Snowflake ARA-R01 Exam Dumps

According to the SnowPro Advanced: Architect documents and learning resources, the ways that Snowflake databases that are created from shares differ from standard databases that are not created from shares are:

Shared databases are read-only. This means that the data consumers who access the shared databases cannot modify or delete the data or the objects in the databases.The data providers who share the databases have full control over the data and the objects, and can grant or revoke privileges on them1.

Shared databases cannot be cloned. This means that the data consumers who access the shared databases cannot create a copy of the databases or the objects in the databases.The data providers who share the databases can clone the databases or the objects, but the clones are not automatically shared2.

Shared databases are not supported by Time Travel. This means that the data consumers who access the shared databases cannot use the AS OF clause to query historical data or restore deleted data.The data providers who share the databases can use Time Travel on the databases or the objects, but the historical data is not visible to the data consumers3.

The other options are incorrect because they are not ways that Snowflake databases that are created from shares differ from standard databases that are not created from shares. Option B is incorrect because shared databases do not need to be refreshed in order for new data to be visible.The data consumers who access the shared databases can see the latest data as soon as the data providers update the data1. Option E is incorrect because shared databases will not have the PUBLIC or INFORMATION_SCHEMA schemas without explicitly granting these schemas to the share.The data consumers who access the shared databases can only see the objects that the data providers grant to the share, and the PUBLIC and INFORMATION_SCHEMA schemas are not granted by default4. Option F is incorrect because shared databases cannot be created as transient databases. Transient databases are databases that do not support Time Travel or Fail-safe, and can be dropped without affecting the retention period of the data.Shared databases are always created as permanent databases, regardless of the type of the source database5.Reference:Introduction to Secure Data Sharing | Snowflake Documentation,Cloning Objects | Snowflake Documentation,Time Travel | Snowflake Documentation,Working with Shares | Snowflake Documentation,CREATE DATABASE | Snowflake Documentation

The DATA_RETENTION_TIME_IN_DAYS parameter controls the Time Travel retention period for an object (database, schema, or table) in Snowflake.This parameter specifies the number of days for which historical data is preserved and can be accessed using Time Travel operations (SELECT, CREATE ... CLONE, UNDROP)1.

The requirement for recovery of staging tables on a rolling 7-day basis means that the DATA_RETENTION_TIME_IN_DAYS parameter should be set to 7 at the database level.However, this parameter can be overridden at the lower levels (schema or table) if they have a different value1.

Therefore, one possible cause for certain tables to remain unrecoverable past 1 day is that the DATA_RETENTION_TIME_IN_DAYS for the staging schema has been set to 1 day. This would override the database level setting and limit the Time Travel retention period for all the tables in the schema to 1 day.To fix this, the parameter should be unset or set to 7 at the schema level1. Therefore, option B is correct.

Another possible cause for certain tables to remain unrecoverable past 1 day is that the staging tables are of the TRANSIENT type. Transient tables are tables that do not have a Fail-safe period and can have a Time Travel retention period of either 0 or 1 day.Transient tables are suitable for temporary or intermediate data that can be easily reproduced or replicated2.To fix this, the tables should be created as permanent tables, which can have a Time Travel retention period of up to 90 days1. Therefore, option D is correct.

Option A is incorrect because the MANAGED ACCESS feature is not related to the data recovery requirement. MANAGED ACCESS is a feature that allows granting access privileges to objects without explicitly granting the privileges to roles.It does not affect the Time Travel retention period or the data availability3.

Option C is incorrect because there is no 1 TB limit for data recovery in Snowflake.The data storage size does not affect the Time Travel retention period or the data availability4.

Option E is incorrect because there is no ALLOW_RECOVERY privilege in Snowflake.The privilege required to perform Time Travel operations is SELECT, which allows querying historical data in tables5.

According to the SnowPro Advanced: Architect documents and learning resources, the minimum object privileges required for the Snowpipe user to execute Snowpipe are:

OWNERSHIP on the named pipe.This privilege allows the Snowpipe user to create, modify, and drop the pipe object that defines the COPY statement for loading data from the stage to the table1.

USAGE and READ on the named stage.These privileges allow the Snowpipe user to access and read the data files from the stage that are loaded by Snowpipe2.

USAGE on the target database and schema.These privileges allow the Snowpipe user to access the database and schema that contain the target table3.

INSERT and SELECT on the target table.These privileges allow the Snowpipe user to insert data into the table and select data from the table4.

The other options are incorrect because they do not specify the minimum object privileges required for the Snowpipe user to execute Snowpipe. Option A is incorrect because it does not include the READ privilege on the named stage, which is required for the Snowpipe user to read the data files from the stage. Option C is incorrect because it does not include the OWNERSHIP privilege on the named pipe, which is required for the Snowpipe user to create, modify, and drop the pipe object. Option D is incorrect because it does not include the OWNERSHIP privilege on the named pipe or the READ privilege on the named stage, which are both required for the Snowpipe user to execute Snowpipe.Reference:CREATE PIPE | Snowflake Documentation,CREATE STAGE | Snowflake Documentation,CREATE DATABASE | Snowflake Documentation,CREATE TABLE | Snowflake Documentation

The error encountered during the cloning attempt arises because the schema STAGING as it existed on June 1st, 2021, is not within the Time Travel retention period. According to the schema history, STAGING was recreated on June 2nd, 2021, after being dropped on the same day. The requested timestamp of '2021-06-01 08:00:00' is prior to this recreation, hence not available. The STAGING schema from before June 2nd was dropped and exceeded the Time Travel period for retrieval by the time of the cloning attempt. Therefore, cloning STAGING as it looked on June 1st, 2021, cannot be achieved because the data from that time is no longer available within the allowed Time Travel window. Reference: Snowflake documentation on Time Travel and data cloning, which is covered under the SnowPro Advanced: Architect certification resources.

According to the Snowflake documentation, the following are some considerations for choosing clustering for a table1:

Clustering is optimal when either:

You require the fastest possible response times, regardless of cost.

Your improved query performance offsets the credits required to cluster and maintain the table.

Clustering is most effective when the clustering key is used in the following types of query predicates:

Filter predicates (e.g. WHERE clauses)

Join predicates (e.g. ON clauses)

Grouping predicates (e.g. GROUP BY clauses)

Sorting predicates (e.g. ORDER BY clauses)

Clustering is less effective when the clustering key is not used in any of the above query predicates, or when the clustering key is used in a predicate that requires a function or expression to be applied to the key (e.g. DATE_TRUNC, TO_CHAR, etc.).

For most tables, Snowflake recommends a maximum of 3 or 4 columns (or expressions) per key. Adding more than 3-4 columns tends to increase costs more than benefits.

Based on these considerations, the best option for the clustering key columns is C. C1, C3, C2, because:

These columns are heavily used in filter and join conditions of SELECT queries, which are the most effective types of predicates for clustering.

These columns have high cardinality, which means they have many distinct values and can help reduce the clustering skew and improve the compression ratio.

These columns are likely to be correlated with each other, which means they can help co-locate similar rows in the same micro-partitions and improve the scan efficiency.

These columns do not require any functions or expressions to be applied to them, which means they can be directly used in the predicates without affecting the clustering.