SOL-C01 · Question #237
You are designing a data lake solution in Snowflake that requires storing and processing both structured and semi-structured data from various sources. The data lake will be used for ad-hoc querying,
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Question
You are designing a data lake solution in Snowflake that requires storing and processing both structured and semi-structured data from various sources. The data lake will be used for ad-hoc querying, data science, and reporting. Which of the following combinations of Snowflake features and practices would be MOST appropriate for building a scalable, performant, and cost-effective data lake?
Options
- AUse external tables to access data stored in cloud storage (e.g., AWS S3) in its native format,
- BLoad all data into Snowflake internal tables in a raw format (e.g., JSON or Parquet), and create
- CStore all data in Snowflake internal tables in a fully normalized relational format, creating indexes
- DUse only external tables to access data in cloud storage, avoiding any data loading into Snowflake
- EIngest all data into a single Snowflake database, and use stored procedures to perform all data
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