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| Section | Objectives |
|---|---|
| Data Sharing and Collaboration | - Secure data sharing
|
| Data Pipelines and Data Engineering | - Data ingestion and transformation
|
| Security, Governance, and Compliance | - Data governance
|
| Data Architecture and Design | - Data modeling and schema design
|
| Performance and Optimization | - Cost optimization
|
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NEW QUESTION # 112
Which Snowflake objects can be used in a data share? (Select TWO).
Answer: A,B
NEW QUESTION # 113
A table contains five columns and it has millions of records. The cardinality distribution of the columns is shown below:
Column C4 and C5 are mostly used by SELECT queries in the GROUP BY and ORDER BY clauses.
Whereas columns C1, C2 and C3 are heavily used in filter and join conditions of SELECT queries.
The Architect must design a clustering key for this table to improve the query performance.
Based on Snowflake recommendations, how should the clustering key columns be ordered while defining the multi-column clustering key?
Answer: D
Explanation:
According to the Snowflake documentation, the following are some considerations for choosing clustering for a table1:
* Clustering is optimal when either:
* You require the fastest possible response times, regardless of cost.
* Your improved query performance offsets the credits required to cluster and maintain the table.
* Clustering is most effective when the clustering key is used in the following types of query predicates:
* Filter predicates (e.g. WHERE clauses)
* Join predicates (e.g. ON clauses)
* Grouping predicates (e.g. GROUP BY clauses)
* Sorting predicates (e.g. ORDER BY clauses)
* Clustering is less effective when the clustering key is not used in any of the above query predicates, or when the clustering key is used in a predicate that requires a function or expression to be applied to the key (e.g. DATE_TRUNC, TO_CHAR, etc.).
* For most tables, Snowflake recommends a maximum of 3 or 4 columns (or expressions) per key.
Adding more than 3-4 columns tends to increase costs more than benefits.
Based on these considerations, the best option for the clustering key columns is C. C1, C3, C2, because:
* These columns are heavily used in filter and join conditions of SELECT queries, which are the most effective types of predicates for clustering.
* These columns have high cardinality, which means they have many distinct values and can help reduce the clustering skew and improve the compression ratio.
* These columns are likely to be correlated with each other, which means they can help co-locate similar rows in the same micro-partitions and improve the scan efficiency.
* These columns do not require any functions or expressions to be applied to them, which means they can be directly used in the predicates without affecting the clustering.
References: 1: Considerations for Choosing Clustering for a Table | Snowflake Documentation
NEW QUESTION # 114
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: D
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 # 115
An Architect needs to grant a group of ORDER_ADMIN users the ability to clean old data in an ORDERS table (deleting all records older than 5 years), without granting any privileges on the table. The group's manager (ORDER_MANAGER) has full DELETE privileges on the table.
How can the ORDER_ADMIN role be enabled to perform this data cleanup, without needing the DELETE privilege held by the ORDER_MANAGER role?
Answer: A
Explanation:
This is the correct answer because it allows the ORDER_ADMIN role to perform the data cleanup without needing the DELETE privilege on the ORDERS table. A stored procedure is a feature that allows scheduling and executing SQL statements or stored procedures in Snowflake. A stored procedure can run with either the caller's rights or the owner's rights. A caller's rights stored procedure runs with the privileges of the role that called the stored procedure, while an owner's rights stored procedure runs with the privileges of the role that created the stored procedure. By creating a stored procedure that runs with owner's rights, the ORDER_MANAGER role can delegate the specific task of deleting old data to the ORDER_ADMIN role, without granting the ORDER_ADMIN role more general privileges on the ORDERS table. The stored procedure must include the appropriate business logic to delete only the records older than 5 years, and the ORDER_MANAGER role must grant the USAGE privilege on the stored procedure to the ORDER_ADMIN role. The ORDER_ADMIN role can then execute the stored procedure to perform the data cleanup12.
References:
* Snowflake Documentation: Stored Procedures
* Snowflake Documentation: Understanding Caller's Rights and Owner's Rights Stored Procedures
NEW QUESTION # 116
A user is executing the following command sequentially within a timeframe of 10 minutes from start to finish:
What would be the output of this query?
Answer: A
Explanation:
The query is executing a clone operation on an existing table t_sales with an offset to account for the retention time. The syntax used is correct for cloning a table in Snowflake, and the use of the at(offset => -60*30) clause is valid. This specifies that the clone should be based on the state of the table 30 minutes prior (60 seconds * 30). Assuming the table t_sales exists and has been modified within the last 30 minutes, and considering the data_retention_time_in_days is set to 1 day (which enables time travel queries for the past
24 hours), the table t_sales_clone would be successfully created based on the state of t_sales 30 minutes before the clone command was issued.
NEW QUESTION # 117
......
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