Create a Materialized View, Including Knowing When to Use Streaming Tables and Materialized Views, and Differentiate Between Dynamic and Materialized Views. DATA-ANALYST-ASSOCIATE Practice Questions (Page 1)

Questions 1–5

1. 1foundation · easy

   Which statement best describes how a materialized view differs from a regular view in Databricks SQL?

   • AA materialized view can only be created on streaming data.
   • BA materialized view stores data physically, while a regular view does not.
   • CA regular view is faster because it always reads the latest data.
   • DA regular view requires more storage than a materialized view.
   Reveal Solution Discussion Select an answer first
2. 2application · medium

   A data analyst needs to create a streaming table that reads from a Kafka source and writes the raw events to a Delta table. The analyst wants to use Databricks SQL. Which statement should the analyst use?

   • ACREATE TABLE raw_events AS SELECT * FROM kafka.`topic`;
   • BCREATE VIEW raw_events AS SELECT * FROM kafka.`topic`;
   • CCREATE MATERIALIZED VIEW raw_events AS SELECT * FROM kafka.`topic`;
   • DCREATE STREAMING TABLE raw_events AS SELECT * FROM kafka.`topic`;
   Reveal Solution Discussion Select an answer first
4. 3application · easy

   A data analyst needs to create a view that provides a filtered subset of a large table for a specific team. The team queries this view frequently, and the underlying table is updated hourly. The analyst wants to ensure the view always reflects the latest data without storing a copy. Which type of view should the analyst create?

   • AA streaming table that continuously updates from the source table.
   • BA materialized view with an hourly refresh.
   • CA dynamic view that filters the table.
   • DA regular table that is refreshed hourly by a scheduled job.
   Reveal Solution Discussion Select an answer first
5. 4foundation · easy

   Which consideration is important when using streaming tables?

   • AThey may incur ongoing compute costs for continuous processing
   • BThey provide exactly-once processing guarantees without any overhead
   • CThey are only suitable for batch data
   • DThey automatically optimize storage costs to zero
   Reveal Solution Discussion Select an answer first
6. 5foundation · easy

   Which scenario is best suited for a materialized view rather than a streaming table?

   • AAn IoT pipeline that ingests sensor data continuously
   • BA daily sales report that aggregates data from a batch-loaded table
   • CA log analysis pipeline that processes events as they are generated
   • DA real-time dashboard that must show data within seconds of arrival
   Reveal Solution Discussion Select an answer first