": {
        "query": "search text",
        "[1]": 2
      }
    }
  }
}

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

Aboost

Bscore

Cweight

Dpriority

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect parameter names like 'score' or 'weight' which are not valid here.

The boost parameter increases the importance of the field in scoring.

3fill in blank

hard

Fix the error in the code to correctly use the function_score query for custom scoring.

Elasticsearch

{
  "query": {
    "function_score": {
      "query": { "match_all": {} },
      "[1]": [
        {
          "weight": 2
        }
      ]
    }
  }
}

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

Afilters

Bfunctions

Cboosts

Dscores

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'filters' or 'boosts' which are not valid keys here.

The function_score query uses the functions array to define scoring functions.

4fill in blank

hard

Fill both blanks to create a query that boosts documents with a specific tag.

Elasticsearch

{
  "query": {
    "function_score": {
      "query": { "match": { "content": "search" } },
      "[1]": [
        {
          "filter": { "term": { "tag": "important" } },
          "[2]": 3
        }
      ]
    }
  }
}

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

Afunctions

Bweight

Cboost

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'boost' instead of 'weight' inside functions.

The functions array holds scoring functions, and weight sets the boost value.

5fill in blank

hard

Fill all three blanks to create a query that boosts documents with tag 'vip' and sorts by score descending.

Elasticsearch

{
  "query": {
    "function_score": {
      "query": { "match_all": {} },
      "[1]": [
        {
          "filter": { "term": { "tag": "vip" } },
          "[2]": 5
        }
      ]
    }
  },
  "sort": [
    { "[3]": { "order": "desc" } }
  ]
}

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

Afunctions

Bweight

C_score

Dboost

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'boost' instead of 'weight' inside functions or sorting by a wrong field.

The functions array holds scoring functions, weight sets boost, and sorting by _score orders results by relevance.

Practice

1. What does relevance scoring in Elasticsearch primarily determine?

easy

A. How well a document matches the search query

B. The size of the document

C. The date when the document was created

D. The number of fields in the document

Solution

1. Step 1: Understand relevance scoring purpose

   Relevance scoring measures how closely a document matches the search query terms.

2. Step 2: Compare options to scoring concept

   Only How well a document matches the search query relates to matching quality, others are unrelated to scoring.

3. Final Answer:

   How well a document matches the search query -> Option A

4. Quick Check:

   Relevance scoring = match quality [OK]

Hint: Relevance scoring ranks by match quality, not metadata [OK]

Common Mistakes:

• Confusing scoring with document size
• Thinking scoring depends on creation date
• Assuming number of fields affects score

2. Which Elasticsearch query part directly influences relevance scoring?

easy

A. sort

B. match

C. filter

D. aggs

Solution

1. Step 1: Identify query parts affecting scoring

   The match query scores documents based on how well they match the query text.

2. Step 2: Differentiate from other parts

   filter and aggs do not affect scoring; sort orders results but doesn't calculate score.

3. Final Answer:

   match -> Option B

4. Quick Check:

   Match query = scoring influence [OK]

Hint: Match queries affect scoring; filters do not [OK]

Common Mistakes:

• Thinking filters affect scoring
• Confusing sort with scoring
• Assuming aggregations influence score

3. Given this Elasticsearch query snippet, which document will rank higher?

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

Documents: 1. {"title": "apple pie recipe"} 2. {"title": "banana apple smoothie"} 3. {"title": "fruit salad"}

medium

A. Document 3

B. Document 2

C. Document 1

D. All have the same score

Solution

1. Step 1: Understand match query scoring

   Documents containing the exact term "apple" in the title get higher scores. Document 1 has "apple" at the start and is more focused.

2. Step 2: Compare documents by term presence

   Document 1 has "apple" prominently; Document 2 has "apple" but mixed with "banana"; Document 3 lacks "apple".

3. Final Answer:

   Document 1 -> Option C

4. Quick Check:

   Exact term presence = higher score [OK]

Hint: Exact term in title ranks higher than partial or missing [OK]

Common Mistakes:

• Assuming all documents score equally
• Choosing document without the term
• Ignoring term position and frequency

4. You wrote this Elasticsearch query but results are not ranked by relevance:

{
  "query": {
    "bool": {
      "filter": {
        "match": { "content": "coffee" }
      }
    }
  }
}

What is the main issue?

medium

A. "filter" does not calculate relevance scores

B. The "match" query is misspelled

C. The query must use "must_not" instead of "filter"

D. The field "content" does not exist

Solution

1. Step 1: Understand bool query filter behavior

   The filter clause filters documents but does not affect relevance scoring.

2. Step 2: Identify why scoring is missing

   Using filter with match disables scoring; to rank by relevance, use must instead.

3. Final Answer:

   "filter" does not calculate relevance scores -> Option A

4. Quick Check:

   Filter disables scoring, use must for relevance [OK]

Hint: Use must, not filter, for scoring queries [OK]

Common Mistakes:

• Thinking filter affects scoring
• Assuming match is misspelled
• Ignoring bool query clause differences

5. You want to rank documents by relevance but also exclude those with "draft" status. Which query correctly ranks by relevance and filters out drafts?

{
  "query": {
    "bool": {
      "must": {
        "match": { "text": "Elasticsearch" }
      },
      "filter": {
        "term": { "status": "draft" }
      }
    }
  }
}

hard

A. Use "filter" for both match and filtering drafts

B. Use "filter" for match and "must" for filtering drafts

C. Use "should" for both match and filtering drafts

D. Use "must" for match and "must_not" for filtering drafts

Solution

1. Step 1: Understand must and filter roles

   must runs scoring queries; filter filters without scoring; must_not excludes documents.

2. Step 2: Apply exclusion for drafts

   To exclude drafts, use must_not with a term filter on status "draft"; keep must for scoring match query.

3. Final Answer:

   Use "must" for match and "must_not" for filtering drafts -> Option D

4. Quick Check:

   Must scores, must_not excludes [OK]

Hint: Use must for scoring, must_not to exclude unwanted docs [OK]

Common Mistakes:

• Using filter to exclude documents (doesn't exclude)
• Using should which affects scoring differently
• Filtering match query disables scoring