- A
Set the 'searchable' attribute on the description field and define a suggester
Searchable enables full-text search; suggester enables autocomplete.
- B
Set the 'filterable' attribute on the description field
Why wrong: Filterable enables filtering, not fuzzy search.
- C
Set the 'sortable' attribute on the ticket_id field
Why wrong: Sortable enables sorting, not fuzzy or autocomplete.
- D
Set the 'facetable' attribute on the category field
Why wrong: Facetable enables faceted navigation, not fuzzy or autocomplete.
Question 578 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. 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.
You are implementing a knowledge mining solution using Azure AI Search. The data source is a large Azure Cosmos DB collection containing customer support tickets. Each ticket has fields: ticket_id, description, category, and resolution. You need to ensure that the search index can support fuzzy search and autocomplete suggestions. What should you configure in the index definition?
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
✓
Set the 'searchable' attribute on the description field and define a suggester
Option A is correct because fuzzy search requires the 'searchable' attribute on fields to enable full-text search, and autocomplete suggestions require a 'suggester' configured on the index. The suggester defines which fields are used to generate suggestion candidates, and the 'searchable' attribute allows the description field to be tokenized and matched against partial or misspelled queries.
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.
- ✓
Set the 'searchable' attribute on the description field and define a suggester
Why this is correct
Searchable enables full-text search; suggester enables autocomplete.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Set the 'filterable' attribute on the description field
Why it's wrong here
Filterable enables filtering, not fuzzy search.
- ✗
Set the 'sortable' attribute on the ticket_id field
Why it's wrong here
Sortable enables sorting, not fuzzy or autocomplete.
- ✗
Set the 'facetable' attribute on the category field
Why it's wrong here
Facetable enables faceted navigation, not fuzzy or autocomplete.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse 'searchable' with 'filterable' or 'facetable', thinking any attribute that enables querying will also support fuzzy search and autocomplete, but only 'searchable' fields are analyzed and tokenized for these features, and a suggester is a separate required configuration.
Detailed technical explanation
How to think about this question
Fuzzy search in Azure AI Search uses the Levenshtein distance algorithm to find terms within a specified edit distance (default 2), and requires the field to be 'searchable' so it is analyzed and indexed with inverted tokens. A suggester must be defined with a 'searchMode' of 'analyzingInfixMatching' to support autocomplete, and the fields listed in the suggester must also be 'searchable' to allow prefix or infix matching. In real-world scenarios, combining fuzzy search with a suggester on the same field (e.g., description) enables both typo-tolerant querying and real-time suggestion dropdowns in a support portal.
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: Set the 'searchable' attribute on the description field and define a suggester — Option A is correct because fuzzy search requires the 'searchable' attribute on fields to enable full-text search, and autocomplete suggestions require a 'suggester' configured on the index. The suggester defines which fields are used to generate suggestion candidates, and the 'searchable' attribute allows the description field to be tokenized and matched against partial or misspelled queries.
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.
About these practice questions
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Last reviewed: Jul 4, 2026
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