DAA-C01 Question 70
Single answerLoad files using SnowsightA data analyst needs to quickly load a monthly CSV extract from a local laptop into an existing Snowflake table for ad hoc analysis. The analyst is using Snowsight and wants to avoid writing SQL if possible. The target table already exists, but the source file contains a header row and stores dates as strings in a non-default format. Which action in Snowsight is the best way to load the file successfully while handling these parsing requirements during the upload workflow?
- A
Use Snowsight to open the target table, choose Load Data, upload the local CSV file, and configure the file format options such as skipping the header row and specifying date parsing settings before loading.
- B
Create an internal stage manually with SQL, use PUT from Snowsight to upload the local file, and then rely on Snowsight to infer all date formats automatically during COPY INTO without any file format settings.
- C
Use Snowsight to upload the file into a worksheet result set first, then save the result set into the existing table so the header row is ignored automatically.
- D
Convert the CSV file to JSON locally before using Snowsight Load Data, because Snowsight cannot apply CSV parsing options such as header skipping or date format handling during upload.
Show answer and explanation
Correct answer: A
Explanation
The best answer is to use Snowsight's built-in Load Data workflow on the existing table and configure the file format options during the upload. In practical analyst workflows, this is the fastest and most appropriate method when loading a local CSV file for analysis. Snowflake supports loading files through Snowsight, including defining or adjusting parsing settings such as header handling and format interpretation. For CSV ingestion, properties like SKIP_HEADER and date-related parsing behavior are important when the source file includes column headers or non-default date representations. The incorrect options reflect common misconceptions: that SQL-based PUT/COPY is required for every load, that worksheet result sets can substitute for file loading, or that CSV parsing must be handled outside Snowflake. These points align with Snowflake documentation and best practices around using Snowsight to load data into tables and configuring file format options appropriately for the source file structure.
- A. Correct.
Correct. Snowsight provides a Load Data workflow that supports uploading local files into a table and allows configuration of file format-related settings during the process. For CSV files, this includes options such as skipping header rows and setting parsing behavior. When the target table already exists, this is the most direct no-code or low-code approach for a data analyst who wants to load a local file while controlling how the file is interpreted.
- B. Incorrect.
Incorrect. This option mixes concepts in a way that is not the best fit for the scenario. Loading a local file with PUT is a SQL-driven approach and does not satisfy the requirement to avoid writing SQL if possible. Also, Snowflake does not simply infer all non-default date formats automatically without appropriate file format or load settings. Explicit parsing configuration is typically needed when source data uses non-default formats.
- C. Incorrect.
Incorrect. Snowsight does not provide a workflow where a local CSV is uploaded into a worksheet result set and then saved into a table as a standard file-loading mechanism. Worksheet result sets come from executed queries, not from directly ingesting raw local files in place of staged file loading. This distractor reflects a common misunderstanding between query results and file ingestion workflows.
- D. Incorrect.
Incorrect. Snowsight can load CSV files directly and supports relevant parsing options in the load workflow. Converting the file to JSON is unnecessary and could introduce extra complexity or transformation errors. The misconception here is that file parsing controls require preprocessing outside Snowflake, which is not true for common CSV load settings.