ARA-C01 Snowflake Architecture Practice Question
Network Topology
Refer to the exhibit. An architect needs to optimize this warehouse to handle a batch process that performs a massive table scan followed by a complex join on 10 billion rows. What is the most effective architectural change?
⚠ Common exam trap
Candidates often choose 'Multi-cluster warehouse' as the solution, confusing the need for concurrency (more users) with the need for raw performance on a single, massive, complex query (larger warehouse size).
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
✓
Change the size of the warehouse from X-SMALL to LARGE.
For a single, complex query involving a massive data set, the bottleneck is typically the compute power and memory available to the nodes. Increasing the size (vertical scaling) from X-Small to a larger size (e.g., Large or X-Large) provides more CPUs and memory per node. This allows the warehouse to process more data in parallel and reduces the need for spilling to local or remote disk.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the max_cluster_count to 5 to enable multi-cluster scaling.
Why it's wrong here
Increasing the cluster count provides horizontal scaling, which helps with query concurrency (multiple users running queries at once). It does not increase the resources available to a single query. Therefore, a massive scan and join on a single query would not benefit from more clusters; it needs a larger, more powerful single cluster.
- ✓
Change the size of the warehouse from X-SMALL to LARGE.
Why this is correct
Vertical scaling by increasing the warehouse size is the correct architectural approach for improving the performance of large, complex queries. A 'Large' warehouse has 8 nodes compared to the 1 node of an 'X-Small'. This provides more parallel processing power and significantly more memory to handle the massive join operation and prevent data spilling.
- ✗
Set the auto_resume property to TRUE to ensure the warehouse starts automatically.
Why it's wrong here
Setting auto_resume to TRUE ensures the warehouse starts when a query is submitted, but it does not improve the performance of the query once it is running. The exhibit shows the warehouse is currently SUSPENDED, so while auto_resume is necessary for it to work, it is not a performance optimization for a massive data scan.
- ✗
Alter the warehouse type from STANDARD to SNOWPARK-OPTIMIZED.
Why it's wrong here
Snowpark-optimized warehouses are designed for memory-intensive workloads like machine learning training or complex memory-heavy transformations. While they provide more memory, a standard 'Large' warehouse is usually sufficient and more cost-effective for typical SQL-based joins and scans. Standard warehouses are the default architectural choice for most data warehousing tasks.
About these practice questions
Courseiva writes every ARA-C01 question from scratch — 209 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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 ARA-C01 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 ARA-C01 exam.