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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Snowflake Architecture | 30% | - Object hierarchy and architectural impact - Data modeling and use cases - Data protection and recovery strategies - Data sharing architecture design - Development lifecycle and workload support |
| Topic 2: Data Engineering | 25% | - Transformation and processing frameworks - Data loading and unloading solutions - Data pipeline and integration design |
| Topic 3: Performance Optimization | 20% | - Query performance tuning - Cost optimization and resource management - Virtual warehouse configuration and sizing - Clustering and partitioning strategies |
| Topic 4: Accounts and Security | 25% | - Account and database strategy design - Security, compliance and governance architecture - Security principles and implementation scenarios |
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NEW QUESTION # 88
The following table exists in the production database:
A regulatory requirement states that the company must mask the username for events that are older than six months based on the current date when the data is queried.
How can the requirement be met without duplicating the event data and making sure it is applied when creating views using the table or cloning the table?
Answer: D
Explanation:
A masking policy is a feature of Snowflake that allows masking sensitive data in query results based on the role of the user and the condition of the data. A masking policy can be applied to a column in a table or a view, and it can use another column in the same table or view as a conditional column. A conditional column is a column that determines whether the masking policy is applied or not based on its value1.
In this case, the requirement can be met by using a masking policy on the username column with event_timestamp as a conditional column. The masking policy can use a function that masks the username if the event_timestamp is older than six months based on the current date, and returns the original username otherwise. The masking policy can be applied to the user_events table, and it will also be applied when creating views using the table or cloning the table2.
The other options are not correct because:
* A. Using a masking policy on the username column using an entitlement table with valid dates would require creating another table that stores the valid dates for each username, and joining it with the user_events table in the masking policy function. This would add complexity and overhead to the masking policy, and it would not use the event_timestamp column as the condition for masking.
* B. Using a row level policy on the user_events table using an entitlement table with valid dates would require creating another table that stores the valid dates for each username, and joining it with the user_events table in the row access policy function. This would filter out the rows that have event_timestamp older than six months based on the valid dates, instead of masking the username column. This would not meet the requirement of masking the username, and it would also reduce the visibility of the event data.
* D. Using a secure view on the user_events table using a case statement on the username column would require creating a view that uses a case expression to mask the username column based on the event_timestamp column. This would meet the requirement of masking the username, but it would not be applied when cloning the table. A secure view is a view that prevents the underlying data from being exposed by queries on the view. However, a secure view does not prevent the underlying data from being exposed by cloning the table3.
References:
* 1: Masking Policies | Snowflake Documentation
* 2: Using Conditional Columns in Masking Policies | Snowflake Documentation
* 3: Secure Views | Snowflake Documentation
NEW QUESTION # 89
Based on the Snowflake object hierarchy, what securable objects belong directly to a Snowflake account?
(Select THREE).
Answer: A,B,F
Explanation:
* A securable object is an entity to which access can be granted in Snowflake. Securable objects include databases, schemas, tables, views, stages, pipes, functions, procedures, sequences, tasks, streams, roles, warehouses, and shares1.
* The Snowflake object hierarchy is a logical structure that organizes the securable objects in a nested manner. The top-most container is the account, which contains all the databases, roles, and warehouses for the customer organization. Each database contains schemas, which in turn contain tables, views, stages, pipes, functions, procedures, sequences, tasks, and streams. Each role can be granted privileges on other roles or securable objects. Each warehouse can be used to execute queries on securable objects2.
* Based on the Snowflake object hierarchy, the securable objects that belong directly to a Snowflake account are databases, roles, and warehouses. These objects are created and managed at the account level, and do not depend on any other securable object. The other options are not correct because:
* Schemas belong to databases, not to accounts. A schema must be created within an existing database3.
* Tables belong to schemas, not to accounts. A table must be created within an existing schema4.
* Stages belong to schemas or tables, not to accounts. A stage must be created within an existing schema or table.
1: Overview of Access Control | Snowflake Documentation
2: Securable Objects | Snowflake Documentation
3: CREATE SCHEMA | Snowflake Documentation
4: CREATE TABLE | Snowflake Documentation
[5]: CREATE STAGE | Snowflake Documentation
NEW QUESTION # 90
A DevOps team has a requirement for recovery of staging tables used in a complex set of data pipelines. The staging tables are all located in the same staging schema. One of the requirements is to have online recovery of data on a rolling 7-day basis.
After setting up the DATA_RETENTION_TIME_IN_DAYS at the database level, certain tables remain unrecoverable past 1 day.
What would cause this to occur? (Choose two.)
Answer: A,C
Explanation:
* The DATA_RETENTION_TIME_IN_DAYS parameter controls the Time Travel retention period for an object (database, schema, or table) in Snowflake. This parameter specifies the number of days for which historical data is preserved and can be accessed using Time Travel operations (SELECT, CREATE ... CLONE, UNDROP)1.
* The requirement for recovery of staging tables on a rolling 7-day basis means that the DATA_RETENTION_TIME_IN_DAYS parameter should be set to 7 at the database level. However, this parameter can be overridden at the lower levels (schema or table) if they have a different value1.
* Therefore, one possible cause for certain tables to remain unrecoverable past 1 day is that the DATA_RETENTION_TIME_IN_DAYS for the staging schema has been set to 1 day. This would override the database level setting and limit the Time Travel retention period for all the tables in the schema to 1 day. To fix this, the parameter should be unset or set to 7 at the schema level1. Therefore, option B is correct.
* Another possible cause for certain tables to remain unrecoverable past 1 day is that the staging tables are of the TRANSIENT type. Transient tables are tables that do not have a Fail-safe period and can have a Time Travel retention period of either 0 or 1 day. Transient tables are suitable for temporary or intermediate data that can be easily reproduced or replicated2. Tofix this, the tables should be created as permanent tables, which can have a Time Travel retention period of up to 90 days1. Therefore, option D is correct.
* Option A is incorrect because the MANAGED ACCESS feature is not related to the data recovery requirement. MANAGED ACCESS is a feature that allows granting access privileges to objects without explicitly granting the privileges to roles. It does not affect the Time Travel retention period or the data availability3.
* Option C is incorrect because there is no 1 TB limit for data recovery in Snowflake. The data storage size does not affect the Time Travel retention period or the data availability4.
* Option E is incorrect because there is no ALLOW_RECOVERY privilege in Snowflake. The privilege required to perform Time Travel operations is SELECT, which allows querying historical data in tables5.
Understanding & Using Time Travel : Transient Tables : Managed Access : Understanding Storage Cost : Table Privileges
NEW QUESTION # 91
A global retail company must ensure comprehensive data governance, security, and compliance with various international regulations while using Snowflake for data warehousing and analytics.
What should an Architect do to meet these requirements? (Select TWO).
Answer: A,D
Explanation:
Snowflake provides built-in governance and security mechanisms that align with global regulatory requirements. Column-level security-implemented through features such as dynamic data masking and row access policies-allows architects to restrict access to sensitive data at a granular level based on roles or conditions (Answer B). This is essential for compliance with regulations such as GDPR, HIPAA, and similar frameworks that require limiting access to personally identifiable or sensitive data.
Role-Based Access Control (RBAC) is the foundation of Snowflake's security model and is critical for governing who can access which data and perform which actions (Answer D). By assigning privileges to roles instead of users, organizations can centrally manage permissions, enforce separation of duties, and audit access more effectively.
Snowflake does not support column-level network policies, and encryption keys are managed by Snowflake (or via Tri-Secret Secure), not manually by customers. Secure Data Sharing is useful for collaboration but is not a core requirement for governance and compliance in this scenario. For the SnowPro Architect exam, mastering RBAC and column-level security is essential for designing compliant and secure Snowflake architectures.
NEW QUESTION # 92
If a multi-cluster warehouse is resized, the new size applies to
Answer: B
NEW QUESTION # 93
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