DEA-C02 Performance Optimization Practice Question
A data engineer is reviewing a Query Profile for a query that filters a large table on a column with a very high cardinality, such as a UUID. The query applies an equality predicate on that column and returns a single row. The table is not clustered on that column. Which Snowflake feature is designed to accelerate this type of highly selective point lookup?
⚠ Common exam trap
It's easy for candidates to confuse search optimization with clustering, when search optimization is the feature specifically built for highly selective point lookups.
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
✓
Search optimization service
Search optimization service is designed to accelerate highly selective point lookups and equality predicates, such as a UUID equality filter returning a single row. It maintains a search access path that lets the optimizer locate matching micro-partitions without scanning the entire table. Clustering, query acceleration, and materialized views serve different purposes and do not target point lookups as directly.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Automatic clustering
Why it's wrong here
Automatic clustering reorganizes micro-partitions based on a clustering key to improve pruning for range and equality filters on that key. However, for a very high cardinality column like a UUID, clustering provides limited benefit because each value appears in few rows and the min/max ranges may still overlap. It is not the feature specifically designed for point lookups, and it incurs reclustering costs.
- ✓
Search optimization service
Why this is correct
Search optimization service is specifically designed to accelerate highly selective point lookups and equality predicates on columns, even when the table is not clustered on those columns. It maintains a search access path that allows the optimizer to find matching micro-partitions quickly. For a UUID equality filter returning a single row, this is the intended use case and provides significant latency improvement.
- ✗
Materialized view with a cluster by clause
Why it's wrong here
A materialized view with a cluster by clause can improve performance for queries that match its definition and filters, but it must be created and maintained, and it does not automatically accelerate arbitrary point lookups on the base table. For a UUID equality filter, a materialized view would need to include that column and be refreshed, adding overhead. It is not the purpose-built feature for point lookups.
- ✗
Query acceleration service
Why it's wrong here
Query acceleration service offloads portions of eligible queries to shared compute resources, primarily benefiting scan-heavy queries with selective filters that still require scanning large amounts of data. It is not a point-lookup optimization and does not maintain a search access path. For a single-row UUID lookup, it would not provide the same targeted acceleration as a search optimization service.
About these practice questions
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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
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.