Exam DP-750 Topic 1 Question 82 Discussion
Actual exam question for Microsoft's DP-750 exam
Question #: 82
Topic #: 1
Question #: 82
Topic #: 1
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains two managed Delta tables named sales.schema1.table1 and sales.schema1.table2.
sales.schema1.table1 contains sales data from the current year.
sales.schema1 .table2 contains historical data.
You need to load all the rows from sales.schema1.table1 into sales.schema1.table2. The solution must preserve any existing data in sales.schema1.table2 and minimize processing effort.
Which command should you run?
sales.schema1.table1 contains sales data from the current year.
sales.schema1 .table2 contains historical data.
You need to load all the rows from sales.schema1.table1 into sales.schema1.table2. The solution must preserve any existing data in sales.schema1.table2 and minimize processing effort.
Which command should you run?
Suggested Answer: C Vote an answer
To load all rows from one table into the other while preserving existing data and minimizing processing effort, you should use the SQL INSERT INTO statement.
Preserves Data: INSERT INTO appends new rows to the target table without modifying or deleting the existing data.
Lowest Processing Effort: It performs a direct data append at the storage level. Unlike MERGE INTO, it does not scan the target table for matches, saving significant compute time and costs.
Delta Lake Optimization: Because these are Delta tables, appending data simply writes new parquet files and commits them to the transaction log, making the operation fast and efficient.
Reference:
https://medium.com/@gema.correa/handling-schema-evolution-and-schema-compensation-in-databricks-lessons-from-the-field-7af8d915beef
Preserves Data: INSERT INTO appends new rows to the target table without modifying or deleting the existing data.
Lowest Processing Effort: It performs a direct data append at the storage level. Unlike MERGE INTO, it does not scan the target table for matches, saving significant compute time and costs.
Delta Lake Optimization: Because these are Delta tables, appending data simply writes new parquet files and commits them to the transaction log, making the operation fast and efficient.
Reference:
https://medium.com/@gema.correa/handling-schema-evolution-and-schema-compensation-in-databricks-lessons-from-the-field-7af8d915beef
by George at Aug 20, 2026, 10:12 PM
0
0
0
10
Comments
Upvoting a comment with a selected answer will also increase the vote count towards that answer by one. So if you see a comment that you already agree with, you can upvote it instead of posting a new comment.
Report Comment
Commenting
You can sign-up / login (it's free).