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

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.
{
    "@odata.context": "https://mysearch.search.windows.net/$metadata#indexers/$entity",
    "name": "blob-indexer",
    "dataSourceName": "blob-datasource",
    "targetIndexName": "myindex",
    "skillsetName": "my-skillset",
    "schedule": {
        "interval": "PT1H",
        "startTime": "2025-01-01T00:00:00Z"
    },
    "parameters": {
        "batchSize": 10,
        "maxFailedItems": 5,
        "maxFailedItemsPerBatch": 2
    },
    "fieldMappings": [],
    "outputFieldMappings": [],
    "status": "running"
}

You have the above indexer configuration. The indexer processes a batch of 10 documents. In that batch, 3 documents fail. What happens?

Exhibit

Refer to the exhibit.
{
    "@odata.context": "https://mysearch.search.windows.net/$metadata#indexers/$entity",
    "name": "blob-indexer",
    "dataSourceName": "blob-datasource",
    "targetIndexName": "myindex",
    "skillsetName": "my-skillset",
    "schedule": {
        "interval": "PT1H",
        "startTime": "2025-01-01T00:00:00Z"
    },
    "parameters": {
        "batchSize": 10,
        "maxFailedItems": 5,
        "maxFailedItemsPerBatch": 2
    },
    "fieldMappings": [],
    "outputFieldMappings": [],
    "status": "running"
}

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 fails the entire batch but continues with the next batch.

Option D is correct because Azure AI Search indexers use a batch-level failure policy: if the number of failed documents in a batch exceeds the configured 'maxFailedItems' threshold (default 0), the entire batch is marked as failed and skipped, but the indexer continues processing subsequent batches. This behavior is controlled by the indexer's 'maxFailedItems' and 'maxFailedItemsPerBatch' properties, which default to 0, meaning any failure in a batch causes the batch to be skipped while the indexer moves on.

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 indexer skips the failed documents and continues with the same batch.

    Why it's wrong here

    The per-batch limit stops the entire batch, not skip individual documents.

  • ✗

    The indexer stops completely because 3 documents failed.

    Why it's wrong here

    The per-batch limit is 2, so the batch fails but the indexer can continue.

  • ✗

    The indexer retries the failed documents.

    Why it's wrong here

    No automatic retry of failed documents within the same batch.

  • ✓

    The indexer fails the entire batch but continues with the next batch.

    Why this is correct

    maxFailedItemsPerBatch=2 causes the batch to abort; overall limit of 5 allows subsequent batches.

    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 assume individual document failures are silently skipped (Option A) or that any failure stops the entire indexer (Option B), but the actual behavior depends on the configurable 'maxFailedItems' and 'maxFailedItemsPerBatch' thresholds, which cause the batch to fail but allow the indexer to continue if cumulative failures are within limits.

Detailed technical explanation

How to think about this question

Under the hood, Azure AI Search indexers process documents in batches (default size 1000, configurable via 'batchSize'). The 'maxFailedItems' (default 0) and 'maxFailedItemsPerBatch' (default 0) properties control failure tolerance: if the number of failed items in a batch exceeds 'maxFailedItemsPerBatch', the entire batch is skipped; if cumulative failures exceed 'maxFailedItems', the indexer run stops. In this scenario, with 3 failures in a batch of 10 and default thresholds of 0, the batch is skipped, but the indexer continues with the next batch because cumulative failures (3) have not yet exceeded 'maxFailedItems' (0? Actually, 3 > 0, so the indexer would stop—this reveals a nuance: the question implies the indexer continues, so the thresholds must be configured differently, e.g., 'maxFailedItems' set to a higher value. The correct behavior per documentation: if 'maxFailedItems' is 0, any failure stops the indexer; thus, for the indexer to continue, the configuration must have non-zero thresholds. The exam expects understanding that batch failure does not stop the entire run unless cumulative failures exceed the limit.

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 indexer fails the entire batch but continues with the next batch. — Option D is correct because Azure AI Search indexers use a batch-level failure policy: if the number of failed documents in a batch exceeds the configured 'maxFailedItems' threshold (default 0), the entire batch is marked as failed and skipped, but the indexer continues processing subsequent batches. This behavior is controlled by the indexer's 'maxFailedItems' and 'maxFailedItemsPerBatch' properties, which default to 0, meaning any failure in a batch causes the batch to be skipped while the indexer moves on.

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.

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.