SnowPro DEA-C02 Cheat Sheet: Data Engineer

May 1, 2026

Review a compact SnowPro Advanced Data Engineer (DEA-C02) cheat sheet for Snowflake ingestion, transformations, streaming, orchestration, sharing, governance, observability, and performance before IT Mastery practice.

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Use this cheat sheet before a SnowPro Advanced Data Engineer DEA-C02 practice set. The exam is scenario-heavy, so identify the pipeline stage, freshness requirement, Snowflake object boundary, and operational evidence before selecting a feature.

Open SnowPro Data Engineer practice when you are ready for the free diagnostic, topic drills, timed mocks, and the full IT Mastery question bank.

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Exam snapshot

Item	SnowPro Data Engineer cue
Vendor	Snowflake
Certification	Snowflake SnowPro Advanced: Data Engineer
Exam code	DEA-C02
Items	65 total
Main practice behavior	Snowflake pipeline design, operations, governance, and performance judgment
IT Mastery status	live practice available

Domain checklist

Domain	Weight	What to know	Common trap
Data sourcing, storage, and ingestion	22%	stages, file formats, COPY, Snowpipe, ingestion patterns, storage layout	treating all ingestion as the same latency and cost pattern
Transformations, programmability, and developer workflows	24%	SQL transformations, Snowpark, UDFs, procedures, dynamic tables, development flow	choosing a procedural pattern for simple set-based transformation
Streaming, orchestration, and near real-time pipeline design	20%	streams, tasks, dependencies, freshness, lag, scheduling, monitoring	using streaming when scheduled batch processing meets the requirement
Sharing, replication, and cross-platform delivery	18%	shares, reader accounts, replication, cross-cloud delivery, marketplace patterns	copying data when live governed access is the requirement
Compute, governance, observability, and performance	16%	warehouses, query history, task history, permissions, cost, optimization	redesigning before reading operational evidence

Must-know distinctions

Snowpipe versus scheduled COPY: continuous file ingestion differs from scheduled warehouse-driven loading.
Stream versus task: streams expose change records; tasks schedule SQL work.
Dynamic table versus task pipeline: dynamic tables express target freshness; tasks orchestrate explicit work.
UDF versus stored procedure: scalar row-level logic differs from procedural orchestration.
Share versus reader account: reader accounts help consumers without Snowflake accounts.
Replication versus sharing: replication makes another copy; sharing exposes provider-controlled data.
Query history versus task history: both can diagnose failures, but they answer different operational questions.

Common traps

Ignoring data freshness and cost constraints when choosing ingestion or orchestration.
Choosing an external function when no external network access is needed.
Treating a transformation issue as an infrastructure issue.
Missing that the consumer already has a Snowflake account.
Optimizing warehouse size before checking query profile, pruning, or task behavior.

Practice strategy

Use the free diagnostic once, then label misses by pipeline stage: source, ingest, transform, orchestrate, deliver, govern, or optimize. If you miss orchestration items, draw the dependency chain. If you miss performance items, identify the evidence source before changing the design.

Revised on Sunday, May 24, 2026

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