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Implement knowledge mining and information extraction solutions →mediumMultiple ChoiceObjective-mapped

Fix Entity Recognition Skill When Input Is an Array Instead of String

This AI-102 practice question tests your understanding of implement knowledge mining and information extraction solutions. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.

Exhibit

Refer to the exhibit.

{
  "name": "my-skillset",
  "skills": [
    {
      "@odata.type": "#Microsoft.Skills.Text.V3.EntityRecognitionSkill",
      "categories": [ "Organization", "Person", "Location" ],
      "defaultLanguageCode": "en",
      "inputs": [ { "name": "text", "source": "/document/content" } ],
      "outputs": [ { "name": "entities", "targetName": "entities" } ]
    },
    {
      "@odata.type": "#Microsoft.Skills.Text.KeyPhraseExtractionSkill",
      "defaultLanguageCode": "en",
      "inputs": [ { "name": "text", "source": "/document/content" } ],
      "outputs": [ { "name": "keyPhrases", "targetName": "keyPhrases" } ]
    }
  ]
}

You have an Azure AI Search skillset defined as shown in the exhibit. When you run the indexer, the enrichment pipeline produces outputs but no entities are extracted. The source documents are in English and contain clear organization and person names. What is the most likely cause?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

  • Clue: "most likely"

    Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.

Exhibit

Refer to the exhibit.

{
  "name": "my-skillset",
  "skills": [
    {
      "@odata.type": "#Microsoft.Skills.Text.V3.EntityRecognitionSkill",
      "categories": [ "Organization", "Person", "Location" ],
      "defaultLanguageCode": "en",
      "inputs": [ { "name": "text", "source": "/document/content" } ],
      "outputs": [ { "name": "entities", "targetName": "entities" } ]
    },
    {
      "@odata.type": "#Microsoft.Skills.Text.KeyPhraseExtractionSkill",
      "defaultLanguageCode": "en",
      "inputs": [ { "name": "text", "source": "/document/content" } ],
      "outputs": [ { "name": "keyPhrases", "targetName": "keyPhrases" } ]
    }
  ]
}

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 '/document/content' field is an array, but the skill expects a string.

The most likely cause is that the '/document/content' field is an array, but the Entity Recognition skill expects a string input. In Azure AI Search, if the source field is an array, the skill cannot process it directly, resulting in no entities being extracted even though the documents contain clear organization and person names.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Answer analysis

Option-by-option breakdown

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

  • ✗

    The skill output is not mapped to the index.

    Why it's wrong here

    Output mapping is not required for skill execution.

  • ✗

    The skills are in the wrong order.

    Why it's wrong here

    Order does not affect entity extraction.

  • ✗

    The documents are not in English.

    Why it's wrong here

    The source is English.

  • ✓

    The '/document/content' field is an array, but the skill expects a string.

    Why this is correct

    EntityRecognitionSkill expects a string input.

    Clue confirmation

    The clue word "most likely" in the question point toward this answer.

    Related concept

    Read the scenario before looking for a memorised answer.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often assume entity extraction fails due to language mismatch or skill ordering, but Azure AI Search's Entity Recognition skill is strict about input types, and an array input will cause silent failure without any error in the pipeline output.

Trap categories for this question

  • Command / output trap

    Output mapping is not required for skill execution.

Detailed technical explanation

How to think about this question

The Entity Recognition skill in Azure AI Search expects a string input for its 'text' field. If the '/document/content' field is an array (e.g., from a split skill or a JSON array source), the skill will fail silently or produce no output because it cannot iterate over array elements. To handle arrays, you must use a 'Shaper' skill or a 'Split' skill to convert the array into individual string values before passing them to the Entity Recognition skill. This is a common misconfiguration when documents contain multiple content segments.

KKey Concepts to Remember

  • Read the scenario before looking for a memorised answer.
  • Find the constraint that changes the correct option.
  • Eliminate answers that are true in general but not in this case.

TExam Day Tips

  • Watch for words such as best, first, most likely and least administrative effort.
  • Review why wrong options are wrong, not only why the correct option is correct.

Key takeaway

Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Real-world example

How this comes up in practice

A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.

What to study next

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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FAQ

Questions learners often ask

What does this AI-102 question test?

Implement knowledge mining and information extraction solutions — This question tests Implement knowledge mining and information extraction solutions — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: The '/document/content' field is an array, but the skill expects a string. — The most likely cause is that the '/document/content' field is an array, but the Entity Recognition skill expects a string input. In Azure AI Search, if the source field is an array, the skill cannot process it directly, resulting in no entities being extracted even though the documents contain clear organization and person names.

What should I do if I get this AI-102 question wrong?

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

Are there clue words in this question I should notice?

Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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Last reviewed: Jul 4, 2026

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This AI-102 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-102 exam.