DEA-C02 Data Transformation Practice Question
An engineer needs to ensure that a transformation pipeline handles 'late-arriving' data in a streaming context. Which feature is most effective?
⚠ Common exam trap
Candidates often incorrectly suggest using Streams and Tasks for late-arriving data. While viable, Dynamic Tables are specifically designed to handle the state and refresh logic declaratively without manual orchestrations.
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
✓
Using Dynamic Tables with a configured lag target.
Snowflake's Dynamic Tables allow for declarative data transformation pipelines that automatically handle data state, including updates and late arrivals. By defining the lag, the engine manages the re-processing required to integrate new data into the final result set. This eliminates the need for manual handling of late data, which is historically a significant pain point in traditional ETL pipeline design.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Using a manual stored procedure to scan for missing data based on timestamps.
Why it's wrong here
Manual scanning via stored procedures is inefficient and prone to errors. It requires constant maintenance and often leads to gaps in data coverage, as late-arriving data might be missed if the scan window is not perfectly aligned with the ingestion latency.
- ✓
Using Dynamic Tables with a configured lag target.
Why this is correct
Dynamic Tables are specifically designed to handle incremental changes, including updates to existing records and late-arriving data. The system automatically manages the refreshes based on the lag target, ensuring that the target table remains consistent with the source data without manual intervention.
- ✗
Implementing a complex Python task to buffer all data in a queue before processing.
Why it's wrong here
Buffering data manually is an anti-pattern in Snowflake. It increases complexity, adds latency, and creates a potential point of failure. Snowflake's built-in features are optimized to handle ingestion and transformation at scale, making manual buffering both unnecessary and detrimental to system performance.
- ✗
Overwriting the entire target table with every execution to ensure data integrity.
Why it's wrong here
Full table overwrites are extremely expensive and inefficient for large datasets. This approach completely ignores the benefits of micro-partitioning and incremental processing, leading to excessive compute costs and slow performance as the dataset scales over time.
About these practice questions
This DEA-C02 question is part of Courseiva's 229-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam 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.