ARA-C01 Snowflake Architecture 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 metadata layer is overloaded and cannot provide pruning information.

  Why it's wrong here

  The metadata layer is highly scalable and rarely becomes a bottleneck for pruning. If the query profile shows many partitions being scanned despite clustering, the issue is more likely related to how the data is being accessed or cached rather than the metadata layer failing to provide the necessary pruning statistics.

• ✓

  The warehouse cache is cold or the data exceeds the local cache capacity.

  Why this is correct

  High Remote Disk I/O is a hallmark of a cold cache or a cache that is too small for the dataset. When the SSDs on the compute nodes don't contain the required micro-partitions, Snowflake must retrieve them from the remote cloud storage, which is significantly slower than reading from the local cache.

• ✗

  The table has too many small files, causing metadata bloat.

  Why it's wrong here

  While many small files can impact performance, Snowflake automatically manages micro-partition size (aiming for 16MB compressed). High Remote Disk I/O specifically refers to the latency of fetching data from remote object storage, which is more directly impacted by cache misses than by the total count of metadata entries.

• ✗

  The clustering depth is 1.0, which indicates the table is perfectly clustered.

  Why it's wrong here

  A clustering depth of 1.0 is the ideal state and would actually minimize the number of micro-partitions scanned. This would reduce Remote Disk I/O because fewer files would need to be fetched. Therefore, a perfectly clustered table cannot be the cause of high I/O; it is the solution to it.