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

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

Exhibit

{
  "indexer": {
    "dataSourceName": "blob-datasource",
    "targetIndexName": "knowledge-index",
    "schedule": {
      "interval": "PT1H"
    },
    "parameters": {
      "batchSize": 10,
      "maxFailedItems": 5,
      "maxFailedItemsPerBatch": 5
    }
  }
}

Refer to the exhibit. You have this Azure AI Search indexer configuration. The indexer is failing after processing 6 documents that contain errors. What should you do to ensure the indexer continues processing even if some documents fail?

⚠ Common exam trap

Test-takers frequently confuse batch size with failure tolerance, thinking that reducing batch size will prevent the indexer from stopping, when in fact the `maxFailedItems` parameter is the direct control for how many document failures are tolerated before the indexer halts.

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

✓

Increase maxFailedItems to a higher value, such as 100

The indexer is failing after processing 6 documents because the `maxFailedItems` threshold has been reached. By increasing `maxFailedItems` to a higher value like 100, the indexer will continue processing even if more documents fail, as long as the total number of failed items stays below the new threshold.

Answer analysis

Option-by-option breakdown

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

✗
Decrease the batch size to 5
Why it's wrong here
Smaller batch size may not prevent failures.
✗
Increase batch size to 20
Why it's wrong here
Larger batch size may increase failures.
✓
Increase maxFailedItems to a higher value, such as 100
Why this is correct
Increasing maxFailedItems allows more failures before stopping.
✗
Remove the schedule to run the indexer on demand
Why it's wrong here
Running on demand does not change failure tolerance.

Go deeper

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

Exhibit

Why each option matters

Option-by-option breakdown

Related to this question