Your company manages an application that captures stock data in an internal database. You need to create an API that provides real-time stock data to users. You want to return stock data to users as…

Question

Options

• ACreate a BigQuery dataset and table to act as the internal database. Query the table when user
• BCreate a Memorystore for Redis instance to store all stock market data. Query this database
• CCreate a Bigtable instance. Query the table when user requests are received. Configure a
• DCreate a Memorystore for Redis instance, and use this database to store the most accessed

• A
  3% (1)
• B
  12% (4)
• C
  6% (2)
• D
  79% (26)

Explanation

Option D uses Memorystore for Redis as a cache layer for the most frequently accessed stock data, falling back to the internal database for less-common queries. This provides sub-millisecond read latency for hot data while remaining cost-effective and highly scalable. Option B (storing ALL stock data in Redis) is impractical because Redis is an in-memory store - storing the entire stock dataset would be prohibitively expensive and is not how caches are designed to work. Option A (BigQuery) is an analytical data warehouse optimized for large batch queries, not for low-latency real-time API responses. Option C (Bigtable) is better suited for time-series or wide-column analytical workloads, not for a general-purpose real-time API cache layer.

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