Module Assessment

1.

You're tuning PostgreSQL for a vector search workload with 2 million 1536-dimensional embeddings. Queries are slow and you observe a cache hit ratio of 85%. Which configuration change should you prioritize?

Increase shared_buffers to keep more data in the PostgreSQL cache

Decrease random_page_cost to encourage more index scans

Increase ivfflat.probes to search more index partitions

2.

You need to create a vector index for a dataset of 5 million product embeddings that receives frequent batch updates (daily full refresh). Build time must be under 30 minutes. Which index configuration should you choose?

IVFFlat with lists set to sqrt(rows)

HNSW with m=16 and ef_construction=64

HNSW with m=8 and ef_construction=32

3.

Your filtered vector search query filters by category_id and then orders by vector similarity. The query plan shows a sequential scan on the products table. What should you check first?

Verify that a B-tree index exists on the category_id column

Verify that the vector index uses the same operator class as the query

Increase hnsw.ef_search to expand the search space

4.

You're implementing connection management for an AI application that makes 500 vector queries per second during peak traffic. Your Azure Database for PostgreSQL instance supports 1,719 max connections. Which approach should you use?

Enable PgBouncer in transaction mode with a pool size appropriate for your application instances

Create a new database connection for each query request

Enable PgBouncer in session mode to maintain persistent connections

5.

You're scaling a recommendation engine that currently runs on a General Purpose 8 vCore instance. CPU utilization averages 75% and P95 query latency is 150 ms, but you need to achieve sub-50ms latency. What scaling approach should you try first?

Upgrade to a Memory Optimized tier with more vCores

Add read replicas to distribute query load

Implement application-level caching with Azure Cache for Redis

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