SnowPro Associate: Platform Question 278
Single answer● Use Snowsight to load dataA data analyst needs to quickly load a local CSV file into an existing Snowflake table using Snowsight. The CSV file contains a header row, uses commas as delimiters, and should be loaded without writing SQL manually. During testing, the analyst notices that column names from the file are being treated as data in the first row of the table. Which action in Snowsight should the analyst take to correctly load the file?
- A
In the Snowsight data loading wizard, configure the file format settings to skip 1 header row before loading the file.
- B
Create a task in Snowsight to remove the first row after the file is loaded into the table.
- C
Convert the CSV file to JSON before uploading it, because Snowsight does not support CSV headers during loading.
- D
Load the file into a temporary stage first, because Snowsight can only ignore headers when loading from named stages.
Show answer and explanation
Correct answer: A
Explanation
The best answer is to configure the file format in Snowsight so that the first row is skipped during loading. In real-world use, Snowsight provides a guided interface for loading local files into Snowflake tables, including previewing the data and adjusting parsing settings such as field delimiter and header rows. If the first row contains column names, it should not be loaded as data, so the analyst should set the header row to be skipped. This aligns with Snowflake best practices of handling format issues at load time rather than correcting them after ingestion. Relevant Snowflake documentation includes Snowsight data loading workflows and file format options such as SKIP_HEADER for CSV files.
- A. Correct.
Correct. When using Snowsight to load data, the loading workflow allows the user to review and configure file format options such as delimiter, compression, and whether header rows should be skipped. Setting the loader to skip the first row prevents the CSV header from being inserted as data. This is the appropriate fix when the source file includes column names in the first line.
- B. Incorrect.
Incorrect. Although post-load cleanup could remove bad rows, this is not the best solution and does not address the root cause during the load process. The scenario specifically asks for the correct action in Snowsight to load the file properly without manual SQL. Using a task would add unnecessary complexity and is not the intended approach for a simple file upload issue.
- C. Incorrect.
Incorrect. Snowsight supports loading CSV files directly, and CSV headers can be handled through file format settings. Converting the file to JSON is unnecessary and reflects a misunderstanding of Snowsight's built-in data loading capabilities.
- D. Incorrect.
Incorrect. Snowsight can load local files and lets users configure parsing behavior, including header handling, during the load workflow. Ignoring headers is not limited to named stages. This option confuses staging choices with file format behavior.