A data science team receives daily CSV files from a marketing platform via an SFTP drop. They need to make the data queryable in Snowflake for feature engineering, but the files are large and arrive at unpredictable times. The team wants to minimize manual intervention and avoid loading raw data into permanent tables. Which approach should they use?

Use an external orchestration tool to move files from SFTP to a cloud storage bucket, then create an external stage and external table over that bucket.

Create an external stage pointing to the SFTP location and query the files directly as an external table.

Set up a Snowflake task that runs every 15 minutes to COPY INTO a raw table from an external stage, and then create a view for feature engineering.

Use Snowpipe to automatically load new files from an external stage into a raw table as they arrive.

SnowflakeSnowPro Advanced — Data Scientist

Domain 2Objective 1

Build Data Pipelines for Data Science Workloads SNOWPRO-ADVANCED-DATA-SCIENTIST Practice Questions (Page 1)

Part of the Data Pipelining domain, which makes up ~15% of our current practice bank.

26questions here

6free pages

10concepts

Questions 1–5

1foundation · easy
Which Snowflake feature allows you to track the history of queries executed on a warehouse, which is useful for monitoring pipeline health?
Select an answer first
2foundation · easy
Which Snowflake feature automatically organizes data in a table to improve query performance on frequently filtered columns?
Select an answer first
3foundation · easy
Which Snowflake feature allows you to create a consistent snapshot of data at a specific point in time, which is useful for reproducibility in ML experiments?
Select an answer first
4foundation · easy
For streaming data ingestion into Snowflake for ML use, which feature is designed to capture and track changes (inserts, updates, deletes) to a table?
Select an answer first
5application · medium
A data science team receives daily CSV files from a marketing platform via an SFTP drop. They need to make the data queryable in Snowflake for feature engineering, but the files are large and arrive at unpredictable times. The team wants to minimize manual intervention and avoid loading raw data into permanent tables. Which approach should they use?
Select an answer first

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Build Data Pipelines for Data Science Workloads SNOWPRO-ADVANCED-DATA-SCIENTIST Practice Questions (Page 1)

Questions 1–5

Which Snowflake feature allows you to track the history of queries executed on a warehouse, which is useful for monitoring pipeline health?

Which Snowflake feature automatically organizes data in a table to improve query performance on frequently filtered columns?

Which Snowflake feature allows you to create a consistent snapshot of data at a specific point in time, which is useful for reproducibility in ML experiments?

For streaming data ingestion into Snowflake for ML use, which feature is designed to capture and track changes (inserts, updates, deletes) to a table?

Keep studying — Data Pipelining