ARA-C01 Performance Optimization Practice Question
Under which TWO scenarios should an architect choose to increase the warehouse size (Vertical Scaling) rather than increasing the maximum number of clusters (Horizontal Scaling)?
⚠ Common exam trap
Candidates often choose horizontal scaling assuming it solves all performance issues, confusing high user concurrency with single-query resource bottlenecks that actually require vertical scaling memory upgrades.
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
✓
A single complex query is spilling significant amounts of data to remote storage.
Vertical scaling (larger warehouse) provides more memory and local storage per node, which is essential for complex queries that perform large joins or aggregations and might otherwise spill to disk. Horizontal scaling (multi-cluster) is specifically designed to handle high concurrency, where many different users are submitting queries at the same time.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
A single complex query is spilling significant amounts of data to remote storage.
Why this is correct
When a query spills to remote storage, it means the local memory and SSD on the current warehouse nodes are exhausted. Increasing the warehouse size provides more resources per node, allowing the query to keep more data in memory or local disk, which significantly improves performance compared to remote spilling.
- ✗
The number of concurrent users has increased from 10 to 100.
Why it's wrong here
This scenario is the primary use case for horizontal scaling (multi-cluster warehouses). Adding more clusters allows Snowflake to distribute the high volume of concurrent queries across more compute resources, preventing queuing and ensuring that each user receives a responsive experience without needing a larger T-shirt size.
- ✓
A query involving multiple large table joins is taking too long to execute.
Why this is correct
Complex joins require significant compute power and memory for hash tables. A larger warehouse size increases the number of nodes and the resources available to each node, allowing for better parallelization of the join logic and reducing the overall execution time for a single, resource-intensive query.
- ✗
The organization wants to reduce the time it takes for a warehouse to auto-suspend.
Why it's wrong here
Auto-suspend is a configuration setting that determines how long a warehouse stays active after the last query completes. Changing the warehouse size or adding clusters does not affect this setting. This is a cost-management feature rather than a performance optimization strategy related to scaling types.
- ✗
The dashboard queries are small but are frequently queuing behind each other.
Why it's wrong here
Queuing of small queries is a concurrency issue. Increasing the warehouse size might make each individual query slightly faster, but it won't resolve the bottleneck as effectively as adding more clusters, which allows multiple queries to run in parallel on different clusters within the same warehouse.
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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
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