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Snowflake SnowPro Advanced Architect Certification Sample Questions (Q21-Q26):

NEW QUESTION # 21
The following chart represents the performance of a virtual warehouse over time:

A Data Engineer notices that the warehouse is queueing queries. The warehouse is sizeX-Small, theminimum and maximum cluster counts are set to 1, thescaling policy is set to standard, andauto-suspend is set to 10 minutes.
How can the performance be improved?

A. Change auto-suspend to a longer time frame.
B. Increase the size of the warehouse.
C. Change the cluster settings.
D. Change the scaling policy to economy.

Answer: C

NEW QUESTION # 22
Is it possible for a data provider account with a Snowflake Business Critical edition to share data with an Enterprise edition data consumer account?

A. If a user in the provider account with a share owning role sets share_restrictions to False when adding an Enterprise consumer account, it can import the share.
B. A Business Critical account cannot be a data sharing provider to an Enterprise consumer. Any consumer accounts must also be Business Critical.
C. If a user in the provider account with role authority to create or alter share adds an Enterprise account as a consumer, it can import the share.
D. If a user in the provider account with a share owning role which also has override share restrictions privilege share_restrictions set to False when adding an Enterprise consumer account, it can import the share.

Answer: C

Explanation:
In Snowflake, data sharing capabilities allow a Business Critical edition account to share data with an Enterprise edition consumer account. The ability to share data is contingent upon the role permissions within the provider account. If a user has the necessary role authority (like ACCOUNTADMIN or a role with similar privileges to create or manage shares), they can add an Enterprise edition account as a consumer. This feature enables flexibility in data sharing across different Snowflake account editions, facilitating broader data collaboration and accessibility.
Reference: Snowflake's data sharing documentation and the specifics of edition-based capabilities discussed in SnowPro Advanced: Architect certification materials.

NEW QUESTION # 23
At which object type level can the APPLY MASKING POLICY, APPLY ROW ACCESS POLICY and APPLY SESSION POLICY privileges be granted?

A. Table
B. Database
C. Schema
D. Global

Answer: D

Explanation:
The object type level at which the APPLY MASKING POLICY, APPLY ROW ACCESS POLICY and APPLY SESSION POLICY privileges can be granted is global. These are account-level privileges that control who can apply or unset these policies on objects such as columns, tables, views, accounts, or users.
These privileges are granted to the ACCOUNTADMIN role by default, and can be granted to other roles as needed. The other options are incorrect because they are not the object type level at which these privileges can be granted. Database, schema, and table are lower-level object types that do not support these privileges. References: Access Control Privileges | Snowflake Documentation, Using Dynamic Data Masking
| Snowflake Documentation, Using Row Access Policies | Snowflake Documentation, Using Session Policies | Snowflake Documentation

NEW QUESTION # 24
A company's daily Snowflake workload consists of a huge number of concurrent queries triggered between
9pm and 11pm. At the individual level, these queries are smaller statements that get completed within a short time period.
What configuration can the company's Architect implement to enhance the performance of this workload?
(Choose two.)

A. Set the connection timeout to a higher value than its default.
B. Increase the size of the virtual warehouse to size X-Large.
C. Reduce the amount of data that is being processed through this workload.
D. Set the MAX_CONCURRENCY_LEVEL to a higher value than its default value of 8 at the virtual warehouse level.
E. Enable a multi-clustered virtual warehouse in maximized mode during the workload duration.

Answer: D,E

Explanation:
Explanation
These two configuration options can enhance the performance of the workload that consists of a huge number of concurrent queries that are smaller and faster.
* Enabling a multi-clustered virtual warehouse in maximized mode allows the warehouse to scale out automatically by adding more clusters as soon as the current cluster is fully loaded, regardless of the number of queries in the queue. This can improve the concurrency and throughput of the workload by minimizing or preventing queuing. The maximized mode is suitable for workloads that require high performance and low latency, and are less sensitive to credit consumption1.
* Setting the MAX_CONCURRENCY_LEVEL to a higher value than its default value of 8 at the virtual warehouse level allows the warehouse to run more queries concurrently on each cluster. This can improve the utilization and efficiency of the warehouse resources, especially for smaller and faster queries that do not require a lot of processing power. The MAX_CONCURRENCY_LEVEL parameter can be set when creating or modifying a warehouse, and it can be changed at any time2.
References:
* Snowflake Documentation: Scaling Policy for Multi-cluster Warehouses
* Snowflake Documentation: MAX_CONCURRENCY_LEVEL

NEW QUESTION # 25
Why might a Snowflake Architect use a star schema model rather than a 3NF model when designing a data architecture to run in Snowflake? (Select TWO).

A. The Architect wants to remove data duplication from the data stored in Snowflake.
B. The Architect is designing a landing zone to receive raw data into Snowflake.
C. The Architect wants to present a simple flattened single view of the data to a particular group of end users.
D. Snowflake cannot handle the joins implied in a 3NF data model.
E. The Bl tool needs a data model that allows users to summarize facts across different dimensions, or to drill down from the summaries.

Answer: C,E

Explanation:
A star schema model is a type of dimensional data model that consists of a single fact table and multiple dimension tables. A 3NF model is a type of relational data model that follows the third normal form, which eliminates data redundancy and ensures referential integrity. A Snowflake Architect might use a star schema model rather than a 3NF model when designing a data architecture to run in Snowflake for the following reasons:
* A star schema model is more suitable for analytical queries that require aggregating and slicing data across different dimensions, such as those performed by a BI tool. A 3NF model is more suitable for transactional queries that require inserting, updating, and deleting individual records.
* A star schema model is simpler and faster to query than a 3NF model, as it involves fewer joins and less complex SQL statements. A 3NF model is more complex and slower to query, as it involves more joins and more complex SQL statements.
* A star schema model can provide a simple flattened single view of the data to a particular group of end
* users, such as business analysts or data scientists, who need to explore and visualize the data. A 3NF model can provide a more detailed and normalized view of the data to a different group of end users, such as application developers or data engineers, who need to maintain and update the data.
The other options are not valid reasons for choosing a star schema model over a 3NF model in Snowflake:
* Snowflake can handle the joins implied in a 3NF data model, as it supports ANSI SQL and has a powerful query engine that can optimize and execute complex queries efficiently.
* The Architect can use both star schema and 3NF models to remove data duplication from the data stored in Snowflake, as both models can enforce data integrity and avoid data anomalies. However, the trade-off is that a star schema model may have more data redundancy than a 3NF model, as it denormalizes the data for faster query performance, while a 3NF model may have less data redundancy than a star schema model, as it normalizes the data for easier data maintenance.
* The Architect can use both star schema and 3NF models to design a landing zone to receive raw data into Snowflake, as both models can accommodate different types of data sources and formats. However, the choice of the model may depend on the purpose and scope of the landing zone, such as whether it is a temporary or permanent storage, whether it is a staging area or a data lake, and whether it is a single source or a multi-source integration.
References:
* Snowflake Architect Training
* Data Modeling: Understanding the Star and Snowflake Schemas
* Data Vault vs Star Schema vs Third Normal Form: Which Data Model to Use?
* Star Schema vs Snowflake Schema: 5 Key Differences
* Dimensional Data Modeling - Snowflake schema
* Star schema vs Snowflake Schema

NEW QUESTION # 26
......

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