What actions are typically involved in working with and querying data in Snowflake? (Select all that apply)

Question

Options

• AUsing randomization techniques
• BEmploying time travel for data retrieval
• CIdentifying and handling data anomalies
• DLeveraging materialized views for aggregations

• A
  100% (20)

Explanation

All four options represent legitimate and commonly used Snowflake data working practices, making this an "all of the above" question. Randomization techniques (via SAMPLE/TABLESAMPLE) allow analysts to query representative subsets of massive tables without full scans. Time Travel is a native Snowflake feature that lets you query data as it existed at any prior point (up to 90 days), invaluable for auditing or recovering from accidental changes. Identifying and handling data anomalies - such as nulls, outliers, and duplicates - is a core data quality activity performed directly in Snowflake using SQL analytical functions. Materialized views pre-compute and cache expensive aggregations, dramatically speeding up repeated queries on large datasets.

Since all choices are correct, there are no distractors - the trap here is assuming one or more options don't belong.

Memory tip: Think of the acronym RTAM - Random sampling, Time travel, Anomaly handling, Materialized views - four pillars of day-to-day Snowflake data work. If an exam question lists all of them together, "select all that apply" is almost always the answer.

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