Snowflake ARA-R01 Exam Dumps

Get All SnowPro Advanced: Architect Recertification Exam Questions with Validated Answers

ARA-R01 Pack
Vendor: Snowflake
Exam Code: ARA-R01
Exam Name: SnowPro Advanced: Architect Recertification
Exam Questions: 162
Last Updated: January 10, 2026
Related Certifications: SnowPro Certification
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Free Snowflake ARA-R01 Exam Actual Questions

Question No. 1

Which Snowflake objects can be used in a data share? (Select TWO).

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Correct Answer: B, D

https://docs.snowflake.com/en/user-guide/data-sharing-intro


Question No. 2

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

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Correct Answer: C, E

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.


SYSTEM$CLUSTERING_INFORMATION: Snowflake Documentation

SYSTEM$CLUSTERING_DEPTH: Snowflake Documentation

Question No. 3

A company has built a data pipeline using Snowpipe to ingest files from an Amazon S3 bucket. Snowpipe is configured to load data into staging database tables. Then a task runs to load the data from the staging database tables into the reporting database tables.

The company is satisfied with the availability of the data in the reporting database tables, but the reporting tables are not pruning effectively. Currently, a size 4X-Large virtual warehouse is being used to query all of the tables in the reporting database.

What step can be taken to improve the pruning of the reporting tables?

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Correct Answer: C

Effective pruning in Snowflake relies on the organization of data within micro-partitions. By using an ORDER BY clause with clustering keys when loading data into the reporting tables, Snowflake can better organize the data within micro-partitions. This organization allows Snowflake to skip over irrelevant micro-partitions during a query, thus improving query performance and reducing the amount of data scanned12.

Reference =

* Snowflake Documentation on micro-partitions and data clustering2

* Community article on recognizing unsatisfactory pruning and improving it1


Question No. 4

A data platform team creates two multi-cluster virtual warehouses with the AUTO_SUSPEND value set to NULL on one. and '0' on the other. What would be the execution behavior of these virtual warehouses?

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Correct Answer: D

The AUTO_SUSPEND parameter controls the amount of time, in seconds, of inactivity after which a warehouse is automatically suspended. If the parameter is set to NULL, the warehouse never suspends. If the parameter is set to '0', the warehouse suspends immediately after executing a query. Therefore, the execution behavior of the two virtual warehouses will be different depending on the AUTO_SUSPEND value. The warehouse with NULL value will keep running until it is manually suspended or the resource monitor limits are reached. The warehouse with '0' value will suspend as soon as it finishes a query and release the compute resources.Reference:

ALTER WAREHOUSE

Parameters


Question No. 5

Two queries are run on the customer_address table:

create or replace TABLE CUSTOMER_ADDRESS ( CA_ADDRESS_SK NUMBER(38,0), CA_ADDRESS_ID VARCHAR(16), CA_STREET_NUMBER VARCHAR(IO) CA_STREET_NAME VARCHAR(60), CA_STREET_TYPE VARCHAR(15), CA_SUITE_NUMBER VARCHAR(10), CA_CITY VARCHAR(60), CA_COUNTY

VARCHAR(30), CA_STATE VARCHAR(2), CA_ZIP VARCHAR(10), CA_COUNTRY VARCHAR(20), CA_GMT_OFFSET NUMBER(5,2), CA_LOCATION_TYPE

VARCHAR(20) );

ALTER TABLE DEMO_DB.DEMO_SCH.CUSTOMER_ADDRESS ADD SEARCH OPTIMIZATION ON SUBSTRING(CA_ADDRESS_ID);

Which queries will benefit from the use of the search optimization service? (Select TWO).

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Correct Answer: A, B

The use of the search optimization service in Snowflake is particularly effective when queries involve operations that match exact substrings or start from the beginning of a string. The ALTER TABLE command adding search optimization specifically for substrings on the CA_ADDRESS_ID field allows the service to create an optimized search path for queries using substring matches.

Option A benefits because it directly matches a substring from the start of the CA_ADDRESS_ID, aligning with the optimization's capability to quickly locate records based on the beginning segments of strings.

Option B also benefits, despite performing a full equality check, because it essentially compares the full length of CA_ADDRESS_ID to a substring, which can leverage the substring index for efficient retrieval. Options C, D, and E involve patterns that do not start from the beginning of the string or use negations, which are not optimized by the search optimization service configured for starting substring matches. Reference: Snowflake's documentation on the use of search optimization for substring matching in SQL queries.


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