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The Snowflake ARA-C01 Exam consists of 90 multiple-choice questions that must be completed within two hours. The questions are designed to test an individual's knowledge of Snowflake's architecture, including multi-cluster warehouses, virtual warehouses, and resource management. It also covers topics such as data modeling, security, performance optimization, and data integration.

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Snowflake ARA-C01 exam is intended for individuals who have significant experience in working with Snowflake technology, and who possess a strong understanding of the Snowflake platform, its features and capabilities, as well as the Snowflake architecture. ARA-C01 exam tests the candidate's knowledge of Snowflake's data warehousing, data processing, and data integration capabilities, as well as their ability to design and implement complex Snowflake solutions.

Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is a highly specialized certification program designed for professionals who want to demonstrate their advanced technical knowledge and skills in designing, deploying, and managing Snowflake solutions. SnowPro Advanced Architect Certification certification exam is designed to assess the knowledge and skills required to build, design, and manage a Snowflake architecture at an expert level.

Snowflake SnowPro Advanced Architect Certification Sample Questions (Q109-Q114):

NEW QUESTION # 109
You have set time-travel retention to 10 days. You now increase the retention period by 10 more days to make it 20 days.
What will be the impact on the table data?

A. Any data which has not reached the 10 days time-travel period, will now have time-travel extended for 20 days
B. Changes will impact only new data
C. Any data that is 10 days older and moved to fail-safe will not have any impact

Answer: A,C

NEW QUESTION # 110
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?

A. Setting a '0' or NULL value means the warehouses will suspend after the default of 600 seconds.
B. Setting a '0' value means the warehouses will suspend immediately, and NULL means the warehouses will never suspend.
C. Setting a '0' or NULL value means the warehouses will suspend immediately.
D. Setting a '0' or NULL value means the warehouses will never suspend.

Answer: B

Explanation:
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. References:
* ALTER WAREHOUSE
* Parameters

NEW QUESTION # 111
Which of the below commands will use warehouse credits?

A. SHOW TABLES LIKE 'SNOWFL%';
B. SELECT MAX(FLAKE_ID) FROM SNOWFLAKE;
C. SELECT COUNT(FLAKE_ID) FROM SNOWFLAKE GROUP BY FLAKE_ID;
D. SELECT COUNT(*) FROM SNOWFLAKE;

Answer: B,C,D

Explanation:
* Warehouse credits are used to pay for the processing time used by each virtual warehouse in Snowflake.
A virtual warehouse is a cluster of compute resources that enables executing queries, loading data, and performing other DML operations. Warehouse credits are charged based on the number of virtual warehouses you use, how long they run, and their size1.
* Among the commands listed in the question, the following ones will use warehouse credits:
* SELECT MAX(FLAKE_ID) FROM SNOWFLAKE: This command will use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the maximum value of the FLAKE_ID column2. Therefore, option B is correct.
* SELECT COUNT(*) FROM SNOWFLAKE: This command will also use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the number of rows in the table3. Therefore, option C is correct.
* SELECT COUNT(FLAKE_ID) FROM SNOWFLAKE GROUP BY FLAKE_ID: This command will also use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the number of rows for each distinct value of the FLAKE_ID column4. Therefore, option D is correct.
* The command that will not use warehouse credits is:
* SHOW TABLES LIKE 'SNOWFL%': This command will not use warehouse credits because it is a metadata operation that does not require a virtual warehouse to execute. The command will return the names of the tables that match the pattern 'SNOWFL%' in the current database and schema5. Therefore, option A is incorrect.
References: : Understanding Compute Cost : MAX Function : COUNT Function : GROUP BY Clause : SHOW TABLES

NEW QUESTION # 112
An Architect is implementing a CI/CD process. When attempting to clone a table from a production to a development environment, the cloning operation fails.
What could be causing this to happen?

A. The table is transient.
B. The table has a masking policy.
C. The retention time for the table is set to zero.
D. Tables cannot be cloned from a higher environment to a lower environment.

Answer: B

Explanation:
Cloning a table with a masking policy can cause the cloning operation to fail because the masking policy is not automatically cloned with the table. This is due to the fact that the masking policy is considered a separate object with its own set of privileges1.
Reference
Snowflake Documentation on Cloning Considerations1.

NEW QUESTION # 113
Company A has recently acquired company B. The Snowflake deployment for company B is located in the Azure West Europe region.
As part of the integration process, an Architect has been asked to consolidate company B's sales data into company A's Snowflake account which is located in the AWS us-east-1 region.
How can this requirement be met?

A. Build a custom data pipeline using Azure Data Factory or a similar tool to extract the sales data from company B's Snowflake account. Transform the data, then load it into company A's Snowflake account.
B. Replicate the sales data from company B's Snowflake account into company A's Snowflake account using cross-region data replication within Snowflake. Configure a direct share from company B's account to company A's account.
C. Migrate company B's Snowflake deployment to the same region as company A's Snowflake deployment, ensuring data locality. Then perform a direct database-to-database merge of the sales data.
D. Export the sales data from company B's Snowflake account as CSV files, and transfer the files to company A's Snowflake account. Import the data using Snowflake's data loading capabilities.

Answer: B

Explanation:
The best way to meet the requirement of consolidating company B's sales data into company A's Snowflake account is to use cross-region data replication within Snowflake. This feature allows data providers to securely share data with data consumers across different regions and cloud platforms. By replicating the sales data from company B's account in Azure West Europe region to company A's account in AWS us-east-1 region, the data will be synchronized and available for consumption. To enable data replication, the accounts must be linked and replication must be enabled by a user with the ORGADMIN role. Then, a replication group must be created and the sales database must be added to the group. Finally, a direct share must be configured from company B's account to company A's account to grant access to the replicated data. This option is more efficient and secure than exporting and importing data using CSV files or migrating the entire Snowflake deployment to another region or cloud platform. It also does not require building a custom data pipeline using external tools.
Reference:
Sharing data securely across regions and cloud platforms
Introduction to replication and failover
Replication considerations
Replicating account objects

NEW QUESTION # 114
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