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Snowflake ADA-C02 Exam Syllabus Topics:

Section	Weight	Objectives
Account and Security Administration	30%	- Access control and security 1. Data security and masking policies 2. User and privilege management 3. Authentication and network policies 4. Role-based access control (RBAC) design - Account configuration and management 1. Cost management and billing 2. Edition and feature configuration 3. Organization and account structures
Performance Monitoring and Tuning	20%	- Query performance analysis 1. Materialized views and search optimization 2. Indexing and clustering keys optimization 3. Query profiling and execution plans - System monitoring and troubleshooting 1. Error logging and troubleshooting techniques 2. Workload isolation and concurrency control 3. Account and warehouse usage metrics
Resource Management and Optimization	25%	- Database and object management 1. Storage management and clustering 2. Schema design and object lifecycle 3. Data retention and time travel - Virtual warehouse administration 1. Cost optimization strategies 2. Workload management and resource monitoring 3. Warehouse sizing, scaling and suspension
Data Sharing and Marketplace	10%	- Snowflake Marketplace 1. Listing and consuming data products 2. Marketplace administration and governance - Secure data sharing 1. Shares, readers and data providers 2. Secure views and data consumption
Business Continuity and Disaster Recovery	15%	- Data protection and replication 1. Time travel and fail-safe configuration 2. Cross-region and cross-cloud replication 3. Database cloning and backup strategies - Disaster recovery planning 1. Failover and failback procedures 2. Recovery time and point objectives

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Snowflake SnowPro Advanced Administrator ADA-C02 Sample Questions (Q15-Q20):

NEW QUESTION # 15
What steps are required to set up OKTAAPI token integration in Snowflake?

A. use role ACCOUNTADMIN;
select system$ generate_scim_access_token ('OKTA PROVISIONING');
Update these steps every 12 months.
B. create or replace security integration okta_provisioning
type = scim
scim_client = 'okta'
run_as_role = 'OKTA PROVISIONER';
Update this step every 6 months.
C. grant role okta_provisioner to role ACCOUNTADMIN;
create or replace security integration okta_provisioning
type = scim
scim_client = 'okta'
run_as_role = 'OKTA PROVISIONER';
Update these steps every 12 months.
D. use role ACCOUNTADMIN;
select system$ generate_scim_access_token ('OKTA PROVISIONING');
Update these steps every 6 months.

Answer: D

Explanation:
Use the ACCOUNTADMIN role to generate the SCIM access token for Okta with system$generate_scim_access_token.
The access token must be regenerated and updated in Okta every 6 months for continued integration.

NEW QUESTION # 16
A user has enrolled in Multi-factor Authentication (MFA) for connecting to Snowflake. The user informs the Snowflake Administrator that they lost their mobile phone the previous evening.
Which step should the Administrator take to allow the user to log in to the system, without revoking their MFA enrollment?

A. Instruct the user to append the normal URL with /?mode=mfa_bypass&code= to log on.
B. Alter the user and set DISABLE_MFA to true, which will suspend the MFA requirement for 24 hours.
C. Alter the user and set MINS_TO_BYPASS_MFA to a value that will disable MFA long enough for the user to log in.
D. Instruct the user to connect to Snowflake using SnowSQL, which does not support MFA authentication.

Answer: C

Explanation:
The correct step is to use ALTER USER ... SET MINS_TO_BYPASS_MFA = <minutes> which temporarily suspends MFA for the specified duration, allowing the user to log in and reconfigure their MFA without revoking enrollment. This preserves MFA setup and is the supported method in Snowflake.

NEW QUESTION # 17
What are characteristics of Dynamic Data Masking? (Choose two.)

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

Answer: A,C

Explanation:
A masking policy applied to a table or column can be dropped or replaced as needed.
The same masking policy can be reused and applied to columns in different tables.

NEW QUESTION # 18
In general, the monthly billing for database replication is proportional to which variables? (Choose two.)

A. The frequency of the secondary database refreshes from the primary database
B. The number of times data moves across regions and/or cloud service providers between the primary and secondary database accounts
C. The amount of table data in the primary database that changes as a result of data loading or DML operations
D. The number and size of warehouses defined in the primary account
E. The frequency of changes to the primary database as a result of data loading or DML operations

Answer: A,C

Explanation:
Replication billing is proportional to the amount of table data in the primary database that changes and needs to be replicated.
The frequency of secondary database refreshes determines how often changed data is transferred, directly impacting replication costs.

NEW QUESTION # 19
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. Sales across stores are not uniformly distributed.
C. The cardinality of the stores to transaction count ratio is too low to use the STORE_ID as a clustering key.
D. The table is not big enough to take advantage of the clustering key.

Answer: B

Explanation:
Clustering is effective when data distribution across the clustering key is relatively uniform. In this case, most transactions are concentrated in only 20 of the 1000 stores, leading to skewed data distribution. This imbalance causes poor pruning, since large portions of micro-partitions still need to be scanned, even for queries targeting specific stores.

NEW QUESTION # 20
......

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