ARA-C01 Performance Optimization Practice Question
A data architect is designing a table that will store 10TB of semi-structured JSON data in a VARIANT column. Queries frequently filter on specific JSON attributes using dot notation, such as data:customer_id::string. The architect wants to minimize query latency and storage costs. Which approach should the architect take?
⚠ Common exam trap
The trap here is assuming that enabling Search Optimization Service on a VARIANT column is always the best way to accelerate JSON queries, overlooking its cost and limited applicability.
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
✓
Extract frequently queried JSON attributes into separate relational columns and cluster on those columns.
Extracting frequently queried JSON attributes into native columns and clustering on them allows Snowflake to prune micro-partitions efficiently, reducing the amount of data scanned. This improves query latency and can reduce storage costs because native columns are more compact than VARIANT. Other options either add overhead or do not address the need for efficient filtering on specific attributes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a materialized view that pre-aggregates the JSON data by customer_id.
Why it's wrong here
Materialized views can improve performance for specific aggregations, but they add storage and maintenance costs. They are not suitable for ad-hoc filtering on arbitrary JSON attributes and may not support all query patterns. For point lookups on customer_id, a materialized view would not help unless it exactly matches the query predicates. It is not a general solution.
- ✓
Extract frequently queried JSON attributes into separate relational columns and cluster on those columns.
Why this is correct
Extracting frequently accessed JSON attributes into native columns allows Snowflake to store them in a columnar format, enabling efficient pruning and compression. Clustering on those columns further improves pruning for filtered queries. This approach reduces the amount of data scanned and can lower storage costs because native columns are more efficient than VARIANT. It is the recommended best practice for performance and cost.
- ✗
Enable the Search Optimization Service on the VARIANT column to accelerate point lookups.
Why it's wrong here
Search Optimization Service can accelerate certain queries on VARIANT columns, but it adds storage and maintenance overhead. For 10TB of JSON, enabling it on the entire VARIANT column may be cost-prohibitive and not necessarily improve performance for all access patterns. It is not the most efficient approach for minimizing both latency and storage costs.
- ✗
Store the JSON data in an external table and query it directly with Snowflake.
Why it's wrong here
External tables are read-only and do not support clustering or the same level of pruning as native tables. Querying JSON from external tables can be slower because data is not stored in Snowflake's optimized columnar format. For a 10TB dataset with frequent filtering, this would likely increase latency and not reduce storage costs within Snowflake.
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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
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