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The Snowflake ARA-C01 certification can play a crucial role in career advancement and increase your earning potential. By obtaining Snowflake ARA-C01 certification, you can demonstrate to employers your expertise and knowledge. The Snowflake world is constantly changing its dynamics. With the Snowflake ARA-C01 Certification Exam you can learn these changes and stay updated with the latest technologies and trends.

Snowflake ARA-C01 certification exam is a vendor-neutral certification, which means that it is recognized across the industry, regardless of the specific technology or solution being used. SnowPro Advanced Architect Certification certification is intended for professionals who have a deep understanding of Snowflake architecture and its various components, including data integration, data warehousing, data modeling, and data governance.

Passing the Snowflake ARA-C01 exam demonstrates that a candidate has a deep understanding of Snowflake's architecture and can design and implement complex Snowflake solutions. SnowPro Advanced Architect Certification certification also signifies that the candidate is capable of integrating Snowflake with other technologies and can provide expert guidance to organizations on how to best leverage Snowflake for their specific needs. With the demand for Snowflake expertise on the rise, achieving the ARA-C01 Certification can open up new career opportunities and increase earning potential for professionals in the field.

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Snowflake SnowPro Advanced Architect Certification Sample Questions (Q106-Q111):

NEW QUESTION # 106
A Snowflake Architect Is working with Data Modelers and Table Designers to draft an ELT framework specifically for data loading using Snowpipe. The Table Designers will add a timestamp column that Inserts the current tlmestamp as the default value as records are loaded into a table. The Intent is to capture the time when each record gets loaded into the table; however, when tested the timestamps are earlier than the loae_take column values returned by the copy_history function or the Copy_HISTORY view (Account Usage).
Why Is this occurring?

A. The timestamps are different because there are parameter setup mismatches. The parameters need to be realigned
B. The Table Designer team has not used the localtimestamp or systimestamp functions in the Snowflake copy statement.
C. The Snowflake timezone parameter Is different from the cloud provider's parameters causing the mismatch.
D. The CURRENT_TIMEis evaluated when the load operation is compiled in cloud services rather than when the record is inserted into the table.

Answer: D

Explanation:
The correct answer is D because the CURRENT_TIME function returns the current timestamp at the start of the statement execution, not at the time of the record insertion. Therefore, if the load operation takes some time to complete, the CURRENT_TIME value may be earlier than the actual load time.
Option A is incorrect because the parameter setup mismatches do not affect the timestamp values. The parameters are used to control the behavior and performance of the load operation, such as the file format, the error handling, the purge option, etc.
Option B is incorrect because the Snowflake timezone parameter and the cloud provider's parameters are independent of each other. The Snowflake timezone parameter determines the session timezone for displaying and converting timestamp values, while the cloud provider's parameters determine the physical location and configuration of the storage and compute resources.
Option C is incorrect because the localtimestamp and systimestamp functions are not relevant for the Snowpipe load operation. The localtimestamp function returns the current timestamp in the session timezone, while the systimestamp function returns the current timestamp in the system timezone. Neither of them reflect the actual load time of the records. Reference:
Snowflake Documentation: Loading Data Using Snowpipe: This document explains how to use Snowpipe to continuously load data from external sources into Snowflake tables. It also describes the syntax and usage of the COPY INTO command, which supports various options and parameters to control the loading behavior.
Snowflake Documentation: Date and Time Data Types and Functions: This document explains the different data types and functions for working with date and time values in Snowflake. It also describes how to set and change the session timezone and the system timezone.
Snowflake Documentation: Querying Metadata: This document explains how to query the metadata of the objects and operations in Snowflake using various functions, views, and tables. It also describes how to access the copy history information using the COPY_HISTORY function or the COPY_HISTORY view.

NEW QUESTION # 107
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?

A. Increase the size of the virtual warehouse to a size 5X-Large.
B. Create larger files for Snowpipe to ingest and ensure the staging frequency does not exceed 1 minute.
C. Eliminate the use of Snowpipe and load the files into internal stages using PUT commands.
D. Use an ORDER BY <cluster_key (s) > command to load the reporting tables.

Answer: D

Explanation:
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.
References =
*Snowflake Documentation on micro-partitions and data clustering2
*Community article on recognizing unsatisfactory pruning and improving it1

NEW QUESTION # 108
A DevOps team has a requirement for recovery of staging tables used in a complex set of data pipelines. The staging tables are all located in the same staging schema. One of the requirements is to have online recovery of data on a rolling 7-day basis.
After setting up the DATA_RETENTION_TIME_IN_DAYS at the database level, certain tables remain unrecoverable past 1 day.
What would cause this to occur? (Choose two.)

A. The staging tables are of the TRANSIENT type.
B. The DATA_RETENTION_TIME_IN_DAYS for the staging schema has been set to 1 day.
C. The staging schema has not been setup for MANAGED ACCESS.
D. The DevOps role should be granted ALLOW_RECOVERY privilege on the staging schema.
E. The tables exceed the 1 TB limit for data recovery.

Answer: A,B

Explanation:
* 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.
References: : Understanding & Using Time Travel : Transient Tables : Managed Access : Understanding Storage Cost : Table Privileges

NEW QUESTION # 109
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).

A. select * from DEMO_DB.DEMO_SCH.CUSTOMER_ADDRESS Where CA_ADDRESS_ID LIKE '%PHPP%';
B. select * from DEMO_DB.DEMO_SCH.CUSTOMER_ADDRESS Where CA_ADDRESS_ID NOT LIKE '%AAAAAAAAPHPPL%';
C. select * from DEMO_DB.DEMO_SCH.CUSTOMER_ADDRESS Where CA_ADDRESS_ID= substring('AAAAAAAAPHPPLBAAASKDJHASLKDJHASKJD',1,16);
D. select * from DEMO_DB.DEMO_SCH.CUSTOMER_ADDRESS Where CA_ADDRESS_ID LIKE '%BAAASKD%';
E. select * from DEMO_DB.DEMO_SCH.CUSTOMER_ADDRESS Where substring(CA_ADDRESS_ID,1,8)= substring('AAAAAAAAPHPPLBAAASKDJHASLKDJHASKJD',1,8);

Answer: C,E

Explanation:
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.

NEW QUESTION # 110
Which organization-related tasks can be performed by the ORGADMIN role? (Choose three.)

A. Enabling the replication of a database
B. Viewing a list of organization accounts
C. Changing the name of an account
D. Deleting an account
E. Changing the name of the organization
F. Creating an account

Answer: A,B,F

NEW QUESTION # 111
......

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