SnowPro Associate: Platform Question 279
Single answer● Use Snowsight to load dataA data analyst needs to load a one-time CSV extract from a local laptop into an existing Snowflake table for quick validation. The analyst has access to Snowsight but does not use SnowSQL or external cloud storage. In Snowsight, which approach should the analyst use to complete this task with the least setup?
- A
Open the target table in Snowsight and use the Load Data option to upload the local CSV file, map the file fields if needed, and load the data into the existing table.
- B
Create an external stage that points to the analyst's local file system, then run COPY INTO from that stage in Snowsight.
- C
Use Snowsight to create a materialized view on the CSV file and then insert the results into the table.
- D
Upload the file to an internal stage only by using SnowSQL, then return to Snowsight to run the load.
Show answer and explanation
Correct answer: A
Explanation
For a one-time local file upload, Snowsight's Load Data workflow is the most practical and exam-relevant choice. Snowsight supports loading data from local files into a new or existing table and allows users to configure or infer file format details such as delimiter, header rows, and column mapping. By contrast, external stages are for supported cloud storage services, not local disks. Materialized views are unrelated to ingesting raw files. Although SnowSQL can be used to PUT files into internal stages and then run COPY INTO, that is not the least-effort solution when Snowsight is available. This aligns with Snowflake documentation on loading data through Snowsight and the distinction between internal/external staging mechanisms.
- A. Correct.
Correct. Snowsight provides a built-in Load Data workflow that supports uploading local files and loading them into a table. This is the simplest approach for a one-time load from a local machine because it does not require SnowSQL or separately managing cloud storage. During the workflow, the user can review file format settings, preview data, and map columns as needed before loading into the existing table.
- B. Incorrect.
Incorrect. External stages are for cloud storage locations such as Amazon S3, Google Cloud Storage, or Azure Blob Storage, not a user's local laptop file system. A common misconception is that any file location can be referenced as a stage, but local files are not used this way in Snowflake.
- C. Incorrect.
Incorrect. Materialized views are created on query results from database objects, not directly on local CSV files. This option confuses data loading with query optimization features. A CSV file must first be staged or uploaded through a supported loading workflow before its data can be queried in Snowflake.
- D. Incorrect.
Incorrect. SnowSQL can upload local files to an internal stage, but the scenario explicitly asks for the least setup using Snowsight. Requiring SnowSQL adds unnecessary steps and does not match the analyst's available tooling. Snowsight can handle this use case directly.