Implement knowledge mining and information extraction solutions →hardMultiple Choice

AI-102 Practice Question: Implement knowledge mining and information extraction solutions

Exhibit

Refer to the exhibit.
{
  "skills": [
    {
      "@odata.type": "#Microsoft.Skills.Text.SplitSkill",
      "name": "split",
      "context": "/document",
      "textSplitMode": "pages",
      "maximumPageLength": 5000,
      "pageOverlapLength": 500,
      "inputs": [
        { "name": "text", "source": "/document/content" }
      ],
      "outputs": [
        { "name": "textItems", "targetName": "pages" }
      ]
    },
    {
      "@odata.type": "#Microsoft.Skills.Text.EntityRecognitionSkill",
      "name": "entities",
      "context": "/document/pages/*",
      "categories": [ "Person", "Organization" ],
      "inputs": [
        { "name": "text", "source": "/document/pages/*" }
      ],
      "outputs": [
        { "name": "entities", "targetName": "entities" }
      ]
    }
  ]
}

You have the above skillset in Azure AI Search. The indexer processes a document with 12,000 characters of content. How many entity recognition skill executions occur?

⚠ Common exam trap

The Azure AI Search entity recognition skill has a maximum text length per execution of 5,000 characters. Candidates might incorrectly divide 12,000 by 5,000 and round down to 2, or assume a single execution can handle the entire document, ignoring the chunking behavior.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

The Azure AI Search entity recognition skill has a maximum text length per execution of 5,000 characters. A document with 12,000 characters is split into chunks of up to 5,000 characters, resulting in three chunks (5,000 + 5,000 + 2,000). Each chunk triggers one skill execution, so three executions occur.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
4
Why it's wrong here
Four executions would require 20,000 characters of capacity, exceeding the document's 12,000 characters. Four is tempting when rounding 12,000 up to the next multiple of 5,000 is miscalculated, but 12,000 characters occupy three pages of 5,000, 5,000 and 2,000.
✗
2
Why it's wrong here
Two executions cover 10,000 characters, leaving 2,000 characters unprocessed. Two is tempting because 12,000 divided by 5,000 rounds near two, but the skill pages input into fixed 5,000-character chunks, so the remainder requires an additional execution.
✓
3
Why this is correct
Entity recognition splits input into 5,000-character chunks, so 12,000 characters yields three executions: two full chunks plus a 2,000-character remainder. This satisfies the stem's chunking constraint, where each skill invocation processes at most 5,000 characters, making three the accurate count.
✗
1
Why it's wrong here
One execution would only cover a single 5,000-character page, leaving the remaining 7,000 characters unprocessed. A single call is tempting when a document is assumed to be handled whole, but the entity recognition skill chunks input at 5,000 characters per invocation.

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AI-102 Practice Question: Implement knowledge mining and information extraction solutions

Exhibit

Why each option matters

Option-by-option breakdown

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