Explanation
The characteristics of data transformations in Snowpark that should be considered to meet this requirement are:
It is possible to join multiple tables using DataFrames.
Snowpark operations are executed lazily on the server.
These characteristics indicate how Snowpark can perform data transformations using DataFrames, which are similar to the ones used in Scala. DataFrames are distributed collections of rows that can be manipulated using various operations, such as joins, filters, aggregations, etc. DataFrames can be created from different sources, such as tables, files, or SQL queries. Snowpark operations are executed lazily on the server, which means that they are not performed until an action is triggered, such as a write or a collect operation. This allows Snowpark to optimize the execution plan and reduce the amount of data transferred between the client and the server.
The other options are not characteristics of data transformations in Snowpark that should be considered to meet this requirement. Option C is incorrect because User-Defined Functions (UDFs) are pushed down to Snowflake and executed on the server. Option D is incorrect because Snowpark does not require a separate cluster outside of Snowflake for computations, but rather uses virtual warehouses within Snowflake. Option E is incorrect because columns in different DataFrames with the same name should be referred to with dot notation, not squared brackets.

Explanation
External stage
References data files stored in a location outside of Snowflake. Currently, the following cloud stor-age services are supported:
Amazon S3 buckets
Google Cloud Storage buckets
Microsoft Azure containers
The storage location can be either private/protected or public.
You cannot access data held in archival cloud storage classes that requires restoration before it can be retrieved. These archival storage classes include, for example, the Amazon S3 Glacier Flexible Retrieval or Glacier Deep Archive storage class, or Microsoft Azure Archive Storage.

Amazon MWAA is a managed service for Apache Airflow, which is an open-source workflow automation tool. Apache Airflow can be used both on-premises and in the cloud, making it ideal for hybrid environments. Using Amazon MWAA allows the data engineer to leverage the managed service in the cloud while maintaining the ability to use the same open-source Airflow setup on-premises, ensuring portability and consistency across environments.

Explanation
For some operations (e.g. duplicate elimination for a huge data set), the amount of memory available for the compute resources used to execute the operation might not be sufficient to hold intermediate results. As a result, the query processing engine will start spilling the data to local disk. If the local disk space is not sufficient, the spilled data is then saved to remote disks.
This spilling can have a profound effect on query performance (especially if remote disk is used for spilling).
To alleviate this, It is recommend that:
Using a larger warehouse (effectively increasing the available memory/local disk space for the op-eration), and/or Processing data in smaller batches.