2023 Updated Verified ARA-C01 dumps Q&As – 100% Pass Guaranteed [Q15-Q31]

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2023 Updated Verified ARA-C01 dumps Q&As – 100% Pass Guaranteed

Provide Valid Dumps To Help You Prepare For SnowPro Advanced Architect Certification Exam

To qualify for the Snowflake ARA-C01 exam, candidates must first obtain the SnowPro Advanced Architect certification. This certification requires passing the SnowPro Core exam, as well as demonstrating proficiency in data modeling and performance tuning. Once candidates have achieved this certification, they can then take the ARA-C01 exam to further validate their expertise.

Q15. Running EXPLAIN on a query does not require a running warehouse

TRUE

FALSE

Q16. When using the Snowflake Connector for Kafka, what data formats are supported for the messages? (Choose two.)

CSV

XML

Avro

JSON

Parquet

Q17. A large manufacturing company runs a dozen individual Snowflake accounts across its business divisions. The company wants to increase the level of data sharing to support supply chain optimizations and increase its purchasing leverage with multiple vendors.
The company’s Snowflake Architects need to design a solution that would allow the business divisions to decide what to share, while minimizing the level of effort spent on configuration and management. Most of the company divisions use Snowflake accounts in the same cloud deployments with a few exceptions for European-based divisions.
According to Snowflake recommended best practice, how should these requirements be met?

Migrate the European accounts in the global region and manage shares in a connected graph architecture. Deploy a Data Exchange.

Deploy a Private Data Exchange in combination with data shares for the European accounts.

Deploy to the Snowflake Marketplace making sure that invoker_share() is used in all secure views.

Deploy a Private Data Exchange and use replication to allow European data shares in the Exchange.

Q18. The insertReport endpoint can be thought of like the UNIX command tail

TRUE

FALSE

Q19. While choosing a cluster key, what is recommended by snowflake?

Cluster columns that are most actively used in selective filters

If there is room for additional cluster keys, then consider columns frequently used in join predicates

Choose a key with high cardinality

Q20. In this scenarios, files are constantly getting ingested in an external stage, the files are between 1 and 4 MB. You want to load the data immediately into a table without any human intervention.
What will you use?

Create a materialize view on the external stage

Use Snowpipe rest APIs

Use Snowpipe with auto ingest

Q21. What conditions should be true for a table to consider search optimization

The table size is at least 100 GB

The table is not clustered OR The table is frequently queried on columns other than the primary cluster key

The table can be of any size

Q22. A media company needs a data pipeline that will ingest customer review data into a Snowflake table, and apply some transformations. The company also needs to use Amazon Comprehend to do sentiment analysis and make the de-identified final data set available publicly for advertising companies who use different cloud providers in different regions.
The data pipeline needs to run continuously ang efficiently as new records arrive in the object storage leveraging event notifications. Also, the operational complexity, maintenance of the infrastructure, including platform upgrades and security, and the development effort should be minimal.
Which design will meet these requirements?

Ingest the data using COPY INTO and use streams and tasks to orchestrate transformations. Export the data into Amazon S3 to do model inference with Amazon Comprehend and ingest the data back into a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.

Ingest the data using Snowpipe and use streams and tasks to orchestrate transformations. Create an external function to do model inference with Amazon Comprehend and write the final records to a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.

Ingest the data into Snowflake using Amazon EMR and PySpark using the Snowflake Spark connector. Apply transformations using another Spark job. Develop a python program to do model inference by leveraging the Amazon Comprehend text analysis API. Then write the results to a Snowflake table and create a listing in the Snowflake Marketplace to make the data available to other companies.

Ingest the data using Snowpipe and use streams and tasks to orchestrate transformations. Export the data into Amazon S3 to do model inference with Amazon Comprehend and ingest the data back into a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.

Q23. The Data Engineering team at a large manufacturing company needs to engineer data coming from many sources to support a wide variety of use cases and data consumer requirements which include:
1) Finance and Vendor Management team members who require reporting and visualization
2) Data Science team members who require access to raw data for ML model development
3) Sales team members who require engineered and protected data for data monetization What Snowflake data modeling approaches will meet these requirements? (Choose two.)

Consolidate data in the company’s data lake and use EXTERNAL TABLES.

Create a raw database for landing and persisting raw data entering the data pipelines.

Create a set of profile-specific databases that aligns data with usage patterns.

Create a single star schema in a single database to support all consumers’ requirements.

Create a Data Vault as the sole data pipeline endpoint and have all consumers directly access the Vault.

Q24. You have an inventory table. You created two views on this table. The views look like as below
CREATE VIEW NON_SECURE_INVENTORY AS
SELECT BIBNUMBER, TITLE, AUTHOR,ISBN
FROM INVENTORY
WHERE BIBNUMBER IN(511784,511805,511988,512044,512052,512063);
CREATE SECURE VIEW SECURE_INVENTORY AS
SELECT BIBNUMBER, TITLE, AUTHOR,ISBN
FROM INVENTORY
WHERE BIBNUMBER IN(511784,511805,511988,512044,512052,512063);
You ran the below queries
ALTER SESSION SET USE_CACHED_RESULT=FALSE;–This is to ensure that we do not retrieve from query cache
SELECT * FROM NON_SECURE_INVENTORY WHERE BIBNUMBER =511784; SELECT * FROM SECURE_INVENTORY WHERE BIBNUMBER =511784;
The query profile for the first query looks as below

However, the query profile for the second one looks like as below

Both the views use the same columns from the same underlying view. So, why is this difference in query profiles.

Secure views do not expose the underlying tables or internal structural details for a view

The query profile is corrupted

The query is run by a role that did not have access to the view

Q25. Which of the following are characteristics of Snowflake’s parameter hierarchy?

Session parameters override virtual warehouse parameters.

Virtual warehouse parameters override user parameters.

Table parameters override virtual warehouse parameters.

Schema parameters override account parameters.

Q26. An Architect needs to grant a group of ORDER_ADMIN users the ability to clean old data in an ORDERS table (deleting all records older than 5 years), without granting any privileges on the table. The group’s manager (ORDER_MANAGER) has full DELETE privileges on the table.
How can the ORDER_ADMIN role be enabled to perform this data cleanup, without needing the DELETE privilege held by the ORDER_MANAGER role?

Create a stored procedure that runs with caller’s rights, including the appropriate “> 5 years” business logic, and grant USAGE on this procedure to ORDER_ADMIN. The ORDER_MANAGER role owns the procedure.

Create a stored procedure that can be run using both caller’s and owner’s rights (allowing the user to specify which rights are used during execution), and grant USAGE on this procedure to ORDER_ADMIN. The ORDER_MANAGER role owns the procedure.

Create a stored procedure that runs with owner’s rights, including the appropriate “> 5 years” business logic, and grant USAGE on this procedure to ORDER_ADMIN. The ORDER_MANAGER role owns the procedure.

This scenario would actually not be possible in Snowflake – any user performing a DELETE on a table requires the DELETE privilege to be granted to the role they are using.

Q27. For the best performance while loading data into table from a stage, you should avoid applying patterns that filter on a large number of files.

FALSE

TRUE

Q28. Which of the below commands will use warehouse credits?

SHOW TABLES LIKE ‘SNOWFL%’;

SELECT MAX(FLAKE_ID) FROM SNOWFLAKE;

SELECT COUNT(*) FROM SNOWFLAKE;

SELECT COUNT(FLAKE_ID) FROM SNOWFLAKE GROUP BY FLAKE_ID;

Q29. Which semi-structured data function returns an OBJECT constructed from the arguments.

OBJECT_CONSTRUCT

OBJECT_ARRAY

OBJECT_INSPECT

Q30. You have a large warehouse with auto suspend configured for 10 minutes. You submitted a query and it is going to run for more than 10 minutes.
What will happen to the query?

The query will automatically cancelled after 10 minutes

The query will go on a hold state

The query will continue to run

Q31. Let’s say that you have two JSONs as below
1. {“stuId”:2000, “stuName”:”Amy”}
2. {“stuId”:2000,”stuCourse”:”Snowflake”}
How will you write a query that will check if stuId in JSON in #1 is also there in JSON in#2

with stu_demography as (select parse_json(column1) as src, src:stuId as ID from values(‘{“stuId”:2000, “stuName”:”Amy”}’)),

stu_course as (select parse_json(column1) as src, src:stuId as ID from values(‘{“stuId”:2000,”stuCourse”:”Snowflake”}’)) select case when stdemo.ID in(select ID from stu_course) then ‘True’ else ‘False’ end as result from stu_demography stdemo;

with stu_demography as (select parse_json(column1) as src, src[‘stuId’] as ID from values(‘{“stuId”:2000, “stuName”:”Amy”}’)), stu_course as (select parse_json(column1) as src, src[‘stuId’] as ID from values(‘{“stuId”:2000,”stuCourse”:”Snowflake”}’)) select case when stdemo.ID in(select ID from stu_course) then ‘True’ else ‘False’ end as result from stu_demography stdemo;

SELECT CONTAINS(‘{“stuId”:2000, “stuName”:”Amy”}’,'{“stuId”:2000,”stuCourse”:”Snowflake”}’);

with stu_demography as (select parse_json(column1) as src, src[‘STUID’] as ID from values(‘{“stuId”:2000, “stuName”:”Amy”}’)), stu_course as (select parse_json(column1) as src, src[‘stuId’] as ID from values(‘{“stuId”:2000,”stuCourse”:”Snowflake”}’)) select case when stdemo.ID in(select ID from stu_course) then ‘True’ else ‘False’ end as result from stu_demography stdemo;

Snowflake ARA-C01: SnowPro Advanced Architect Certification is a prestigious and highly sought-after certification that validates the skills and knowledge of experienced architects and consultants working with Snowflake’s cloud data platform. The exam is designed to test the advanced level of expertise in designing, deploying, managing, and optimizing Snowflake solutions.

The Snowflake ARA-C01 exam is intended for experienced architects who have a deep understanding of Snowflake’s cloud data platform and its capabilities. The exam tests the candidate’s ability to design and implement complex data warehousing solutions, including data modeling, data integration, data transformation, and data analysis.

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