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Snowflake ADA-C01 Exam Syllabus Topics:

Topic
Details

Topic 1

Implement and manage data governance in Snowflake
Data Sharing, Data Exchange, and Snowflake Marketplace

Topic 2

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

Topic 3

Given a scenario, create and manage access control
Given a scenario, implement resource monitors

Topic 4

Interpret and make recommendations for data clustering
Manage DML locking and concurrency in Snowflake

Topic 5

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

Topic 6

Manage and implement data sharing
Given a set of business requirements, establish access control architecture

Snowflake SnowPro Advanced Administrator Sample Questions (Q18-Q23):

NEW QUESTION # 18
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. Sales across stores are not uniformly distributed.
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. The STORE_ID should be numeric.

Answer: A

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 # 19
The following commands were executed:
Grant usage on database PROD to role PROD_ANALYST;
Grant usage on database PROD to role PROD_SUPERVISOR;
Grant ALL PRIVILEGES on schema PROD. WORKING to role PROD_ANALYST;
Grant ALL PRIVILEGES on schema PROD. WORKING to role PROD_SUPERVISOR;
Grant role PROD ANALYST to user A;
Grant role PROD SUPERVISOR to user B;
What authority does each user have on the WORKING schema?

A. Only user B can create tables, because all privileges were transferred to PROD_SUPERVISOR.
B. Tables created by either user A or user B will be visible to both users.
C. Both user A and user B can create tables in the PROD. WORKING schema.
D. All existing tables in schema PROD. WORKING will be visible to both users.

Answer: C

NEW QUESTION # 20
An Administrator needs to create a sample of the table LINEITEM. The sample should not be repeatable and the sampling function should take the data by blocks of rows.
What select command will generate a sample of 20% of the table?

A. select * from LINEITEM sample system (20);
B. select * from LINEITEM sample bernoulli (20);
C. select * from LINEITEM tablesample system (20) seed (1);
D. select * from LINEITEM tablesample block (20 rows);

Answer: A

Explanation:
Explanation
This command will generate a sample of 20% of the table by using the SYSTEM (or BLOCK) sampling method, which selects each block of rows with a probability of 20/100. This method is suitable for taking data by blocks of rows, as the question requires. According to the Snowflake documentation, "SYSTEM (or BLOCK): Includes each block of rows with a probability of p/100. Similar to flipping a weighted coin for each block of rows. This method does not support fixed-size sampling." The other options are either incorrect or do not meet the requirements of the question. Option A uses the BERNOULLI (or ROW) sampling method, which selects each row with a probability of 20/100, but does not take data by blocks of rows. Option C uses the BLOCK sampling method, but specifies a fixed number of rows (20) instead of a percentage (20%).
Option D uses the SYSTEM sampling method, but specifies a seed value (1), which makes the sampling repeatable, contrary to the question.

NEW QUESTION # 21
An Administrator has been asked to support the company's application team need to build a loyalty program for its customers. The customer table contains Personal Identifiable Information (PII), and the application team's role is DEVELOPER.
CREATE TABLE customer_data (
customer_first_name string,
customer_last_name string,
customer_address string,
customer_email string,
... some other columns,
);
The application team would like to access the customer data, but the email field must be obfuscated.
How can the Administrator protect the sensitive information, while maintaining the usability of the data?

A. Use the CURRENT_ROLE context function to integrate with a masking policy on the fields that contain sensitive data.
B. Create a view on the customer_data table to eliminate the email column by omitting it from the SELECT clause. Grant the role DEVELOPER access to the view.
C. Create a separate table for all the non-Pll columns and grant the role DEVELOPER access to the new table.
D. Use the CURRENT_ROLE and CURRENT_USER context functions to integrate with a secure view and filter the sensitive data.

Answer: A

NEW QUESTION # 22
A user with the proper role issues the following commands when setting up and activating network policies:
CREATE OR REPLACE NETWORK POLICY foo_policy
ALLOWED_IP_LIST = ( '1.1.1.0/24', '2.2.2.0/24' , '3.3. 3. 0/24' )
BLOCKED IP LIST = ( '1.1.1.1')
COMMENT = 'Account level policy';
ALTER ACCOUNT SET NETWORK_POLICY=FOO_POLICY;
CREATE OR REPLACE NETWORK POLICY bar_policy
ALLOWED_IP_LIST = ('3.3.3.0/24')
BLOCKED IP LIST = ('3.3.3.10')
COMMENT = 'user level policy';
ALTER USER userl SET NETWORK_POLICY=BAR_POLICY;
Afterwards, user1 attempts to log in to Snowflake from IP address 3.3.3.10.
Will the login be successful?

A. No, because 3.3.3.10 is found in the BLOCKED_IP_LIST of bar_policy.
B. Yes, because 3.3.3.10 is found in the ALLOWED_IP_LIST of foo_policy.
C. No, because 3.3.3.10 is not found in the ALLOWED_IP_LIST of foo_policy.
D. Yes, because 3.3.3.10 is found in the ALLOWED_IP_LIST of bar_policy.

Answer: A

Explanation:
According to the Snowflake documentation1, network policies are a feature that allows restricting access to your account based on user IP address. A network policy can be applied to an account, a user, or a security integration, and can specify a list of allowed IP addresses and a list of blocked IP addresses. If there are network policies applied to more than one of these, the most specific network policy overrides more general network policies. In this case, the user1 has a network policy (bar_policy) applied to them, which overrides the account-level network policy (foo_policy). The bar_policy allows access only from the IP range 3.3.3.0/24, and blocks access from the IP address 3.3.3.10. Therefore, the user1 will not be able to log in to Snowflake from IP address 3.3.3.10, as it is found in the BLOCKED_IP_LIST of bar_policy. Option A is incorrect because the ALLOWED_IP_LIST of bar_policy does not override the BLOCKED_IP_LIST of bar_policy. Option C is incorrect because the ALLOWED_IP_LIST of foo_policy does not apply to user1, as it is overridden by the user-level network policy. Option D is incorrect because the ALLOWED_IP_LIST of foo_policy does not matter, as it is overridden by the user-level network policy.

NEW QUESTION # 23
......

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