ARA-C01 Performance Optimization 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 query is not using the Result Cache; enable the Result Cache.

  Why it's wrong here

  The result cache is automatically used for identical queries and does not impact query execution logic. If a query is already spilling, it means the processing is memory-intensive. Enabling the result cache does not resolve the resource exhaustion occurring during the actual computation of the query result.

• ✗

  Data is not clustered properly; re-cluster the table.

  Why it's wrong here

  Clustering improves pruning, which reduces the number of micro-partitions scanned. While this reduces total I/O, it does not necessarily prevent memory spills if the join or sort operations still involve large datasets. Memory spilling is typically addressed by scaling up compute resources rather than re-clustering data.

• ✓

  Insufficient memory for operations; increase warehouse size.

  Why this is correct

  Increasing the warehouse size provides more RAM per node, which directly reduces the likelihood of spilling intermediate datasets to disk. When a query is complex, scaling up is the most effective way to provide the memory headroom required for operations like large-scale joins and window functions.

• ✗

  Network latency is high; change the cloud provider region.

  Why it's wrong here

  Remote disk spilling is a resource-level issue related to local memory and compute capacity, not network latency. Changing regions does not provide the local memory required for query execution. Therefore, region selection will not solve the underlying problem of exceeding local compute resources for memory-intensive operations.