DEA-C02 Storage and Data Protection Practice Question
Exhibit
{
"cluster_by_keys" : "LINEAR(C1, C2)",
"total_partition_count" : 1500,
"total_constant_partition_count" : 100,
"average_overlaps" : 12.5,
"average_depth" : 8.4,
"partition_depth_histogram" : { ... }
}Refer to the exhibit. A data engineer runs SYSTEM$CLUSTERING_INFORMATION on a table. Based on the output, what is the most accurate interpretation of the table's current state?
⚠ Common exam trap
Candidates often misinterpret high clustering depth as a good sign, failing to realize that high values indicate significant overlap and inefficient data pruning, which degrades query performance.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The clustering depth indicates that a significant number of micro-partitions overlap.
The clustering information provides metrics on how well data is grouped within micro-partitions. An average depth of 8.4 and high average overlaps (12.5) relative to the total partitions suggest that the table is not well-clustered for the specified keys. This indicates that queries filtering on C1 and C2 will likely scan more partitions than necessary.
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 table is perfectly clustered because the average depth is less than 10.
Why it's wrong here
An average depth of 8.4 is not considered 'perfect.' In a perfectly clustered table, the depth would be closer to 1.0, meaning that for any given value of the clustering key, only one micro-partition would need to be scanned, greatly improving query performance and data pruning.
- ✓
The clustering depth indicates that a significant number of micro-partitions overlap.
Why this is correct
High average overlaps and a depth of 8.4 indicate that the values for C1 and C2 are scattered across many different micro-partitions. This overlap prevents efficient partition pruning during query execution, as the warehouse must open multiple partitions to find all relevant records for a specific key range.
- ✗
The table does not have a clustering key defined, so the depth is irrelevant.
Why it's wrong here
The exhibit explicitly shows 'cluster_by_keys' as 'LINEAR(C1, C2)', confirming that a clustering key has indeed been defined for this table. The depth and overlap metrics are directly calculated based on this definition to help engineers assess if the Automatic Clustering service is performing effectively.
- ✗
The constant partition count of 100 means the table is mostly read-only.
Why it's wrong here
Constant partitions are those where the clustering key values do not overlap with any other partitions. Having only 100 constant partitions out of 1500 total partitions (less than 7%) actually suggests that the table is poorly clustered and that most data is widely distributed across overlapping blocks.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Snowflake exam blueprint
This DEA-C02 practice question is part of Courseiva's free Snowflake certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C02 exam.