ARA-C01 Data Engineering Practice Question
An architect is building a Dynamic Table that reads from a base table receiving continuous inserts. The refresh is configured with TARGET_LAG = '1 minute' and the warehouse is a dedicated XSMALL. Monitoring shows refreshes frequently take longer than one minute and sometimes overlap with the next scheduled run. The team wants to reduce refresh latency without changing the query logic. Which change is most appropriate?
⚠ Common exam trap
The trap here is treating the target lag value as a performance guarantee, when it is only a freshness objective that depends on available compute actually finishing each refresh in time.
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
✓
Increase the warehouse size used by the Dynamic Table so that each refresh completes within the target lag window.
Dynamic Table refreshes execute on the warehouse tied to the object, so when a run cannot finish within TARGET_LAG the practical remedy is more compute. Scaling the warehouse up shortens each incremental refresh and keeps runs from overlapping. Changing refresh mode to full, altering initialization behavior, or replacing the object with a task-driven view does not reduce per-run duration and in some cases increases cost or complexity.
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 INITIALIZE to ON_CREATE and recreate the Dynamic Table so that the initial population happens at creation time.
Why it's wrong here
INITIALIZE controls whether the table is populated when it is created, not how subsequent refreshes are scheduled or sized. Recreating the table triggers a fresh initial load and momentarily loses the existing materialization, adding disruption. It has no effect on per-run refresh duration, so the overlap caused by slow refreshes would persist unchanged.
- ✗
Set REFRESH_MODE to FULL on the Dynamic Table so that each run recomputes the entire result set instead of applying incremental changes.
Why it's wrong here
FULL refresh recomputes the whole result on every cycle, which is typically slower and more expensive than incremental processing for a continuously appended base table. It would worsen the latency problem rather than solve it. FULL mode is intended for cases where incremental maintenance is unsupported or incorrect, such as certain non-deterministic constructs, not for accelerating a lagging refresh.
- ✓
Increase the warehouse size used by the Dynamic Table so that each refresh completes within the target lag window.
Why this is correct
When refresh duration exceeds TARGET_LAG, the bottleneck is compute rather than query design. Snowflake refreshes Dynamic Tables on the warehouse associated with them, so scaling that warehouse up shortens each incremental refresh and allows runs to finish inside the one-minute window. This preserves the declarative refresh model and requires no query rewrite, directly resolving the overlap caused by slow runs.
- ✗
Convert the Dynamic Table to a regular view and schedule a task to run every minute to materialize the results into a physical table.
Why it's wrong here
Replacing the Dynamic Table with a view plus a task abandons automatic incremental refresh and dependency tracking. The task would need custom change detection and would likely recompute more data than necessary, increasing cost. It also loses the declarative TARGET_LAG contract, so the team would manage scheduling and overlap manually, which does not address the underlying compute shortfall.
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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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