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NEW QUESTION # 44
In general, the monthly billing for database replication is proportional to which variables? (Select TWO).
- A. The number of times data moves across regions and/or cloud service providers between the primary and secondary database accounts
- B. The frequency of changes to the primary database as a result of data loading or DML operations
- C. The amount of table data in the primary database that changes as a result of data loading or DML operations
- D. The frequency of the secondary database refreshes from the primary database
- E. The number and size of warehouses defined in the primary account
Answer: B,C
Explanation:
Snowflake charges for database replication based on two categories: data transfer and compute resources1. Data transfer costs depend on the amount of data that is transferred from the primary database to the secondary database across regions and/or cloud service providers2. Compute resource costs depend on the use of Snowflake-provided compute resources to copy data between accounts across regions1. Both data transfer and compute resource costs are proportional to the frequency and amount of changes to the primary database as a result of data loading or DML operations3.
Therefore, the answer is A and B. The other options are not directly related to the replication billing, as the frequency of secondary database refreshes does not affect the amount of data transferred or copied4, and the number and size of warehouses defined in the primary account do not affect the replication process5.
NEW QUESTION # 45
A team of developers created a new schema for a new project. The developers are assigned the role DEV_TEAM which was set up using the following statements:
USE ROLE SECURITYADMIN;
CREATE ROLE DEV TEAM;
GRANT USAGE, CREATE SCHEMA ON DATABASE DEV_DB01 TO ROLE DEV_TEAM;
GRANT USAGE ON WAREHOUSE DEV_WH TO ROLE DEV_TEAM;
Each team member's access is set up using the following statements:
USE ROLE SECURITYADMIN;
CREATE ROLE JDOE_PROFILE;
CREATE USER JDOE LOGIN NAME = 'JDOE' DEFAULT_ROLE='JDOE_PROFILE';
GRANT ROLE JDOE_PROFILE TO USER JDOE;
GRANT ROLE DEV_TEAM TO ROLE JDOE_PROFILE;
New tables created by any of the developers are not accessible by the team as a whole.
How can an Administrator address this problem?
- A. Set up the new schema as a managed-access schema.
- B. Assign usage privilege on the virtual warehouse DEV_WH to the role JDOE_PROFILE.
- C. Set up future grants on the newly-created schemas.
- D. Assign ownership privilege to DEV_TEAM on the newly-created schema.
Answer: C
Explanation:
Explanation
According to the Snowflake documentation1, future grants are a way to automatically grant privileges on future objects of a specific type that are created in a database or schema. By setting up future grants on the newly-created schemas, the administrator can ensure that any tables created by the developers in those schemas will be accessible by the DEV_TEAM role, without having to grant privileges on each table individually. Option A is incorrect because assigning ownership privilege to DEV_TEAM on the newly-created schema does not grant privileges on the tables in the schema, only on the schema itself. Option B is incorrect because assigning usage privilege on the virtual warehouse DEV_WH to the role JDOE_PROFILE does not affect the access to the tables in the schemas, only the ability to use the warehouse.
Option D is incorrect because setting up the new schema as a managed-access schema does not grant privileges on the tables in the schema, but rather requires explicit grants for each table.
NEW QUESTION # 46
A large international company with many operating regions requires data to be shared bi-directionally among all offices (head office to regional offices and regional offices among themselves). This company is a Snowflake account holder with European operations deployed in Microsoft Azure (single region) while North American regional offices are using AWS (single region) as their deployment cloud. This setup is required to comply with Personal Identifiable Information (PII) regulations in some of the European countries. The corporate head office is in Europe.
How can this data be shared bi-directionally, while MINIMIZING costs?
- A. Use data replication everywhere to reduce costs associated with same-region sharing.
- B. Use the PUT command to move files to an Amazon S3 bucket and Azure Blobs, and use an external file management application to move files within the corporate VPC.
- C. Use bi-directional data sharing among offices in the same region and replication among offices across the continents.
- D. Move all the Snowflake accounts to a single region, and implement data sharing.
Answer: C
Explanation:
Explanation
According to the Snowflake documentation1, data sharing is a feature that allows sharing selected objects in a database in one account with other accounts in the same organization, without copying or transferring any data. Data sharing is supported across regions and across cloud platforms, but it requires enabling account database replication for both the source and target accounts2. Data replication is a feature that allows replicating objects from a source account to one or more target accounts in the same organization, providing read-only access for the replicated objects. Data replication is also supported across regions and across cloud platforms, but it incurs additional storage costs for the replicated data2. Therefore, the best way to share data bi-directionally among all offices, while minimizing costs, is to use data sharing among offices in the same region, which does not require replication or additional storage, and use replication among offices across the continents, which provides near real-time access to the shared data. Option A is incorrect because using data replication everywhere would increase the costs associated with additional storage and compute resources for the replicated data. Option B is incorrect because using the PUT command to move files to an Amazon S3 bucket and Azure Blobs, and using an external file management application to move files within the corporate VPC, would not leverage the benefits of Snowflake's data sharing and replication features, and would also incur additional costs and complexity for data transfer and synchronization. Option C is incorrect because moving all the Snowflake accounts to a single region would violate the PII regulations in some of the European countries, and would also incur additional costs and complexity for data migration and consolidation.
NEW QUESTION # 47
A Snowflake account is configured with SCIM provisioning for user accounts and has bi-directional synchronization for user identities. An Administrator with access to SECURITYADMIN uses the Snowflake UI to create a user by issuing the following commands:
use role USERADMIN;
create or replace role DEVELOPER_ROLE;
create user PTORRES PASSWORD = 'hello world!' MUST_CHANGE_PASSWORD = FALSE default_role = DEVELOPER_ROLE; The new user named PTORRES successfully logs in, but sees a default role of PUBLIC in the web UI. When attempted, the following command fails:
use DEVELOPER_ROLE;
Why does this command fail?
- A. The new role can only take effect after USERADMIN has logged out.
- B. USERADMIN needs to explicitly grant the DEVELOPER_ROLE to the new USER.
- C. The DEVELOPER_ROLE needs to be granted to SYSADMIN before user PTORRES will be able to use the role.
- D. The new role will only take effect once the identity provider has synchronized by way of SCIM with the Snowflake account.
Answer: B
Explanation:
Explanation
According to the Snowflake documentation1, creating a user with a default role does not automatically grant that role to the user. The user must be explicitly granted the role by the role owner or a higher-level role.
Therefore, the USERADMIN role, which created the DEVELOPER_ROLE, needs to explicitly grant the DEVELOPER_ROLE to the new user PTORRES using the GRANT ROLE command. Otherwise, the user PTORRES will not be able to use the DEVELOPER_ROLE and will see the default role of PUBLIC in the web UI. Option A is incorrect because the DEVELOPER_ROLE does not need to be granted to SYSADMIN before user PTORRES can use the role. Option B is incorrect because the new role can take effect immediately after it is created and granted to the user, and does not depend on the USERADMIN role logging out. Option D is incorrect because the new role will not be affected by the identity provider synchronization, as it is created and managed in Snowflake.
NEW QUESTION # 48
A retailer uses a TRANSACTIONS table (100M rows, 1.2 TB) that has been clustered by the STORE_ID column (varchar(50)). The vast majority of analyses on this table are grouped by STORE_ID to look at store performance.
There are 1000 stores operated by the retailer but most sales come from only 20 stores. The Administrator notes that most queries are currently experiencing poor pruning, with large amounts of bytes processed by even simple queries.
Why is this occurring?
- A. The STORE_ID should be numeric.
- B. The table is not big enough to take advantage of the clustering key.
- C. The cardinality of the stores to transaction count ratio is too low to use the STORE_ID as a clustering key.
- D. Sales across stores are not uniformly distributed.
Answer: D
Explanation:
Explanation
According to the Snowflake documentation1, clustering keys are most effective when the data is evenly distributed across the key values. If the data is skewed, such as in this case where most sales come from only
20 stores out of 1000, then the micro-partitions will not be well-clustered and the pruning will be poor. This means that more bytes will be scanned by queries, even if they filter by STORE_ID. Option A is incorrect because the data type of the clustering key does not affect the pruning. Option B is incorrect because the table is large enough to benefit from clustering, if the data was more balanced. Option D is incorrect because the cardinality of the clustering key is not relevant for pruning, as long as the key values are distinct.
1: Considerations for Choosing Clustering for a Table | Snowflake Documentation
NEW QUESTION # 49
Which tasks can be performed by the ORGADMIN role? (Select THREE).
- A. View a list of all regions enabled for the organization.
- B. Create secure views on application tables within the organization.
- C. View usage information for all accounts in the organization.
- D. Create one or more accounts in the organization.
- E. Create a reader account to share data with another organization.
- F. Perform zero-copy cloning on account data.
Answer: A,C,D
Explanation:
Explanation
A user with the ORGADMIN role can perform the following tasks1:
*Create one or more accounts in the organization.
*View a list of all regions enabled for the organization.
*View usage information for all accounts in the organization.
Option C is incorrect because creating secure views on application tables is not a function of the ORGADMIN role, but rather a function of the roles that have access to the tables and schemas within the accounts. Option E is incorrect because performing zero-copy cloning on account data is not a function of the ORGADMIN role, but rather a function of the roles that have the CLONE privilege on the objects within the accounts. Option F is incorrect because creating a reader account to share data with another organization is not a function of the ORGADMIN role, but rather a function of the roles that have the CREATE SHARE privilege on the objects within the accounts.
NEW QUESTION # 50
What are the MINIMUM grants required on the database, schema, and table for a stream to be properly created and managed?
- A. Database: Usage
Schema: Usage, Create Stream
Table: Select - B. Database: Usage
Schema: Usage
Table: Select - C. Database: Usage
Schema: Usage
Table: Select, Create Stream - D. Database: Usage, Create Stream
Schema: Usage
Table: Select
Answer: C
NEW QUESTION # 51
An organization's sales team leverages this Snowflake query a few times a day:
SELECT CUSTOMER ID, CUSTOMER_NAME, ADDRESS, PHONE NO
FROM CUSTOMERS
WHERE LAST UPDATED BETWEEN TO_DATE (CURRENT_TIMESTAMP) AND (TO_DATE (CURRENT_TIMESTAMP) -7); What can the Snowflake Administrator do to optimize the use of persisted query results whenever possible?
- A. Wrap the query in a User-Defined Function (UDF) to match syntax execution.
- B. Assign everyone on the sales team to the same security role.
- C. Leverage the CURRENT_DATE function for date calculations.
- D. Assign everyone on the sales team to the same virtual warehouse.
Answer: C
Explanation:
According to the web search results from my predefined tool search_web, one of the factors that affects the reuse of persisted query results is the exact match of the query syntax1. If the query contains functions that return different values for successive runs, such as CURRENT_TIMESTAMP, then the query will not match the previous query and will not benefit from the cache. To avoid this, the query should use functions that return consistent values for the same day, such as CURRENT_DATE, which returns the current date without the time component2. Option A is incorrect because wrapping the query in a UDF does not guarantee the syntax match, as the UDF may also contain dynamic functions. Option B is incorrect because the virtual warehouse does not affect the persisted query results, which are stored at the account level1. Option C is incorrect because the security role does not affect the persisted query results, as long as the role has the necessary privileges to access the tables and views used in the query1.
1: Using Persisted Query Results | Snowflake Documentation 2: Date and Time Functions | Snowflake Documentation
NEW QUESTION # 52
If the query matches the definition, will Snowflake always dynamically rewrite the query to use a materialized view?
- A. No, because the materialized view may not be up-to-date.
- B. Yes, because materialized views are always faster.
- C. No, because joins are not supported by materialized views.
- D. No, because the optimizer might decide against it.
Answer: D
Explanation:
Explanation
Snowflake's query optimizer can automatically rewrite queries against the base table or regular views to use the materialized view instead, if the query matches the definition of the materialized view1. However, this is not always guaranteed, as the optimizer might decide against using the materialized view based on various factors, such as the freshness of the data, the size of the result set, the complexity of the query, and the availability of the materialized view2. Therefore, the answer is no, because the optimizer might decide against it.
NEW QUESTION # 53
An Administrator is evaluating a complex query using the EXPLAIN command. The Globalstats operation indicates 500 partitionsAssigned.
The Administrator then runs the query to completion and opens the Query Profile. They notice that the partitions scanned value is 429.
Why might the actual partitions scanned be lower than the estimate from the EXPLAIN output?
- A. The GlobalStats partition assignment includes the micro-partitions that will be assigned for preservation of the query results.
- B. The EXPLAIN results always include a 10-15% safety factor in order to provide conservative estimates.
- C. In-flight data compression will result in fewer micro-partitions being scanned at the virtual warehouse layer than were identified at the storage layer.
- D. Runtime optimizations such as join pruning can reduce the number of partitions and bytes scanned during query execution.
Answer: D
Explanation:
Explanation
The EXPLAIN command returns the logical execution plan for a query, which shows the upper bound estimates for the number of partitions and bytes that might be scanned by the query1. However, these estimates do not account for the runtime optimizations that Snowflake performs to improve the query performance and reduce the resource consumption2. One of these optimizations is join pruning, which eliminates unnecessary partitions from the join inputs based on the join predicates2. This can result in fewer partitions and bytes scanned than the estimates from the EXPLAIN output3. Therefore, the actual partitions scanned value in the Query Profile can be lower than the partitionsAssigned value in the EXPLAIN output4.
NEW QUESTION # 54
Which actions are considered breaking changes to data that is shared with consumers in the Snowflake Marketplace? (Select TWO).
- A. Renaming a table
- B. Dropping a column from a table
- C. Adding region availability to the listing
- D. Unpublishing the data listing
- E. Deleting data from a table
Answer: A,B
Explanation:
Explanation
According to the Snowflake documentation1, breaking changes are changes that affect the schema or structure of the shared data, such as dropping or renaming a column or a table. These changes may cause errors or unexpected results for the consumers who query the shared data. Deleting data from a table, unpublishing the data listing, or adding region availability to the listing are not breaking changes, as they do not alter the schema or structure of the shared data.
1: Managing Data Listings in Snowflake Data Marketplace | Snowflake Documentation
NEW QUESTION # 55
A company has many users in the role ANALYST who routinely query Snowflake through a reporting tool. The Administrator has noticed that the ANALYST users keep two small clusters busy all of the time, and occasionally they need three or four clusters of that size.
Based on this scenario, how should the Administrator set up a virtual warehouse to MOST efficiently support this group of users?
- A. Create a multi-cluster warehouse with MIN_CLUSTERS set to 1. Give MANAGE privileges to the ANALYST role so this group can start and stop the warehouse, and increase the number of clusters as needed.
- B. Create a standard X-Large warehouse, which is equivalent to four small clusters. Set the warehouse to auto-resume and auto-suspend, and give USAGE privileges to the ANALYST role.
- C. Create a multi-cluster warehouse with MIN_CLUSTERS set to 2. Set the warehouse to auto-resume and auto-suspend, and give USAGE privileges to the ANALYST role. Allow the warehouse to auto-scale.
- D. Create four virtual warehouses (sized Small through XL) and set them to auto-suspend and auto-resume. Have users in the ANALYST role select the appropriate warehouse based on how many queries are being run.
Answer: C
Explanation:
According to the Snowflake documentation1, a multi-cluster warehouse is a virtual warehouse that consists of multiple clusters of compute resources that can scale up or down automatically to handle the concurrency and performance needs of the queries submitted to the warehouse. A multi-cluster warehouse has a minimum and maximum number of clusters that can be specified by the administrator. Option B is the most efficient way to support the group of users, as it allows the administrator to create a multi-cluster warehouse with MIN_CLUSTERS set to 2, which means that the warehouse will always have two clusters running to handle the standard workload. The warehouse can also auto-scale up to the maximum number of clusters (which can be set according to the peak workload) when there is a spike in demand, and then scale down when the demand decreases. The warehouse can also auto-resume and auto-suspend, which means that the warehouse will automatically start when a query is submitted and automatically stop after a period of inactivity. The administrator can also give USAGE privileges to the ANALYST role, which means that the users can use the warehouse to execute queries and load data, but not modify or operate the warehouse. Option A is not efficient, as it requires the users to manually start and stop the warehouse, and increase the number of clusters as needed, which can be time-consuming and error-prone. Option C is not efficient, as it creates a standard X-Large warehouse, which is equivalent to four small clusters, which may be more than needed for the standard workload, and may not be enough for the peak workload. Option D is not efficient, as it creates four virtual warehouses of different sizes, which can be confusing and cumbersome for the users to select the appropriate warehouse based on how many queries are being run, and may also result in wasted resources and costs.
NEW QUESTION # 56
A company has many users in the role ANALYST who routinely query Snowflake through a reporting tool.
The Administrator has noticed that the ANALYST users keep two
small clusters busy all of the time, and occasionally they need three or four clusters of that size.
Based on this scenario, how should the Administrator set up a virtual warehouse to MOST efficiently support this group of users?
- A. Create a multi-cluster warehouse with MIN_CLUSTERS set to 1. Give MANAGE privileges to the ANALYST role so this group can start and stop the warehouse, and increase the number of clusters as needed.
- B. Create a standard X-Large warehouse, which is equivalent to four small clusters. Set the warehouse to auto-resume and auto-suspend, and give USAGE privileges to the ANALYST role.
- C. Create four virtual warehouses (sized Small through XL) and set them to auto-suspend andauto-resume.
Have users in the ANALYST role select the appropriate warehouse based on how many queries are being run. - D. Create a multi-cluster warehouse with MIN_CLUSTERS set to 2. Set the warehouse to auto-resume and auto-suspend, and give USAGE privileges to the ANALYST role. Allow the warehouse to auto-scale.
Answer: D
Explanation:
Explanation
According to the Snowflake documentation1, a multi-cluster warehouse is a virtual warehouse that consists of multiple clusters of compute resources that can scale up or down automatically to handle the concurrency and performance needs of the queries submitted to the warehouse. A multi-cluster warehouse has a minimum and maximum number of clusters that can be specified by the administrator. Option B is the most efficient way to support the group of users, as it allows the administrator to create a multi-cluster warehouse with MIN_CLUSTERS set to 2, which means that the warehouse will always have two clusters running to handle the standard workload. The warehouse can also auto-scale up to the maximum number of clusters (which can be set according to the peak workload) when there is a spike in demand, and then scale down when the demand decreases. The warehouse can also auto-resume and auto-suspend, which means that the warehouse will automatically start when a query is submitted and automatically stop after a period of inactivity. The administrator can also give USAGE privileges to the ANALYST role, which means that the users can use the warehouse to execute queries and load data, but not modify or operate the warehouse. Option A is not efficient, as it requires the users to manually start and stop the warehouse, and increase the number of clusters as needed, which can be time-consuming and error-prone. Option C is not efficient, as it creates a standard X-Large warehouse, which is equivalent to four small clusters, which may be more than needed for the standard workload, and may not be enough for the peak workload. Option D is not efficient, as it creates four virtual warehouses of different sizes, which can be confusing and cumbersome for the users to select the appropriate warehouse based on how many queries are being run, and may also result in wasted resources and costs.
NEW QUESTION # 57
A Snowflake Administrator wants to create a virtual warehouse that supports several dashboards, issuing various queries on the same database.
For this warehouse, why should the Administrator consider setting AUTO_SUSPEND to 0 or NULL?
- A. To save costs on warehouse shutdowns and startups for different queries
- B. To keep the data cache warm to support good performance of similar queries
- C. To keep the query result cache warm for good performance on repeated queries
- D. To save costs by running the warehouse as little as possible
Answer: B
Explanation:
According to the Snowflake documentation1, the AUTO_SUSPEND property specifies the number of seconds of inactivity after which a warehouse is automatically suspended. If the property is set to 0 or NULL, the warehouse never suspends automatically. For a warehouse that supports several dashboards, issuing various queries on the same database, setting AUTO_SUSPEND to 0 or NULL can help to keep the data cache warm, which means that the data used by the queries is already loaded into the warehouse memory and does not need to be fetched from the storage layer. This can improve the performance of similar queries that access the same data. Option A is incorrect because setting AUTO_SUSPEND to 0 or NULL does not save costs on warehouse shutdowns and startups, but rather increases the costs by keeping the warehouse running continuously. Option B is incorrect because setting AUTO_SUSPEND to 0 or NULL does not run the warehouse as little as possible, but rather runs the warehouse as much as possible. Option D is incorrect because setting AUTO_SUSPEND to 0 or NULL does not affect the query result cache, which is a separate cache that stores the results of previous queries for a period of time. The query result cache is not dependent on the warehouse state, but on the query criteria2.
NEW QUESTION # 58
A Snowflake Administrator needs to persist all virtual warehouse configurations for auditing and backups.
Given a table already exists with the following schema:
Table Name:VWH_META
Column 1:SNAPSHOT_TIME TIMESTAMP_NTZ
Column 2:CONFIG VARIANT
Which commands should be executed to persist the warehouse data at the time of execution in JSON format in the table VWH META?
- A. 1. SHOW WAREHOUSES;
2. INSERT INTO VWH META
SELECT CURRENT TIMESTAMP (), *
FROM TABLE (RESULT_SCAN (LAST_QUERY_ID ())) ; - B. 1. SHOW WAREHOUSES;
2. INSERT INTO VWH META
SELECT CURRENT TIMESTAMP (),
FROM TABLE (RESULT_SCAN (LAST_QUERY_ID(1) ) ) ; - C. 1. SHOW WAREHOUSES;
2. INSERT INTO VWH META
SELECT CURRENT TIMESTAMP (), *
FROM TABLE (RESULT_SCAN (SELECT
LAST QUERY ID(-1))); - D. 1. SHOW WAREHOUSES;
2. INSERT INTO VWH_META
SELECT CURRENT_TIMESTAMP (),
OBJECT CONSTRUCT (*)
FROM TABLE (RESULT_SCAN (LAST_QUERY_ID ()));
Answer: D
Explanation:
Explanation
According to the Using Persisted Query Results documentation, the RESULT_SCAN function allows you to query the result set of a previous command as if it were a table. The LAST_QUERY_ID function returns the query ID of the most recent statement executed in the current session. Therefore, the combination of these two functions can be used to access the output of the SHOW WAREHOUSES command, which returns the configurations of all the virtual warehouses in the account. However, to persist the warehouse data in JSON format in the table VWH_META, the OBJECT_CONSTRUCT function is needed to convert the output of the SHOW WAREHOUSES command into a VARIANT column. The OBJECT_CONSTRUCT function takes a list of key-value pairs and returns a single JSON object. Therefore, the correct commands to execute are:
1.SHOW WAREHOUSES;
2.INSERT INTO VWH_META SELECT CURRENT_TIMESTAMP (), OBJECT_CONSTRUCT (*) FROM TABLE (RESULT_SCAN (LAST_QUERY_ID ())); The other options are incorrect because:
*A. This option does not use the OBJECT_CONSTRUCT function, so it will not persist the warehouse data in JSON format. Also, it is missing the * symbol in the SELECT clause, so it will not select any columns from the result set of the SHOW WAREHOUSES command.
*B. This option does not use the OBJECT_CONSTRUCT function, so it will not persist the warehouse data in JSON format. It will also try to insert multiple columns into a single VARIANT column, which will cause a type mismatch error.
*D. This option does not use the OBJECT_CONSTRUCT function, so it will not persist the warehouse data in JSON format. It will also try to use the RESULT_SCAN function on a subquery, which is not supported. The RESULT_SCAN function can only be used on a query ID or a table name.
NEW QUESTION # 59
......
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