Meilisearch is a fast, open-source search engine with a rich feature set including vector search and hybrid ranking. These exercises cover the configuration and query patterns you need to build production search experiences.
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At standup, a colleague asks what filterable attributes are in Meilisearch. What is the correct answer?
Filterable attributes are fields you configure in Meilisearch's index settings (filterableAttributes) so the engine builds a filter index for them at indexing time. At query time you use the filter parameter with expressions like brand = 'Nike' AND price < 100 to narrow results. Only fields declared as filterable can be used in filter expressions — using an undeclared field raises an error.
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During a PR review, a teammate asks what sortable attributes configuration enables. Which answer is correct?
Meilisearch requires you to declare sortableAttributes in index settings before you can sort on those fields. At indexing time, Meilisearch builds a sort structure for declared fields. At query time you pass sort: ['price:asc', 'name:desc']. Fields not in sortableAttributes cannot be used in sort. Declaring a field sortable does not automatically make it filterable — those are separate settings.
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In a design review, the team discusses vector search in Meilisearch. A junior engineer asks how it differs from keyword search. What is correct?
Meilisearch's vector search stores embedding vectors in a _vectors field and uses approximate nearest-neighbour (ANN) search to find semantically similar documents. You can use it standalone (pure semantic search) or combine it with keyword search as hybrid search, tuning the balance with the semanticRatio parameter (0 = pure keyword, 1 = pure vector). Embeddings can be generated by Meilisearch (via configured embedders like OpenAI) or provided by your application.
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An incident report shows slow indexing after enabling multiple indexes with overlapping data. A senior engineer asks what multi-search does in Meilisearch. What is correct?
Meilisearch's multi-search endpoint (POST /multi-search) batches multiple search queries into a single HTTP request. Each query in the queries array specifies its own indexUid and search parameters, and results come back as an array in the same order. This is useful for federated search UIs (searching products, articles, and users in one request) and reduces network round-trips compared to making separate calls.
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During a code review, a senior engineer asks what hybrid search in Meilisearch requires to be configured. What is accurate?
To use hybrid search in Meilisearch you must first configure an embedder in the index's embedders settings — specifying the source (openAi, huggingFace, rest, etc.), model, and credentials. Meilisearch then generates and stores vectors for new documents automatically. At query time you pass hybrid: { semanticRatio: 0.5, embedder: 'default' } to blend keyword and semantic scores.
What does the "Meilisearch Search Patterns Vocabulary" vocabulary exercise cover?
This exercise tests real IT vocabulary related to meilisearch search patterns vocabulary through 5 multiple-choice questions, each built from realistic workplace sentences rather than abstract definitions.
Is this vocabulary exercise free to use?
Yes. Every exercise on CoderSlingo, including this one, is completely free — no account, sign-up, or payment required.
How many questions does this exercise have?
This exercise has 5 questions. Each one shows a real-world sentence or scenario with multiple-choice options and an explanation once you answer.
What happens after I answer a question?
You'll see immediate feedback showing whether your answer was correct, along with a short explanation of why — then a button to move to the next question, and a full results screen at the end.
Can I retry the exercise if I get questions wrong?
Yes. Once you reach the results screen, click "Try again" to reset your answers and go through the exercise from the start as many times as you like.
Do I need to create an account to take this exercise?
No account is needed. Your answers are scored in your browser during the session — nothing is saved to a server, so you can jump straight in.
Is my progress saved if I leave the page?
No — progress within an exercise resets if you navigate away or reload. Each exercise is short enough to complete in a few minutes in one sitting.
Are these vocabulary exercises connected to other topics?
Yes — browse the full vocabulary exercises hub to find related modules covering adjacent IT topics and roles.
How is this different from reading a glossary or blog article?
Exercises like this one are active recall drills — you have to choose the correct term or phrasing yourself, which builds retention faster than passively reading a definition.
Where can I find more vocabulary exercises?
Browse the full Vocabulary exercises hub for hundreds of modules covering Agile, DevOps, security, databases, architecture, and more — organised by IT role and skill.