A machine learning team is using pgvector in Azure Database for PostgreSQL to store 1536-dimensional embeddings for a document search feature. Queries are becoming slow as the table grows. Which approach will most directly reduce the compute overhead of vector similarity searches?

Use the pg_similarity extension instead of pgvector.

Increase the number of replicas for the PostgreSQL server.

Store embeddings as JSONB arrays instead of the vector data type.

Create an HNSW index on the embedding column with an appropriate distance function.

Which schema design practice is most important for a table that will store high-volume transactional data in Azure Database for PostgreSQL?

Normalizing data to reduce redundancy

A developer notices that a query filtering on a non-indexed column in a large PostgreSQL table is slow. The query is run frequently and the filter column has high cardinality. Which action should the developer take to reduce query latency?

Create a BRIN index on the filter column.

Create a GIN index on the filter column.

Create a B-tree index on the filter column.

Create a hash index on the filter column.

Microsoft Certified:Azure AI Cloud Developer Associate

Domain 2Objective 2

Develop AI Solutions by Using Azure Database for PostgreSQL AI-200 Practice Questions (Page 7)

Part of the Develop AI solutions by using Azure data management services domain, which accounts for 25–30% of the AI-200 exam. Microsoft does not publish an official question count, but from its 120-minute exam (~50–80 total, ~13–24 in this domain), expect 4–8 from this objective — we provide 48 practice questions to prepare you well beyond it. (estimate)

48questions here

10free pages

13concepts

25–30%of the exam

Questions 31–35

31foundation · easy
Which PostgreSQL feature is designed to store semi-structured or flexible-schema data within a table?
Select an answer first
32application · medium
A machine learning team is using pgvector in Azure Database for PostgreSQL to store 1536-dimensional embeddings for a document search feature. Queries are becoming slow as the table grows. Which approach will most directly reduce the compute overhead of vector similarity searches?
Select an answer first
33foundation · easy
Which schema design practice is most important for a table that will store high-volume transactional data in Azure Database for PostgreSQL?
Select an answer first
34application · medium
A developer notices that a query filtering on a non-indexed column in a large PostgreSQL table is slow. The query is run frequently and the filter column has high cardinality. Which action should the developer take to reduce query latency?
Select an answer first
35foundation · easy
Which SQL clause is used with a pgvector similarity operator to return only the top K most similar vectors?
Select an answer first

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Develop AI Solutions by Using Azure Database for PostgreSQL AI-200 Practice Questions (Page 7)

Questions 31–35

Which PostgreSQL feature is designed to store semi-structured or flexible-schema data within a table?

A machine learning team is using pgvector in Azure Database for PostgreSQL to store 1536-dimensional embeddings for a document search feature. Queries are becoming slow as the table grows. Which approach will most directly reduce the compute overhead of vector similarity searches?

Which schema design practice is most important for a table that will store high-volume transactional data in Azure Database for PostgreSQL?

A developer notices that a query filtering on a non-indexed column in a large PostgreSQL table is slow. The query is run frequently and the filter column has high cardinality. Which action should the developer take to reduce query latency?

Which SQL clause is used with a pgvector similarity operator to return only the top K most similar vectors?

Keep studying — Develop AI solutions by using Azure data management services