ARA-C01 Data Engineering Practice Question
A data engineer is designing a batch transformation pipeline using Dynamic Tables. The source table is updated hourly, and the target Dynamic Table must reflect changes within 30 minutes. The transformation involves a complex join and aggregation. Which approach best meets the freshness requirement while minimizing cost?
⚠ Common exam trap
The trap here is assuming that a larger warehouse or multi-cluster warehouse is always better; the key is to match warehouse size to the refresh workload and monitor to adjust.
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
✓
Set the target lag to 30 minutes and use a dedicated virtual warehouse sized based on the complexity of the transformation, monitoring and adjusting as needed.
Dynamic Tables refresh automatically based on the target lag. The warehouse size must be adequate to complete the refresh within the lag. Starting with a size based on transformation complexity and monitoring allows tuning to meet freshness without overspending. This approach balances performance and cost effectively.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set the target lag to 30 minutes and use a dedicated virtual warehouse sized as XSMALL.
Why it's wrong here
An XSMALL warehouse may be too small for a complex join and aggregation, causing refreshes to exceed the target lag. If the refresh takes longer than 30 minutes, the Dynamic Table will not meet the freshness requirement. The warehouse size should be tuned to ensure the refresh completes within the lag.
- ✓
Set the target lag to 30 minutes and use a dedicated virtual warehouse sized based on the complexity of the transformation, monitoring and adjusting as needed.
Why this is correct
The target lag defines the maximum acceptable delay. The warehouse size should be chosen to ensure the refresh completes within that lag. Starting with a moderate size and monitoring refresh duration allows you to adjust up or down, balancing performance and cost. This iterative approach is recommended for Dynamic Tables because refresh time depends on data volume and transformation complexity.
- ✗
Set the target lag to 30 minutes and use a multi-cluster warehouse with a minimum of 1 and maximum of 3 clusters.
Why it's wrong here
Multi-cluster warehouses are designed for concurrency, not for reducing refresh time of a single Dynamic Table refresh. A single refresh runs on one cluster, so additional clusters do not speed up the transformation. This adds cost without addressing the performance of the refresh operation.
- ✗
Set the target lag to 30 minutes and use a dedicated virtual warehouse sized as LARGE.
Why it's wrong here
Setting the target lag to 30 minutes is correct, but using a LARGE warehouse may be overkill and increases cost. The warehouse size should be chosen based on the complexity of the transformation and data volume. A smaller warehouse might suffice, and Snowflake automatically manages refreshes. Over-provisioning leads to unnecessary credit consumption.
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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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