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| Section | Objectives |
|---|---|
| Topic 1: Data Pipelines and Data Engineering | - Data orchestration
|
| Topic 2: Performance and Optimization | - Query performance tuning
|
| Topic 3: Data Architecture and Design | - Design scalable Snowflake architectures
|
| Topic 4: Security, Governance, and Compliance | - Data governance
|
| Topic 5: Data Sharing and Collaboration | - Secure data sharing
|
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NEW QUESTION # 177
How do Snowflake databases that are created from shares differ from standard databases that are not created from shares? (Choose three.)
Answer: C,E,F
Explanation:
Explanation
According to the SnowPro Advanced: Architect documents and learning resources, the ways that Snowflake databases that are created from shares differ from standard databases that are not created from shares are:
* Shared databases are read-only. This means that the data consumers who access the shared databases cannot modify or delete the data or the objects in the databases. The data providers who share the databases have full control over the data and the objects, and can grant or revoke privileges on them1.
* Shared databases cannot be cloned. This means that the data consumers who access the shared databases cannot create a copy of the databases or the objects in the databases. The data providers who share the databases can clone the databases or the objects, but the clones are not automatically shared2.
* Shared databases are not supported by Time Travel. This means that the data consumers who access the shared databases cannot use the AS OF clause to query historical data or restore deleted data. The data providers who share the databases can use Time Travel on the databases or the objects, but the historical data is not visible to the data consumers3.
The other options are incorrect because they are not ways that Snowflake databases that are created from shares differ from standard databases that are not created from shares. Option B is incorrectbecause shared databases do not need to be refreshed in order for new data to be visible. The data consumers who access the shared databases can see the latest data as soon as the data providers update the data1. Option E is incorrect because shared databases will not have the PUBLIC or INFORMATION_SCHEMA schemas without explicitly granting these schemas to the share. The data consumers who access the shared databases can only see the objects that the data providers grant to the share, and the PUBLIC and INFORMATION_SCHEMA schemas are not granted by default4. Option F is incorrect because shared databases cannot be created as transient databases. Transient databases are databases that do not support Time Travel or Fail-safe, and can be dropped without affecting the retention period of the data. Shared databases are always created as permanent databases, regardless of the type of the source database5. References: Introduction to Secure Data Sharing | Snowflake Documentation, Cloning Objects | Snowflake Documentation, Time Travel | Snowflake Documentation, Working with Shares | Snowflake Documentation, CREATE DATABASE | Snowflake Documentation
NEW QUESTION # 178
A company needs an efficient way to get data from an external partner who is also a Snowflake user. The current process involves daily JSON files uploaded manually and frequently changed.
What is the MOST efficient solution?
Answer: C
Explanation:
When both parties are Snowflake users, Secure Data Sharing is the most efficient and reliable way to exchange data (Answer A). By creating a share, the data provider can grant real-time access to selected objects without copying or moving data. This eliminates ingestion pipelines, reduces latency, and automatically adapts to schema changes.
Exporting data to cloud storage introduces unnecessary complexity, delays, and maintenance overhead.
Reader accounts are intended for consumers who are not Snowflake customers, which does not apply in this scenario. Maintaining manual file-based ingestion is inefficient and error-prone, especially when schemas change frequently.
For SnowPro Architect candidates, this question reinforces a core Snowflake principle: whenever possible, use native Secure Data Sharing to simplify architectures, improve freshness, and reduce operational costs.
NEW QUESTION # 179
Running EXPLAIN on a query does not require a running warehouse
Answer: A
NEW QUESTION # 180
A healthcare company is deploying a Snowflake account that may include Personal Health Information (PHI). The company must ensure compliance with all relevant privacy standards.
Which best practice recommendations will meet data protection and compliance requirements? (Choose three.)
Answer: B,E,F
Explanation:
A healthcare company that handles PHI data must ensure compliance with relevant privacy standards, such as HIPAA, HITRUST, and GDPR. Snowflake provides several features and best practices to help customers meet their data protection and compliance requirements1.
One best practice recommendation is to use, at minimum, the Business Critical edition of Snowflake. This edition provides the highest level of data protection and security, including end-to-end encryption with customer-managed keys, enhanced object-level security, and HIPAA and HITRUST compliance2. Therefore, option A is correct.
Another best practice recommendation is to create Dynamic Data Masking policies and apply them to columns that contain PHI. Dynamic Data Masking is a feature that allows masking or redacting sensitive data based on the current user's role. This way, only authorized users can view the unmasked data, while others will see masked values, such as NULL, asterisks, or random characters3. Therefore, option B is correct.
A third best practice recommendation is to use the External Tokenization feature to obfuscate sensitive data. External Tokenization is a feature that allows replacing sensitive data with tokens that are generated and stored by an external service, such as Protegrity. This way, the original data is never stored or processed by Snowflake, and only authorized users can access the tokenized data through the external service4. Therefore, option D is correct.
Option C is incorrect, because the Internal Tokenization feature is not available in Snowflake. Snowflake does not provide any native tokenization functionality, but only supports integration with external tokenization services4.
Option E is incorrect, because rewriting SQL queries to eliminate projections of PHI data based on current_role() is not a best practice. This approach is error-prone, inefficient, and hard to maintain. A better alternative is to use Dynamic Data Masking policies, which can automatically mask data based on the user's role without modifying the queries3.
Option F is incorrect, because avoiding sharing data with partner organizations is not a best practice. Snowflake enables secure and governed data sharing with internal and external consumers, such as business units, customers, or partners. Data sharing does not involve copying or moving data, but only granting access privileges to the shared objects. Data sharing can also leverage Dynamic Data Masking and External Tokenization features to protect sensitive data5.
NEW QUESTION # 181
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: B,D
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 # 182
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