Design and Implement Scalable Data Models Using Delta Lake to Manage Large Datasets. DATA-ENGINEER-PROFESSIONAL Practice Questions (Page 2)

Questions 6–10

1. 6application · medium

   A team manages a Delta Lake table that stores customer profiles. The schema has columns `customer_id`, `name`, and `email`. A new data source includes a `phone` column. The team wants to add this column while ensuring that any future writes with unexpected columns are rejected. What should they do?

   • ARun `ALTER TABLE ADD COLUMNS` to add the `phone` column
   • BEnable `spark.databricks.delta.schema.autoMerge.enabled` to true
   • CUse `MERGE` with schema evolution to add the column
   • DDrop and recreate the table with the new schema
   Reveal Solution Discussion Select an answer first
2. 7expert · hard

   A data platform team manages a large Delta Lake table that stores IoT sensor readings. The table is partitioned by `date` and `hour`. Queries often filter on `device_id` and `sensor_type`. The team has noticed that queries are slow and want to improve performance. They are considering partitioning by `device_id` or Z-ordering. What should they do?

   • APartition by `sensor_type` and `device_id`
   • BZ-order by `device_id` and `sensor_type` while keeping the current partitioning
   • CPartition by `device_id` instead of `date` and `hour`
   • DRun VACUUM to reduce the table size
   Reveal Solution Discussion Select an answer first
4. 8application · medium

   A media company stores user clickstream data in a Delta Lake table partitioned by `date`. Queries often filter on `user_id` and `campaign_id`. The team notices that queries scan many files even when filters are selective. They want to improve data skipping without changing the partitioning. What should they do?

   • AAdd `user_id` as a partition column
   • BRun ANALYZE TABLE to collect statistics on all columns
   • CRun OPTIMIZE with ZORDER BY (user_id, campaign_id)
   • DIncrease the number of files by writing with a smaller target file size
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5. 9foundation · easy

   A Delta Lake table stores event data partitioned by `event_date`. After several months, the data engineer notices that queries are slower and many files are smaller than 1 MB. Which action is most directly aimed at improving file sizes and query performance?

   • ARun `OPTIMIZE` on the table
   • BRun `VACUUM` to delete old files
   • CAdd a new partition column
   • DIncrease the number of partitions
   Reveal Solution Discussion Select an answer first
6. 10application · medium

   A logistics company has a Delta Lake table that stores shipment tracking events. The table is partitioned by `event_date` and receives frequent small batch writes throughout the day. Over time, the table has accumulated many small files, causing slow queries. The team wants to improve query performance and reduce file count. What should they do?

   • ADisable Delta Lake transaction logs to reduce overhead
   • BIncrease the number of partitions by partitioning on `event_type` as well
   • CRun OPTIMIZE to compact small files into larger ones
   • DRun VACUUM to delete old files and reduce storage
   Reveal Solution Discussion Select an answer first