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Assuming that the session parameter USE_CACHED_RESULT is set to false, what are characteristics of Snowflake virtual warehouses in terms of the use of Snowpark?
Creating a DataFrame from a table will start a virtual warehouse because it requires reading data from Snowflake. The other options will not start a virtual warehouse because they either operate on local data or use an existing session to query Snowflake.
Which use case would be BEST suited for the search optimization service?
The use case that would be best suited for the search optimization service is business users who need fast response times using highly selective filters. The search optimization service is a feature that enables faster queries on tables with high cardinality columns by creating inverted indexes on those columns. High cardinality columns are columns that have a large number of distinct values, such as customer IDs, product SKUs, or email addresses. Queries that use highly selective filters on high cardinality columns can benefit from the search optimization service because they can quickly locate the relevant rows without scanning the entire table. The other options are not best suited for the search optimization service. Option A is incorrect because analysts who need to perform aggregates over high cardinality columns will not benefit from the search optimization service, as they will still need to scan all the rows that match the filter criteria. Option C is incorrect because data scientists who seek specific JOIN statements with large volumes of data will not benefit from the search optimization service, as they will still need to perform join operations that may involve shuffling or sorting data across nodes. Option D is incorrect because data engineers who create clustered tables with frequent reads against clustering keys will not benefit from the search optimization service, as they already have an efficient way to organize and access data based on clustering keys.
A new customer table is created by a data pipeline in a Snowflake schema where MANAGED ACCESS enabled.
.... Can gran access to the CUSTOMER table? (Select THREE.)
The roles that can grant access to the CUSTOMER table are the role that owns the schema, the role that owns the database, and the SECURITYADMIN role. These roles have the ownership or the manage grants privilege on the schema or the database level, which allows them to grant access to any object within them. The other options are incorrect because they do not have the necessary privilege to grant access to the CUSTOMER table. Option C is incorrect because the role that owns the customer table cannot grant access to itself or to other roles. Option D is incorrect because the SYSADMIN role does not have the manage grants privilege by default and cannot grant access to objects that it does not own. Option F is incorrect because the USERADMIN role with the manage grants privilege can only grant access to users and roles, not to tables.
Company A and Company B both have Snowflake accounts. Company A's account is hosted on a different cloud provider and region than Company B's account Companies A and B are not in the same Snowflake organization.
How can Company A share data with Company B? (Select TWO).
The ways that Company A can share data with Company B are:
Create a share within Company A's account and add Company B's account as a recipient of that share: This is a valid way to share data between different accounts on different cloud platforms and regions. Snowflake supports cross-cloud and cross-region data sharing, which allows users to create shares and grant access to other accounts regardless of their cloud platform or region. However, this option may incur additional costs for network transfer and storage replication.
Create a separate database within Company A's account to contain only those data sets they wish to share with Company B Create a share within Company A's account and add all the objects within this separate database to the share Add Company B's account as a recipient of the share: This is also a valid way to share data between different accounts on different cloud platforms and regions. This option is similar to the previous one, except that it uses a separate database to isolate the data sets that need to be shared. This can improve security and manageability of the shared data. The other options are not valid because:
Create a share within Company A's account, and create a reader account that is a recipient of the share Grant Company B access to the reader account: This option is not valid because reader accounts are not supported for cross-cloud or cross-region data sharing. Reader accounts are Snowflake accounts that can only consume data from shares created by their provider account. Reader accounts must be on the same cloud platform and region as their provider account.
Use database replication to replicate Company A's data into Company B's account Create a share within Company B's account and grant users within Company B's account access to the share: This option is not valid because database replication cannot be used for cross-cloud or cross-region data sharing. Database replication is a feature in Snowflake that allows users to copy databases across accounts within the same cloud platform and region. Database replication cannot copy databases across different cloud platforms or regions.
Create a new account within Company A's organization in the same cloud provider and region as Company B's account Use database replication to replicate Company A's data to the new account Create a share within the new account and add Company B's account as a recipient of that share: This option is not valid because it involves creating a new account within Company A's organization, which may not be feasible or desirable for Company A. Moreover, this option is unnecessary, as Company A can directly share data with Company B without creating an intermediate account.
What are characteristics of Snowpark Python packages? (Select THREE).
Third-party packages can be registered as a dependency to the Snowpark session using the session, import () method.
The characteristics of Snowpark Python packages are:
Third-party packages can be registered as a dependency to the Snowpark session using the session.import() method.
The SQL command DESCRIBE FUNCTION will list the imported Python packages of the Python User-Defined Function (UDF).
Querying information_schema.packages will provide a list of supported Python packages and versions.
These characteristics indicate how Snowpark Python packages can be imported, inspected, and verified in Snowflake. The other options are not characteristics of Snowpark Python packages. Option B is incorrect because Python packages can be loaded in both local and remote environments using Snowpark. Option C is incorrect because third-party supported Python packages are not locked down to prevent hitting external endpoints, but rather restricted by network policies and security settings.