ARA-C01 Data Engineering Practice Question
A retail company ingests JSON clickstream events into a Snowflake table using Snowpipe streaming. The events contain a nested field 'user' with subfields 'id' and 'name'. The architect needs to query only the 'id' subfield without scanning the entire JSON. Which approach is most efficient?
⚠ Common exam trap
The trap here is assuming that VARIANT with dot notation is always the best for JSON, but for frequent selective subfield access, a dedicated column can be far more efficient.
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
✓
Load the JSON into a table with a separate column for user_id, extracted during ingestion.
Extracting the required subfield into a dedicated column during ingestion is the most efficient method because it eliminates the need to parse the entire JSON document at query time. This approach leverages Snowflake's columnar storage and allows for better compression and pruning, resulting in faster queries and lower compute costs. It is ideal when a specific subfield is frequently queried.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Load the JSON into a table with a separate column for user_id, extracted during ingestion.
Why this is correct
Extracting the needed subfield into a dedicated column during ingestion allows Snowflake to store and query it as a native data type (e.g., VARCHAR or NUMBER). This avoids parsing the entire JSON at query time, reduces I/O, and enables better compression and pruning. It is the most efficient for frequent, selective access to a specific subfield.
- ✗
Use a VARIANT column and query with a dot notation, e.g., SELECT user:id FROM events.
Why it's wrong here
Storing JSON in a VARIANT column and querying with dot notation is convenient but not the most efficient for selective subfield access. VARIANT stores the entire JSON document, and extracting a subfield often requires parsing the whole object, leading to higher I/O and compute. For optimal performance with frequent subfield queries, a more structured approach is recommended.
- ✗
Use a VARIANT column and create a materialized view that selects user:id.
Why it's wrong here
Materialized views in Snowflake can improve performance for repeated queries, but they have limitations, such as not supporting certain expressions like dot notation on VARIANT. Additionally, materialized views incur storage and maintenance costs. For a single subfield, extracting it into a column is simpler and more efficient than maintaining a materialized view.
- ✗
Use a VARIANT column and query with a lateral flatten, e.g., SELECT value:id FROM events, LATERAL FLATTEN(input => user).
Why it's wrong here
LATERAL FLATTEN is designed for exploding arrays or objects into multiple rows, not for simple subfield extraction. Using it to access a single subfield adds unnecessary complexity and overhead, as it processes the entire JSON structure. For direct subfield access, dot notation or a dedicated column is more appropriate.
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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
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.