Question 224 of 988

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

Quick Answer

The correct answer is that the indexer lacks a skillset or field mapping to populate the 'content' field from the blob. Without a skillset or explicit field mapping, Azure AI Search does not automatically map raw blob content into the index’s 'content' field unless a parsing mode like JSON or delimited text is specified. This is a common pitfall: the indexer runs successfully because the connection and container are valid, but no documents are indexed because the blob’s binary data is never extracted into the target field. On the AI-102 exam, this scenario tests your understanding of how indexers, skillsets, and field mappings interact—specifically that blob content is not automatically ingested into a 'content' field without a parsing mode or a skillset to perform extraction. A frequent trap is assuming a successful run means data was indexed; the key is to verify field mappings and skillset configuration. Memory tip: "No skillset, no content—blobs stay silent without a mapping assignment."

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

This AI-102 practice question tests your understanding of implement knowledge mining and information extraction solutions. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.

{
  "dataSource": {
    "name": "blob-datasource",
    "type": "azureblob",
    "credentials": {
      "connectionString": "DefaultEndpointsProtocol=https;AccountName=myaccount;AccountKey=...;EndpointSuffix=core.windows.net"
    },
    "container": {
      "name": "documents"
    }
  },
  "index": {
    "name": "docs-index",
    "fields": [
      {"name":"id","type":"Edm.String","key":true,"searchable":false},
      {"name":"content","type":"Edm.String","searchable":true},
      {"name":"metadata_storage_name","type":"Edm.String","searchable":true}
    ]
  },
  "indexer": {
    "name": "docs-indexer",
    "dataSourceName": "blob-datasource",
    "targetIndexName": "docs-index",
    "parameters": {
      "batchSize": 10,
      "maxFailedItems": -1
    }
  }
}

You review the configuration for an Azure AI Search indexer. The indexer runs successfully but no documents are indexed. 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.

Question 1easymultiple choice

Full question →

Exhibit

Refer to the exhibit.

{
  "dataSource": {
    "name": "blob-datasource",
    "type": "azureblob",
    "credentials": {
      "connectionString": "DefaultEndpointsProtocol=https;AccountName=myaccount;AccountKey=...;EndpointSuffix=core.windows.net"
    },
    "container": {
      "name": "documents"
    }
  },
  "index": {
    "name": "docs-index",
    "fields": [
      {"name":"id","type":"Edm.String","key":true,"searchable":false},
      {"name":"content","type":"Edm.String","searchable":true},
      {"name":"metadata_storage_name","type":"Edm.String","searchable":true}
    ]
  },
  "indexer": {
    "name": "docs-indexer",
    "dataSourceName": "blob-datasource",
    "targetIndexName": "docs-index",
    "parameters": {
      "batchSize": 10,
      "maxFailedItems": -1
    }
  }
}

A
The connection string in the data source is invalid
Why wrong: The indexer runs successfully, so connection is fine.
B
The container name is incorrect
Why wrong: Indexer runs, so container exists.
C
The indexer does not have a skillset or field mappings to populate the 'content' field from the blob
Without a skillset or field mappings, the blob's content is not extracted into the content field.
D
The maxFailedItems parameter is set to -1, which causes the indexer to skip all documents
Why wrong: -1 means no limit on failed items, but indexer still indexes successful ones.

Full breakdown with real-world context →

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 indexer does not have a skillset or field mappings to populate the 'content' field from the blob

Option A is correct because the indexer does not specify a skillset or a field mapping to extract content; without a skillset, the blob content is not automatically mapped to the 'content' field unless default mappings exist. But here, the default content field mapping is not explicitly set, and the index expects a 'content' field but the blob's content is not automatically mapped if the indexer is not using a parsing mode. Option B is wrong because the connection string is valid. Option C is wrong because the container name is correct. Option D is wrong because maxFailedItems = -1 allows all failures.

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 connection string in the data source is invalid
Why it's wrong here
The indexer runs successfully, so connection is fine.
✗
The container name is incorrect
Why it's wrong here
Indexer runs, so container exists.
✓
The indexer does not have a skillset or field mappings to populate the 'content' field from the blob
Why this is correct
Without a skillset or field mappings, the blob's content is not extracted into the content field.
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.
✗
The maxFailedItems parameter is set to -1, which causes the indexer to skip all documents
Why it's wrong here
-1 means no limit on failed items, but indexer still indexes successful ones.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.

Detailed technical explanation

How to think about this question

This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.

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.
Use explanations to understand the rule behind the answer.

TExam Day Tips

Underline the problem statement mentally.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.

What to study next

Got this wrong? Here's your next step.

Identify which AI-102 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.

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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 indexer does not have a skillset or field mappings to populate the 'content' field from the blob — Option A is correct because the indexer does not specify a skillset or a field mapping to extract content; without a skillset, the blob content is not automatically mapped to the 'content' field unless default mappings exist. But here, the default content field mapping is not explicitly set, and the index expects a 'content' field but the blob's content is not automatically mapped if the indexer is not using a parsing mode. Option B is wrong because the connection string is valid. Option C is wrong because the container name is correct. Option D is wrong because maxFailedItems = -1 allows all failures.

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

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.

About these practice questions

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Last reviewed: Jun 20, 2026

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