Snowflake ARA-R01 Exam Practice Test Instant Access

Question 1

The data share exists between a data provider account and a data consumer account. Five tables from the provider account are being shared with the consumer account. The consumer role has been granted the imported privileges privilege.

What will happen to the consumer account if a new table (table_6) is added to the provider schema?

AThe consumer role will automatically see the new table and no additional grants are needed.

BThe consumer role will see the table only after this grant is given on the consumer side:
grant imported privileges on database PSHARE_EDW_4TEST_DB to DEV_ROLE;

CThe consumer role will see the table only after this grant is given on the provider side:
use role accountadmin;
Grant select on table EDW.ACCOUNTING.Table_6 to share PSHARE_EDW_4TEST;

DThe consumer role will see the table only after this grant is given on the provider side:
use role accountadmin;
grant usage on database EDW to share PSHARE_EDW_4TEST ;
grant usage on schema EDW.ACCOUNTING to share PSHARE_EDW_4TEST ;
Grant select on table EDW.ACCOUNTING.Table_6 to database PSHARE_EDW_4TEST_DB ;

Answer : D

When a new table (table_6) is added to a schema in the provider's account that is part of a data share, the consumer will not automatically see the new table. The consumer will only be able to access the new table once the appropriate privileges are granted by the provider. The correct process, as outlined in option D, involves using the provider's ACCOUNTADMIN role to grant USAGE privileges on the database and schema, followed by SELECT privileges on the new table, specifically to the share that includes the consumer's database. This ensures that the consumer account can access the new table under the established data sharing setup. Reference:

Snowflake Documentation on Managing Access Control

Snowflake Documentation on Data Sharing

Question 2

A table, EMP_ TBL has three records as shown:

The following variables are set for the session:

Which SELECT statements will retrieve all three records? (Select TWO).

ASelect * FROM Stbl_ref WHERE Scol_ref IN ('Name1','Nam2','Name3');

BSELECT * FROM EMP_TBL WHERE identifier(Scol_ref) IN ('Namel','Name2', 'Name3');

CSELECT * FROM identifier<Stbl_ref> WHERE NAME IN ($var1, $var2, $var3);

DSELECT * FROM identifier($tbl_ref) WHERE ID IN Cvarl','var2','var3');

ESELECT * FROM $tb1_ref WHERE $col_ref IN ($var1, Svar2, Svar3);

Answer : B, E

The correct answer is B and E because they use the correct syntax and values for the identifier function and the session variables.

The identifier function allows you to use a variable or expression as an identifier (such as a table name or column name) in a SQL statement. It takes a single argument and returns it as an identifier. For example, identifier($tbl_ref) returns EMP_TBL as an identifier.

The session variables are set using the SET command and can be referenced using the $ sign. For example, $var1 returns Name1 as a value.

Option A is incorrect because it uses Stbl_ref and Scol_ref, which are not valid session variables or identifiers. They should be $tbl_ref and $col_ref instead.

Option C is incorrect because it uses identifier<Stbl_ref>, which is not a valid syntax for the identifier function. It should be identifier($tbl_ref) instead.

Option D is incorrect because it uses Cvarl, var2, and var3, which are not valid session variables or values. They should be $var1, $var2, and $var3 instead.Reference:

Snowflake Documentation: Identifier Function

Snowflake Documentation: Session Variables

Snowflake Learning: SnowPro Advanced: Architect Exam Study Guide

Question 3

An Architect has designed a data pipeline that Is receiving small CSV files from multiple sources. All of the files are landing in one location. Specific files are filtered for loading into Snowflake tables using the copy command. The loading performance is poor.

What changes can be made to Improve the data loading performance?

AIncrease the size of the virtual warehouse.

BCreate a multi-cluster warehouse and merge smaller files to create bigger files.

CCreate a specific storage landing bucket to avoid file scanning.

DChange the file format from CSV to JSON.

Answer : B

According to the Snowflake documentation, the data loading performance can be improved by following some best practices and guidelines for preparing and staging the data files. One of the recommendations is to aim for data files that are roughly 100-250 MB (or larger) in size compressed, as this will optimize the number of parallel operations for a load. Smaller files should be aggregated and larger files should be split to achieve this size range. Another recommendation is to use a multi-cluster warehouse for loading, as this will allow for scaling up or out the compute resources depending on the load demand. A single-cluster warehouse may not be able to handle the load concurrency and throughput efficiently. Therefore, by creating a multi-cluster warehouse and merging smaller files to create bigger files, the data loading performance can be improved.Reference:

Data Loading Considerations

Preparing Your Data Files

Planning a Data Load

Question 4

A company wants to Integrate its main enterprise identity provider with federated authentication with Snowflake.

The authentication integration has been configured and roles have been created in Snowflake. However, the users are not automatically appearing in Snowflake when created and their group membership is not reflected in their assigned rotes.

How can the missing functionality be enabled with the LEAST amount of operational overhead?

AOAuth must be configured between the identity provider and Snowflake. Then the authorization server must be configured with the right mapping of users and roles.

BOAuth must be configured between the identity provider and Snowflake. Then the authorization server must be configured with the right mapping of users, and the resource server must be configured with the right mapping of role assignment.

CSCIM must be enabled between the identity provider and Snowflake. Once both are synchronized through SCIM, their groups will get created as group accounts in Snowflake and the proper roles can be granted.

DSCIM must be enabled between the identity provider and Snowflake. Once both are synchronized through SCIM. users will automatically get created and their group membership will be reflected as roles In Snowflake.

Answer : D

The best way to integrate an enterprise identity provider with federated authentication and enable automatic user creation and role assignment in Snowflake is to use SCIM (System for Cross-domain Identity Management). SCIM allows Snowflake to synchronize with the identity provider and create users and groups based on the information provided by the identity provider. The groups are mapped to roles in Snowflake, and the users are assigned the roles based on their group membership. This way, the identity provider remains the source of truth for user and group management, and Snowflake automatically reflects the changes without manual intervention. The other options are either incorrect or incomplete, as they involve using OAuth, which is a protocol for authorization, not authentication or user provisioning, and require additional configuration of authorization and resource servers.

Question 5

A global company needs to securely share its sales and Inventory data with a vendor using a Snowflake account.

The company has its Snowflake account In the AWS eu-west 2 Europe (London) region. The vendor's Snowflake account Is on the Azure platform in the West Europe region. How should the company's Architect configure the data share?

A1. Create a share.
2. Add objects to the share.
3. Add a consumer account to the share for the vendor to access.

B1. Create a share.
2. Create a reader account for the vendor to use.
3. Add the reader account to the share.

C1. Create a new role called db_share.
2. Grant the db_share role privileges to read data from the company database and schema.
3. Create a user for the vendor.
4. Grant the ds_share role to the vendor's users.

D1. Promote an existing database in the company's local account to primary.
2. Replicate the database to Snowflake on Azure in the West-Europe region.
3. Create a share and add objects to the share.
4. Add a consumer account to the share for the vendor to access.

Answer : A

The correct way to securely share data with a vendor using a Snowflake account on a different cloud platform and region is to create a share, add objects to the share, and add a consumer account to the share for the vendor to access. This way, the company can control what data is shared, who can access it, and how long the share is valid. The vendor can then query the shared data without copying or moving it to their own account. The other options are either incorrect or inefficient, as they involve creating unnecessary reader accounts, users, roles, or database replication.

https://learn.snowflake.com/en/certifications/snowpro-advanced-architect/

Question 6

Database DB1 has schema S1 which has one table, T1.

DB1 --> S1 --> T1

The retention period of EG1 is set to 10 days.

The retention period of s: is set to 20 days.

The retention period of t: Is set to 30 days.

The user runs the following command:

Drop Database DB1;

What will the Time Travel retention period be for T1?

A10 days

B20 days

C30 days

D37 days

Answer : C

The Time Travel retention period for T1 will be 30 days, which is the retention period set at the table level. The Time Travel retention period determines how long the historical data is preserved and accessible for an object after it is modified or dropped. The Time Travel retention period can be set at the account level, the database level, the schema level, or the table level. The retention period set at the lowest level of the hierarchy takes precedence over the higher levels. Therefore, the retention period set at the table level overrides the retention periods set at the schema level, the database level, or the account level. When the user drops the database DB1, the table T1 is also dropped, but the historical data is still preserved for 30 days, which is the retention period set at the table level. The user can use the UNDROP command to restore the table T1 within the 30-day period. The other options are incorrect because:

10 days is the retention period set at the database level, which is overridden by the table level.

20 days is the retention period set at the schema level, which is also overridden by the table level.

37 days is not a valid option, as it is not the retention period set at any level.

Understanding & Using Time Travel

AT | BEFORE

Snowflake Time Travel & Fail-safe

Question 7

How can the Snowpipe REST API be used to keep a log of data load history?

ACall insertReport every 20 minutes, fetching the last 10,000 entries.

BCall loadHistoryScan every minute for the maximum time range.

CCall insertReport every 8 minutes for a 10-minute time range.

DCall loadHistoryScan every 10 minutes for a 15-minutes range.

Answer : D

The Snowpipe REST API provides two endpoints for retrieving the data load history: insertReport and loadHistoryScan. The insertReport endpoint returns the status of the files that were submitted to the insertFiles endpoint, while the loadHistoryScan endpoint returns the history of the files that were actually loaded into the table by Snowpipe. To keep a log of data load history, it is recommended to use the loadHistoryScan endpoint, which provides more accurate and complete information about the data ingestion process. The loadHistoryScan endpoint accepts a start time and an end time as parameters, and returns the files that were loaded within that time range. The maximum time range that can be specified is 15 minutes, and the maximum number of files that can be returned is 10,000. Therefore, to keep a log of data load history, the best option is to call the loadHistoryScan endpoint every 10 minutes for a 15-minute time range, and store the results in a log file or a table. This way, the log will capture all the files that were loaded by Snowpipe, and avoid any gaps or overlaps in the time range. The other options are incorrect because:

Calling insertReport every 20 minutes, fetching the last 10,000 entries, will not provide a complete log of data load history, as some files may be missed or duplicated due to the asynchronous nature of Snowpipe. Moreover, insertReport only returns the status of the files that were submitted, not the files that were loaded.

Calling loadHistoryScan every minute for the maximum time range will result in too many API calls and unnecessary overhead, as the same files will be returned multiple times. Moreover, the maximum time range is 15 minutes, not 1 minute.

Calling insertReport every 8 minutes for a 10-minute time range will suffer from the same problems as option A, and also create gaps or overlaps in the time range.

Snowpipe REST API

Option 1: Loading Data Using the Snowpipe REST API

PIPE_USAGE_HISTORY

Snowflake ARA-R01 SnowPro Advanced: Architect Recertification Exam Practice Test