DEA-C02 Performance Optimization Practice Question
A data engineer runs a dashboard query that aggregates sales by region for the current month. The query scans a large fact table but returns only a few rows. The Query Profile shows that most time is spent scanning micro-partitions that do not contain the current month's data. Which feature should the engineer use to improve performance for this recurring query?
⚠ Common exam trap
A common mix-up: candidates confuse result caching or search optimization with a materialized view, when only a materialized view pre-aggregates and persistently reduces the scanned data for recurring aggregations.
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
✓
Create a materialized view that pre-aggregates sales by region and month.
The recurring dashboard query aggregates a large fact table but returns few rows, and the profile shows scanning of unnecessary micro-partitions. A materialized view pre-aggregates sales by region and month, so the query reads a compact result instead of the base table. Snowflake maintains the view automatically and can transparently rewrite the query to use it, cutting scan time and cost for this repetitive pattern.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable the USE_CACHED_RESULT session parameter for the dashboard user.
Why it's wrong here
USE_CACHED_RESULT only reuses results when the exact same query text and underlying data are unchanged. Dashboard queries often vary filters or time ranges, and any data change invalidates the cache. It does not address the root cause of scanning many micro-partitions for a recurring aggregation, so performance would remain inconsistent.
- ✓
Create a materialized view that pre-aggregates sales by region and month.
Why this is correct
A materialized view stores the pre-computed aggregation, so the recurring dashboard query can read a much smaller, pre-aggregated result set instead of scanning the full fact table. Snowflake automatically maintains the materialized view as base data changes, and the optimizer can rewrite queries to use it, dramatically reducing scan time for this repetitive aggregation pattern.
- ✗
Increase the warehouse size to a larger multi-cluster warehouse.
Why it's wrong here
Scaling up adds compute resources, which can speed up a single scan, but the query still reads the same volume of micro-partitions. A multi-cluster warehouse helps with concurrency, not with reducing the scanned data volume. The inefficiency is caused by scanning partitions that do not contain the current month's data, so more compute is a costly workaround rather than a fix.
- ✗
Add a search optimization service to the fact table on the region column.
Why it's wrong here
Search optimization service accelerates point lookups and selective equality predicates, not aggregations over a time range. It would not reduce the scan of micro-partitions for a monthly aggregation. The query's bottleneck is scanning historical partitions, so search optimization would add overhead without addressing the aggregation pattern.
About these practice questions
Courseiva writes every DEA-C02 question from scratch — 229 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 DEA-C02 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 DEA-C02 exam.