Snowflake SnowPro Advanced Data Engineer Exam Practice Questions (P. 2)

GPT-4o - Answer

When using the COPY INTO command in Snowflake, understand that external stages can indeed be set up in any region and on any cloud provider, hence options D and E which involve Amazon S3 buckets in different AWS regions are completely plausible. For internal stages, they're automatically configured to be in the same region as your Snowflake deployment, contrary to the option given for B, hence it can lead to some confusion but generally, option B wouldn't be correct. This highlights the flexibility of external stages in terms of location and cloud provider compared to the more restricted internal stages.

GPT-4o - Answer

Creating a transient database from a production database clone ensures no Fail-safe retention is applied, aligning with your need to disable Fail-safe for all tables within the development environment. Transient databases also offer cost benefits when frequently modifying transitory data without the necessity of Fail-safe overhead. Opting for this database type not only satisfies the requirement but also adds flexibility and efficiency in data handling during development phases.

GPT-4o - Answer

The correct choice, Option C, effectively ensures we are considering only instances of MYTASK that were scheduled within the last hour and are either still running or have ended. This is done by setting conditions on the STATE and SCHEDULED_TIME columns. Key here is the precise use of the ORDER BY clause to fetch the latest 20 task executions. The WHERE condition excludes tasks planned for future execution or those without a designated schedule time, thus honing in on recent activity.

GPT-4o - Answer

Absolutely! So, for crafting a DataFrame in Snowpark, three proven methods stand out. You can leverage the `session.read.json()` when you want to pull in data from JSON files – super useful for ingesting structured data. The `session.table()` function is handy when you need to access already existing tables directly within your session. And, by utilizing `session.sql()`, you engage the powerful SQL engine directly, running queries and getting results encapsulated as DataFrames. This trio offers flexible, robust ways to handle diverse data sourcing needs in Snowflake's environment.