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Snowflake ADA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	Disaster Recovery, Backup, and Data Replication: This section of the exam measures the skills of Disaster Recovery Engineers and Cloud Operations Managers and covers Snowflake methods for ensuring business continuity. Candidates must understand how to replicate databases and account-level objects, implement failover strategies, and perform backup and restoration through Time Travel and Fail-safe features. The domain emphasizes replication across accounts, handling data consistency during failover, and applying cost-efficient disaster recovery strategies to maintain availability during outages or regional failures.
Topic 2	Data Sharing, Data Exchange, and Snowflake Marketplace: This section of the exam measures the skills of Data Integration Specialists and Data Platform Administrators and covers managing and implementing data-sharing solutions within Snowflake. It evaluates understanding of data sharing models across regions and clouds, secure data sharing methods, and managing provider-consumer relationships. The domain also includes the use of Snowflake Data Exchange and Marketplace to publish, consume, and manage data listings, ensuring secure collaboration and efficient data monetization.
Topic 3	Snowflake Security, Role-Based Access Control (RBAC), and User Administration: This section of the exam measures the skills of Snowflake Administrators and Cloud Security Engineers and covers authentication, access control, and network management in Snowflake. Candidates must understand how to configure authentication methods such as SSO, MFA, OAuth, and key-pair authentication, and how to manage network policies and private connectivity. The domain also tests knowledge of user and role management using SCIM, designing access control architecture, and applying the RBAC framework to ensure secure user authorization and data protection within Snowflake environments.
Topic 4	Account Management and Data Governance: This section of the exam measures the skills of Data Governance Managers and Database Administrators and covers account organization, access control, and regulatory data protection. Candidates will learn how to manage organizational accounts, encryption keys, and Tri-Secret Secure implementations. It focuses on applying best practices in ORGADMIN and ACCOUNTADMIN roles, implementing masking and row access policies, and performing data classification and tagging. The domain also emphasizes data auditing, account identifiers, and effective management of tables, views, and query operations to support enterprise-wide governance standards.
Topic 5	Performance Monitoring and Tuning: This section of the exam measures the skills of Cloud Infrastructure Engineers and Performance Analysts and focuses on optimizing Snowflake compute and storage resources. Candidates will need to understand how to configure and manage virtual warehouses, evaluate query profiles, and apply caching and clustering strategies for performance tuning. It also includes monitoring concurrency, resource utilization, and implementing cost optimization strategies. The ability to interpret, explain plans, apply search optimization, and manage cost controls is key for maintaining efficient Snowflake environments.

Snowflake SnowPro Advanced Administrator Sample Questions (Q16-Q21):

NEW QUESTION # 16
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 (SELECT
LAST QUERY ID(-1)));
C. 1. SHOW WAREHOUSES;
2. INSERT INTO VWH_META
SELECT CURRENT_TIMESTAMP (),
OBJECT CONSTRUCT (*)
FROM TABLE (RESULT_SCAN (LAST_QUERY_ID ()));
D. 1. SHOW WAREHOUSES;
2. INSERT INTO VWH META
SELECT CURRENT TIMESTAMP (),
FROM TABLE (RESULT_SCAN (LAST_QUERY_ID(1) ) ) ;

Answer: C

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 # 17
A Snowflake Administrator is investigating why a query is not re-using the persisted result cache.
The Administrator found the two relevant queries from the SNOWFLAKE. ACCOUNT_USAGE.
QUERY_HISTORY view:

Why is the second query re-scanning micro-partitions instead of using the first query's persisted result cache?

A. The second query includes a CURRENT_DATE () function.
B. The queries are executed with two different virtual warehouses.
C. The second query includes a CURRENT_TIMESTAMP () function.
D. The queries are executed with two different roles.

Answer: B

Explanation:
Explanation
The inclusion of the CURRENT_TIMESTAMP() function in the second query prevents it from re-using the first query's persisted result cache because this function makes each execution unique due to the constantly changing timestamp. According to the Snowflake documentation, "The query does not include non-reusable functions, which return different results for successive runs of the same query. UUID_STRING, RANDOM, and RANDSTR are good examples of non-reusable functions." The CURRENT_TIMESTAMP() function is another example of a non-reusable function, as it returns the current date and time at the start of query execution, which varies for each run. Therefore, the second query is not identical to the first query, and the result cache is not reused. The other options are either incorrect or irrelevant to the question. Option B is incorrect, as the CURRENT_DATE() function is a reusable function, as it returns the same value for all queries executed within the same day. Option C is irrelevant, as the virtual warehouse used to execute the query does not affect the result cache reuse. Option D is also irrelevant, as the role used to execute the query does not affect the result cache reuse, as long as the role has the necessary access privileges for all the tables used in the query.

NEW QUESTION # 18
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 future grants on the newly-created schemas.
B. Set up the new schema as a managed-access schema.
C. Assign ownership privilege to DEV_TEAM on the newly-created schema.
D. Assign usage privilege on the virtual warehouse DEV_WH to the role JDOE_PROFILE.

Answer: A

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 # 19
Which type of listing in the Snowflake Marketplace can be added and queried immediately?

A. Standard listing
B. Regional listing
C. Personalized listing
D. Monetized listing

Answer: A

Explanation:
Explanation
According to the Snowflake documentation1, a standard listing is a type of listing that provides free access to the full data product, with no payment required. A standard listing can be added andqueried immediately by the consumer, as long as they accept the terms and conditions of the listing. A monetized listing is a type of listing that charges for access to the data product, using the pricing models offered by Snowflake. A monetized listing requires the consumer to provide payment information and agree to the billing terms before accessing the data product. A regional listing is not a type of listing, but a way to specify the regions where the listing is available. A personalized listing is a type of listing that provides limited trial access to the data product, with unlimited access to the full data product available upon request. A personalized listing requires the consumer to request access from the provider and wait for the provider to grant access before accessing the data product.
Therefore, the only type of listing that can be added and queried immediately is the standard listing.

NEW QUESTION # 20
What is a characteristic of Snowflake's transaction locking and concurrency modeling?

A. Queries executed within a given transaction see that transaction's uncommitted changes.
B. Transaction locking in Snowflake is enforced exclusively at the row and table levels.
C. A deadlock cannot occur in Snowflake, since concurrently executed queries and DML statements do not block one another.
D. If two queries are concurrently executed against the same table, one of the two queries will be blocked until the other query completes.

Answer: C

Explanation:
Explanation
According to the Snowflake documentation1, Snowflake uses a multi-version concurrency control (MVCC) model, which means that each transaction operates on a consistent snapshot of the database at a point in time.
This allows queries and DML statements to run concurrently without blocking each other, as they do not modify the same data. Therefore, a deadlock, which occurs when concurrent transactions are waiting on resources that are locked by each other, cannot happen in Snowflake. Option B is incorrect because queries and DML statements do not block each other in Snowflake, unless they are explicitly started transactions and multiple statements in each transaction2. Option C is incorrect because transaction locking in Snowflake is enforced at the partition level, not the row or table level3. Option D is incorrect because queries executed within a given transaction do not see that transaction's uncommitted changes, but only the committed changes that occurred before the transaction started1.

NEW QUESTION # 21
......

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