Snowflake COF-C03: Snowflake connectivity Practice Questions

The Snowflake JDBC driver is the standard, supported way for Java applications to connect to Snowflake using the widely adopted JDBC API, letting the app issue SQL over a standard Java database connectivity interface. SnowSQL (option A) is a standalone command-line tool, not a Java library meant to be embedded into application code. The Python connector (option C) is specifically for Python applications, not Java. Embedding Snowsight via an iframe (option D) is not a supported or sensible programmatic database access pattern for backend application logic.

SSO (single sign-on) via SAML-based federated authentication lets users authenticate through their existing corporate identity provider, centralizing access control and letting the IdP enforce policies like MFA — exactly matching the described requirement. Key-pair authentication (option A) is a strong method but is typically used for service accounts/programmatic access rather than as the primary human-user SSO experience tied to a corporate IdP. Option D (a separate Snowflake-only password) directly contradicts the goal of using existing corporate identity. Option B (no authentication, relying only on network policies) is not a substitute for identity verification and network policies alone don't authenticate individual users.

SnowSQL supports variable substitution using an ampersand prefix (e.g., &variable_name), letting values defined with !define, -D on the command line, or SET be substituted into SQL text at runtime. Option D incorrectly claims $ is the only valid form usable exactly like a shell variable — SnowSQL's substitution mechanism specifically uses the & syntax. Option B describes Jinja-style templating, which is a dbt/templating-tool convention, not native SnowSQL syntax. Option C is false; variable substitution is a documented, commonly used SnowSQL scripting feature.

The Snowflake Connector for Spark is purpose-built to let Spark DataFrames read from and write to Snowflake tables efficiently, using Snowflake-specific optimizations (like pushing down certain operations) rather than treating Snowflake as a generic JDBC source. The Kafka connector (option B) is designed for streaming ingestion from Kafka topics, not for general Spark DataFrame ETL. Shelling out to SnowSQL (option C) is a fragile, inefficient workaround compared to a native Spark data source integration. Manually wiring the ODBC driver into Spark's JDBC layer (option D) is unnecessary and not how Snowflake's Spark integration is designed to be used, when a dedicated Spark connector exists.

The Snowflake ODBC driver implements the ODBC standard specifically so applications that only support ODBC-based data source connections (common in many legacy desktop and BI tools) can connect to Snowflake. The Node.js driver (option B) is for JavaScript/Node.js applications, not a generic ODBC-compatible interface. SnowSQL (option C) is a separate command-line tool, not an ODBC driver an application can link against. The Spark connector (option A) is specifically for Apache Spark data processing jobs, unrelated to a desktop application's ODBC requirement.