DEA-C02 Data Transformation Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  The number of columns included in the SELECT list of the source query.

  Why it's wrong here

  While selecting more columns increases data movement, the primary bottleneck for MERGE operations is the join execution. The number of columns has a negligible impact on performance compared to the cost of locating rows to be updated or inserted, which depends on join efficiency.

• ✓

  The clustering of the target table on the join column used in the MERGE statement.

  Why this is correct

  Clustering on the join column allows the query optimizer to prune partitions that do not contain matching keys. This significantly reduces the amount of data read from storage, which is the most expensive part of a MERGE operation on large datasets.

• ✗

  The size of the virtual warehouse, as larger warehouses always make MERGE operations faster.

  Why it's wrong here

  Scaling up a warehouse does not always improve MERGE performance if the underlying data scan is inefficient. If the query must scan the entire table, a larger warehouse just incurs higher costs without necessarily optimizing the join mechanism itself.

• ✗

  The use of an explicit transaction block around the MERGE statement.

  Why it's wrong here

  While transaction blocks are useful for atomicity, they do not impact the underlying execution performance of the MERGE operation. The bottleneck remains the data retrieval and matching process, which is determined by clustering and join strategy rather than transaction management.