- A
Configure the skillset to run entity recognition and key phrase extraction on all documents.
Why wrong: This would run on all documents, not just those >1 MB.
- B
Implement a custom skill that checks the document size and only calls the built-in skills if size > 1 MB.
A custom skill can conditionally run built-in skills based on document properties.
- C
Use indexer parameters to specify a document size filter.
Why wrong: Indexer parameters cannot filter by document content size.
- D
Create two indexers: one for large documents with skills, one for small documents without.
Why wrong: This is inefficient and not a standard approach.
Question 983 of 993
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. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
You are implementing a knowledge mining solution using Azure Cognitive Search with built-in AI enrichment. The pipeline must extract named entities and key phrases from documents. The enrichment pipeline should be triggered only for documents that are larger than 1 MB. Which approach should you use?
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
✓
Implement a custom skill that checks the document size and only calls the built-in skills if size > 1 MB.
Option B is correct because Azure Cognitive Search does not natively support document-size-based conditional skill execution. By implementing a custom skill that checks the document size and only invokes the built-in entity recognition and key phrase extraction skills for documents larger than 1 MB, you achieve the required conditional enrichment logic. This approach leverages the custom skill's ability to inspect document metadata and control downstream skill execution.
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.
- ✗
Configure the skillset to run entity recognition and key phrase extraction on all documents.
Why it's wrong here
This would run on all documents, not just those >1 MB.
- ✓
Implement a custom skill that checks the document size and only calls the built-in skills if size > 1 MB.
Why this is correct
A custom skill can conditionally run built-in skills based on document properties.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use indexer parameters to specify a document size filter.
Why it's wrong here
Indexer parameters cannot filter by document content size.
- ✗
Create two indexers: one for large documents with skills, one for small documents without.
Why it's wrong here
This is inefficient and not a standard approach.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume Azure Cognitive Search has a built-in document size filter or conditional skill execution feature, but in reality, such conditional logic requires a custom skill to inspect metadata and control the enrichment pipeline.
Detailed technical explanation
How to think about this question
Custom skills in Azure Cognitive Search are implemented as Azure Functions or web APIs that receive a JSON payload containing the document's content and metadata. The custom skill can parse the document size from the metadata (e.g., from the `metadata_storage_size` field if using Azure Blob Storage) and return a conditional output that controls whether subsequent built-in skills execute. This pattern is essential for cost optimization and performance tuning in large-scale knowledge mining pipelines where enrichment costs scale with document volume.
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
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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: Implement a custom skill that checks the document size and only calls the built-in skills if size > 1 MB. — Option B is correct because Azure Cognitive Search does not natively support document-size-based conditional skill execution. By implementing a custom skill that checks the document size and only invokes the built-in entity recognition and key phrase extraction skills for documents larger than 1 MB, you achieve the required conditional enrichment logic. This approach leverages the custom skill's ability to inspect document metadata and control downstream skill execution.
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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