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1

A production ETL job must deduplicate records by customer_id while retaining the newest record by event_time. Which approach is most appropriate during a production release?

A. Use row_number over a partition by customer_id ordered by event_time descending, then keep row 1 B. Use count(*) grouped by customer_id and keep every group C. Sort the entire table alphabetically and drop the first row D. Use coalesce(1) before reading the source

2

A production ETL job must deduplicate records by customer_id while retaining the newest record by event_time. Which approach is most appropriate for a high-volume enterprise workload?

A. Use row_number over a partition by customer_id ordered by event_time descending, then keep row 1 B. Use count(*) grouped by customer_id and keep every group C. Sort the entire table alphabetically and drop the first row D. Use coalesce(1) before reading the source

3

A production ETL job must deduplicate records by customer_id while retaining the newest record by event_time. Which approach is most appropriate when a retry may replay prior input?

A. Use row_number over a partition by customer_id ordered by event_time descending, then keep row 1 B. Use count(*) grouped by customer_id and keep every group C. Sort the entire table alphabetically and drop the first row D. Use coalesce(1) before reading the source

4

A production ETL job must deduplicate records by customer_id while retaining the newest record by event_time. Which approach is most appropriate while preserving auditability?

A. Use row_number over a partition by customer_id ordered by event_time descending, then keep row 1 B. Use count(*) grouped by customer_id and keep every group C. Sort the entire table alphabetically and drop the first row D. Use coalesce(1) before reading the source

5

A production ETL job must deduplicate records by customer_id while retaining the newest record by event_time. Which approach is most appropriate under a strict data-quality requirement?

A. Use row_number over a partition by customer_id ordered by event_time descending, then keep row 1 B. Use count(*) grouped by customer_id and keep every group C. Sort the entire table alphabetically and drop the first row D. Use coalesce(1) before reading the source

6

A production ETL job must deduplicate records by customer_id while retaining the newest record by event_time. Which approach is most appropriate when cost and latency both matter?

A. A production ETL job must deduplicate records by customer_id while retaining the newest record by event_time. Which approach is most appropriate when cost and latency both matter? B. Use count(*) grouped by customer_id and keep every group C. Sort the entire table alphabetically and drop the first row D. Use coalesce(1) before reading the source

7

A production ETL job must deduplicate records by customer_id while retaining the newest record by event_time. Which approach is most appropriate in a multi-team environment?

A. Use row_number over a partition by customer_id ordered by event_time descending, then keep row 1 B. Use count(*) grouped by customer_id and keep every group C. Sort the entire table alphabetically and drop the first row D. Use coalesce(1) before reading the source

8

A production ETL job must deduplicate records by customer_id while retaining the newest record by event_time. Which approach is most appropriate when upstream schemas evolve?

A. Use row_number over a partition by customer_id ordered by event_time descending, then keep row 1 B. Use count(*) grouped by customer_id and keep every group C. Sort the entire table alphabetically and drop the first row D. Use coalesce(1) before reading the source

9

A production ETL job must deduplicate records by customer_id while retaining the newest record by event_time. Which approach is most appropriate for a governed Unity Catalog deployment?

A. Use row_number over a partition by customer_id ordered by event_time descending, then keep row 1 B. Use count(*) grouped by customer_id and keep every group C. Sort the entire table alphabetically and drop the first row D. Use coalesce(1) before reading the source

10

A production ETL job must deduplicate records by customer_id while retaining the newest record by event_time. Which approach is most appropriate when troubleshooting intermittent failures?

A. Use row_number over a partition by customer_id ordered by event_time descending, then keep row 1 B. Use count(*) grouped by customer_id and keep every group C. Sort the entire table alphabetically and drop the first row D. Use coalesce(1) before reading the source

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A production ETL job must deduplicate records by customer_id while retaining the newest record by event_time. Which approach is most appropriate during a production release?

A production ETL job must deduplicate records by customer_id while retaining the newest record by event_time. Which approach is most appropriate for a high-volume enterprise workload?

A production ETL job must deduplicate records by customer_id while retaining the newest record by event_time. Which approach is most appropriate when a retry may replay prior input?

A production ETL job must deduplicate records by customer_id while retaining the newest record by event_time. Which approach is most appropriate while preserving auditability?

A production ETL job must deduplicate records by customer_id while retaining the newest record by event_time. Which approach is most appropriate under a strict data-quality requirement?

A production ETL job must deduplicate records by customer_id while retaining the newest record by event_time. Which approach is most appropriate when cost and latency both matter?

A production ETL job must deduplicate records by customer_id while retaining the newest record by event_time. Which approach is most appropriate in a multi-team environment?

A production ETL job must deduplicate records by customer_id while retaining the newest record by event_time. Which approach is most appropriate when upstream schemas evolve?

A production ETL job must deduplicate records by customer_id while retaining the newest record by event_time. Which approach is most appropriate for a governed Unity Catalog deployment?

A production ETL job must deduplicate records by customer_id while retaining the newest record by event_time. Which approach is most appropriate when troubleshooting intermittent failures?

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