Certified Data Engineer Professional Exam

An hourly batch job is configured to ingest data files from a cloud object storage container where each batch represent all records produced by the source system in a given hour. The batch job to process these records into the Lakehouse is sufficiently delayed to ensure no late-arriving data is missed. The user_id field represents a unique key for the data, which has the following schema:

user_id BIGINT, username STRING, user_utc STRING, user_region STRING, last_login BIGINT, auto_pay BOOLEAN, last_updated BIGINT

New records are all ingested into a table named account_history which maintains a full record of all data in the same schema as the source. The next table in the system is named account_current and is implemented as a Type 1 table representing the most recent value for each unique user_id.

Which implementation can be used to efficiently update the described account_current table as part of each hourly batch job assuming there are millions of user accounts and tens of thousands of records processed hourly?

Filter records in account_history using the last_updated field and the most recent hour processed, making sure to deduplicate on username; write a merge statement to update or insert the most recent value for each username.

Use Auto Loader to subscribe to new files in the account_history directory; configure a Structured Streaming trigger available job to batch update newly detected files into the account_current table.

Overwrite the account_current table with each batch using the results of a query against the account_history table grouping by user_id and filtering for the max value of last_updated.

Filter records in account_history using the last_updated field and the most recent hour processed, as well as the max last_login by user_id write a merge statement to update or insert the most recent value for each user_id.