ARA-C01 Reliable Test Blueprint - ARA-C01 Certification Exam Infor

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Snowflake ARA-C01 certification is recognized by industry leaders and is a valuable asset for professionals who want to advance their careers in cloud computing. It is an excellent way to demonstrate your expertise in Snowflake and open up new career opportunities. SnowPro Advanced Architect Certification certification is also a testament to your commitment to staying up-to-date with the latest industry trends and technologies.

Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is an industry-recognized certification that validates the advanced skills and knowledge of professionals in designing and implementing Snowflake solutions. It is a comprehensive exam that covers a wide range of topics, including Snowflake architecture, security, performance optimization, data modeling, and integration with other technologies. SnowPro Advanced Architect Certification certification is ideal for professionals who are responsible for managing Snowflake solutions for their organizations and want to demonstrate their expertise in this area.

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Snowflake SnowPro Advanced Architect Certification Sample Questions (Q64-Q69):

NEW QUESTION # 64
What Snowflake system functions are used to view and or monitor the clustering metadata for a table? (Select TWO).

A. SYSTEMSCLUSTERING_RATIO
B. SYSTEMSCLUSTERING
C. SYSTEMSTABLE_CLUSTERING
D. SYSTEMSCLUSTERING_INFORMATION
E. SYSTEMSCLUSTERING_DEPTH

Answer: D,E

Explanation:
The Snowflake system functions used to view and monitor the clustering metadata for a table are:
* SYSTEM$CLUSTERING_INFORMATION
* SYSTEM$CLUSTERING_DEPTH
Comprehensive But Short Explanation:
* The SYSTEM$CLUSTERING_INFORMATION function in Snowflake returns a variety of clustering information for a specified table. This information includes the average clustering depth, total number of micro-partitions, total constant partition count, average overlaps, average depth, and a partition depth histogram. This function allows you to specify either one or multiple columns for which the clustering information is returned, and it returns this data in JSON format.
* The SYSTEM$CLUSTERING_DEPTH function computes the average depth of a table based on specified columns or the clustering key defined for the table. A lower average depth indicates that the table is better clustered with respect to the specified columns. This function also allows specifying columns to calculate the depth, and the values need to be enclosed in single quotes.
References:
* SYSTEM$CLUSTERING_INFORMATION: Snowflake Documentation
* SYSTEM$CLUSTERING_DEPTH: Snowflake Documentation

NEW QUESTION # 65
If a multi-cluster warehouse is resized, the new size applies to

A. All of the above
B. Clusters that are currently running
C. Clusters that are started after the warehouse is resized

Answer: A

NEW QUESTION # 66
Files arrive in an external stage every 10 seconds from a proprietary system. The files range in size from 500 K to 3 MB. The data must be accessible by dashboards as soon as it arrives.
How can a Snowflake Architect meet this requirement with the LEAST amount of coding? (Choose two.)

A. Use Snowpipe with auto-ingest.
B. Use a COPY command with a task.
C. Use a materialized view on an external table.
D. Use a combination of a task and a stream.
E. Use the COPY INTO command.

Answer: A,C

Explanation:
These two options are the best ways to meet the requirement of loading data from an external stage and making it accessible by dashboards with the least amount of coding.
* Snowpipe with auto-ingest is a feature that enables continuous and automated data loading from an external stage into a Snowflake table. Snowpipe uses event notifications from the cloud storage service to detect new or modified files in the stage and triggers a COPY INTO command to load the data into the table. Snowpipe is efficient, scalable, and serverless, meaning it does not require any infrastructure or maintenance from the user. Snowpipe also supports loading data from files of any size, as long as they are in a supported format1.
* A materialized view on an external table is a feature that enables creating a pre-computed result set from an external table and storing it in Snowflake. A materialized view can improve the performance and efficiency of querying data from an external table, especially for complex queries or dashboards. A materialized view can also support aggregations, joins, and filters on the external table data. A
* materialized view on an external table is automatically refreshed when the underlying data in the external stage changes, as long as the AUTO_REFRESH parameter is set to true2.
References:
* Snowpipe Overview | Snowflake Documentation
* Materialized Views on External Tables | Snowflake Documentation

NEW QUESTION # 67
What transformations are supported in the below SQL statement? (Select THREE).
CREATE PIPE ... AS COPY ... FROM (...)

A. Data can be filtered by an optional where clause.
B. Incoming data can be joined with other tables.
C. Columns can be reordered.
D. Type casts are supported.
E. The ON ERROR - ABORT statement command can be used.
F. Columns can be omitted.

Answer: A,C,F

NEW QUESTION # 68
A user has the appropriate privilege to see unmasked data in a column.
If the user loads this column data into another column that does not have a masking policy, what will occur?

A. Unmasked data will be loaded in the new column.
B. Unmasked data will be loaded into the new column and no users will be able to see the unmasked data.
C. Unmasked data will be loaded into the new column but only users with the appropriate privileges will be able to see the unmasked data.
D. Masked data will be loaded into the new column.

Answer: A

Explanation:
According to the SnowPro Advanced: Architect documents and learning resources, column masking policies are applied at query time based on the privileges of the user who runs the query. Therefore, if a user has the privilege to see unmasked data in a column, they will see the original data when they query that column. If they load this column data into another column that does not have a masking policy, the unmasked data will be loaded in the new column, and any user who can query the new column will see the unmasked data as well.
The masking policy does not affect the underlying data in the column, only the query results.
References:
* Snowflake Documentation: Column Masking
* Snowflake Learning: Column Masking

NEW QUESTION # 69
......

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