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100% Pass Snowflake - High Pass-Rate ADA-C01 - SnowPro Advanced Administrator Latest Training

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Snowflake ADA-C01 Exam Syllabus Topics:

Topic
Details

Topic 1

Set up and manage network and private connectivity
Given a scenario, manage Snowflake Time Travel and Fail-safe

Topic 2

Given a scenario, configure access controls
Set up and manage security administration and authorization

Topic 3

Snowflake Security, Role-Based Access Control (RBAC), and User Administration
Disaster Recovery, Backup, and Data Replication

Topic 4

Given a scenario, manage databases, tables, and views
Manage organizations and access control

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Snowflake SnowPro Advanced Administrator Sample Questions (Q69-Q74):

NEW QUESTION # 69
A company enabled replication between accounts and is ready to replicate data across regions in the same cloud service provider.
The primary database object is : PROD_AWS_EAST. Location : AWS_EAST
The secondary database object is : PROD_AWS_WEST. Location : AWS_WEST
What command and account location is needed to refresh the data?

A. Location : AWS_EAST
Command : REFRESH DATABASE PROD_AWS_WEST REFRESH;
B. Location : AWS EAST
Command: ALTER DATABASE PROD_AWS_WEST REFRESH;
C. Location : AWS_WEST
Command : REFRESH DATABASE PROD_AWS WEST REFRESH;
D. Location : AWS_WEST
Command : ALTER DATABASE PROD AWS WEST REFRESH;

Answer: C

Explanation:
Explanation
The REFRESH DATABASE command is used to refresh a secondary database with the latest data and metadata from the primary database1. The command must be executed in the target account where the secondary database resides2. Therefore, the answer is A, as the location is AWS_WEST and the command is REFRESH DATABASE PROD_AWS_WEST REFRESH. The other options are incorrect because they either use the wrong location, the wrong command, or the wrong database name.

NEW QUESTION # 70
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 2. Set the warehouse to auto-resume and auto-suspend, and give USAGE privileges to the ANALYST role. Allow the warehouse to auto-scale.
B. 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.
C. 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.
D. 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.

Answer: A

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 # 71
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 table is not big enough to take advantage of the clustering key.
B. The cardinality of the stores to transaction count ratio is too low to use the STORE_ID as a clustering key.
C. The STORE_ID should be numeric.
D. Sales across stores are not uniformly distributed.

Answer: D

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 # 72
What are characteristics of Dynamic Data Masking? (Select TWO).

A. A masking policy can be applied to the VALUE column of an external table.
B. A single masking policy can be applied to columns in different tables.
C. A masking policy that is currently set on a table can be dropped.
D. A single masking policy can be applied to columns with different data types.
E. The role that creates the masking policy will always see unmasked data in query results.

Answer: B,D

Explanation:
Explanation
According to the Using Dynamic Data Masking documentation, Dynamic Data Masking is a feature that allows you to alter sections of data in table and view columns at query time using a predefined masking strategy. The following are some of the characteristics of Dynamic Data Masking:
*A single masking policy can be applied to columns in different tables. This means that you can write a policy once and have it apply to thousands of columns across databases and schemas.
*A single masking policy can be applied to columns with different data types. This means that you can use the same masking strategy for columns that store different kinds of data, such as strings, numbers, dates, etc.
*A masking policy that is currently set on a table can be dropped. This means that you can remove the masking policy from the table and restore the original data visibility.
*A masking policy can be applied to the VALUE column of an external table. This means that you can mask data that is stored in an external stage and queried through an external table.
*The role that creates the masking policy will always see unmasked data in query results. This is not true, as the masking policy can also apply to the creator role depending on the execution context conditions defined in the policy. For example, if the policy specifies that only users with a certain custom entitlement can see the unmasked data, then the creator role will also need to have that entitlement to see the unmasked data.

NEW QUESTION # 73
What access control policy will be put into place when future grants are assigned to both database and schema objects?

A. Schema privileges will take precedence over database privileges.
B. An access policy combining both the database object and the schema object will be used, with the most permissive policy taking precedence.
C. An access policy combining both the database object and the schema object will be used, with the most restrictive policy taking precedence.
D. Database privileges will take precedence over schema privileges.

Answer: A

Explanation:
When future grants are defined on the same object type for a database and a schema in the same database, the schema-level grants take precedence over the database level grants, and the database level grants are ignored4. This behavior applies to privileges on future objects granted to one role or different roles4. Future grants allow defining an initial set of privileges to grant on new (i.e. future) objects of a certain type in a database or a schema3. As soon as the new objects are created inside the database or schema, the predefined set of privileges are assigned to the object automatically without any manual intervention3.

NEW QUESTION # 74
......

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Samuel Ramirez Samuel Ramirez

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