COF-C03 Question 244
Single answerSnowflake connectorsA data engineering team is building a Python application that connects to Snowflake to run parameterized SQL queries and load query results into pandas DataFrames for downstream analysis. The security team requires support for modern authentication methods and the ability to specify connection parameters such as account, warehouse, database, and schema in code. Which connector should the team use?
- A
Use the Snowflake Connector for Python, which supports connecting from Python applications and can be used together with pandas workflows.
- B
Use SnowSQL, because it is the primary programmatic interface for Python applications and returns results directly as pandas DataFrames.
- C
Use the Snowflake CLI, because it is the recommended Python library for executing parameterized SQL inside application code.
- D
Use a JDBC driver, because JDBC is Snowflake’s native connector for Python and provides the simplest integration with pandas.
Show answer and explanation
Correct answer: A
Explanation
For Python applications, the correct choice is the Snowflake Connector for Python. Snowflake provides language-specific drivers and connectors, and the Python connector is designed for application development in Python, including executing SQL, using bind variables for parameterized queries, and integrating with Python data tools such as pandas. In contrast, SnowSQL and the Snowflake CLI are command-line tools rather than embedded application connectors, and JDBC is intended for Java connectivity. Snowflake documentation distinguishes these interfaces clearly: drivers and connectors are used by applications, while command-line tools are used for interactive or scripted administration and operations. For COF-C03, candidates should be able to identify the appropriate Snowflake client technology based on workload and environment.
- A. Correct.
Correct. The Snowflake Connector for Python is the appropriate connector for Python applications. It supports standard connection parameters such as account identifier, user, password or other authentication settings, warehouse, database, and schema. It is also designed to work with Python-based data workflows, including integration patterns with pandas for fetching query results into DataFrames. This matches the scenario of a Python application that needs programmatic access and DataFrame-oriented analysis.
- B. Incorrect.
Incorrect. SnowSQL is Snowflake’s command-line client, not the primary connector for embedding database access inside Python application code. While it can execute SQL scripts and commands interactively or in automation, it does not serve as the standard in-process Python connector and is not the correct choice for returning results directly into pandas DataFrames within an application.
- C. Incorrect.
Incorrect. The Snowflake CLI is a command-line interface for interacting with Snowflake resources and executing operations from a shell or automation environment. It is not the Python connector used to build application-level database interactions with parameterized SQL and pandas integration. Choosing it here reflects a confusion between command-line tooling and application connectors.
- D. Incorrect.
Incorrect. JDBC is intended for Java-based connectivity. Although JDBC is a valid Snowflake driver for Java ecosystems and some tools can bridge to JDBC, it is not Snowflake’s native Python connector and is not the simplest or most appropriate option for a Python application that needs direct pandas-oriented workflows.