ARA-C01 Data Engineering Practice Question
A data engineering team needs to transform raw JSON events into a curated table that refreshes automatically as new data arrives, without writing or scheduling any orchestration code. The target must reflect changes within a defined lag and be queryable like a regular table. Which Snowflake feature should the architect recommend?
⚠ Common exam trap
The trap here is assuming that a stream plus a materialized view provides automatic refresh, when neither can persist a transformed curated table without additional orchestration.
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 Dynamic Table defined with a target lag, which Snowflake refreshes automatically based on the query definition and the specified lag.
Dynamic Tables let the architect declare the transformation and a target lag, and Snowflake handles scheduling, dependency tracking, and incremental refresh automatically. The result is a queryable table that stays current without external orchestration. Tasks require manual logic, streams and views do not persist a curated result, and standard views have no refresh mechanism.
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 task with a schedule that runs a CREATE OR REPLACE TABLE AS SELECT statement every minute to rebuild the curated table from scratch.
Why it's wrong here
This requires writing and maintaining orchestration logic, and rebuilding the entire table every minute is wasteful and costly compared to incremental refresh. It also does not natively track dependencies or lag; the schedule is fixed regardless of data arrival. While a task can perform the transformation, it does not meet the requirement of automatic refresh without orchestration code.
- ✗
A view with a scheduled refresh policy and a query acceleration service enabled to keep the result current.
Why it's wrong here
Standard views in Snowflake are virtual and always evaluate the underlying query at run time; they have no refresh policy and cannot be scheduled. Query acceleration service speeds up eligible scans but does not create or maintain a materialized result. This option misrepresents how views work and does not deliver automatic refresh of a curated table.
- ✗
A stream on the raw table plus a materialized view over the stream to expose the transformed rows as they arrive.
Why it's wrong here
Streams capture change data but do not transform or persist a curated result on their own, and materialized views cannot be defined over a stream or express arbitrary transformations. Combining them does not yield an automatically refreshed table. The architect would still need additional logic to apply the changes, which violates the no-orchestration requirement.
- ✓
A Dynamic Table defined with a target lag, which Snowflake refreshes automatically based on the query definition and the specified lag.
Why this is correct
Dynamic Tables are declarative: the architect defines the transformation as a query and a target lag, and Snowflake schedules and executes refreshes automatically as base data changes. No external orchestration is required, and the result is queryable like a normal table. This matches the requirement for automatic, lag-bounded refresh of a curated table.
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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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