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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Engineering | 25% | - Transformation and processing frameworks - Data pipeline and integration design - Data loading and unloading solutions |
| Topic 2: Snowflake Architecture | 30% | - Data modeling and use cases - Object hierarchy and architectural impact - Data sharing architecture design - Data protection and recovery strategies - Development lifecycle and workload support |
| Topic 3: Accounts and Security | 25% | - Account and database strategy design - Security, compliance and governance architecture - Security principles and implementation scenarios |
| Topic 4: Performance Optimization | 20% | - Virtual warehouse configuration and sizing - Query performance tuning - Cost optimization and resource management - Clustering and partitioning strategies |
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NEW QUESTION # 17
Which SQL alter command will MAXIMIZE memory and compute resources for a Snowpark stored procedure when executed on the snowpark_opt_wh warehouse?
Answer: C
Explanation:
To maximize memory and compute resources for a Snowpark stored procedure, you need to set the MAX_CONCURRENCY_LEVEL parameter for the warehouse that executes the stored procedure. This parameter determines the maximum number of concurrent queries that can run on a single warehouse. By setting it to 16, you ensure that the warehouse can use all the available CPU cores and memory on a single node, which is the optimal configuration for Snowpark-optimized warehouses. This will improve the performance and efficiency of the stored procedure, as it will not have to share resources with other queries or nodes. The other options are incorrect because they either do not change the MAX_CONCURRENCY_LEVEL parameter, or they set it to a lower value than 16, which will reduce the memory and compute resources for the stored procedure. Reference:
[Snowpark-optimized Warehouses] 1
[Training Machine Learning Models with Snowpark Python] 2
[Snowflake Shorts: Snowpark Optimized Warehouses] 3
NEW QUESTION # 18
Is it possible for a data provider account with a Snowflake Business Critical edition to share data with an Enterprise edition data consumer account?
Answer: B
Explanation:
When a SnowflakeBusiness Critical (BC)edition account shares data, it must followdata sharing restrictionsdesigned to maintain thehigher level of compliance and securityguaranteed by BC.
Bydefault, BC accounts canonly share data with other BC (or higher)accounts, to maintain consistent security and compliance (e.g., HIPAA, HITRUST, FedRAMP).
However,an exceptioncan be madeif a user with the proper privilegeexplicitly disables the restriction.
Key Concept: share_restrictions
* Snowflake enforcesdata sharing restrictionsby default forBC accounts.
* Arole with the OVERRIDE SHARE RESTRICTIONS global privilegecan bypass this by setting theshare_restrictions = FALSEwhen adding the target account.
Correct Option: D
This is correct because:
* The usermust have a role with the OVERRIDE SHARE RESTRICTIONS privilege.
* That user can thenset share_restrictions = FALSEwhen adding the Enterprise edition consumer account.
Official Documentation Extract:
"If the data provider is a Business Critical (or higher) account, Snowflake enforces a restriction by default that only allows sharing data with other Business Critical (or higher) accounts. A user in the provider account with a role that has the global privilege OVERRIDE SHARE RESTRICTIONS can override this restriction by explicitly setting SHARE_RESTRICTIONS = FALSE when adding the consumer account." Source:Snowflake Docs - CREATE SHARE Why Other Options Are Incorrect:
A:Incorrect - This is not absolute. Business Critical accountscanshare data with Enterprise accounts,if the restriction is explicitly overridden.
B:Incorrect - Simply having authority to create or alter a share isnot enough. You must have the OVERRIDE SHARE RESTRICTIONS privilege and set the restriction explicitly.
C:Incorrect - Setting share_restrictions = FALSE is required, but theprivilege to overridemust also be held by the role. Without the privilege, the action will fail.
References:
CREATE SHARE - SHARE_RESTRICTIONS Parameter
Snowflake Editions Comparison
Managing Shares and Data Sharing Restrictions
NEW QUESTION # 19
An Architect for a multi-national transportation company has a system that is used to check the weather conditions along vehicle routes. The data is provided to drivers.
The weather information is delivered regularly by a third-party company and this information is generated as JSON structure. Then the data is loaded into Snowflake in a column with a VARIANT data type. This table is directly queried to deliver the statistics to the drivers with minimum time lapse.
A single entry includes (but is not limited to):
- Weather condition; cloudy, sunny, rainy, etc.
- Degree
- Longitude and latitude
- Timeframe
- Location address
- Wind
The table holds more than 10 years' worth of data in order to deliver the statistics from different years and locations. The amount of data on the table increases every day.
The drivers report that they are not receiving the weather statistics for their locations in time.
What can the Architect do to deliver the statistics to the drivers faster?
Answer: B
Explanation:
To improve the performance of queries on semi-structured data, such as JSON stored in a VARIANT column, Snowflake's search optimization service can be utilized. By adding search optimization specifically for the longitude and latitude fields within the VARIANT column, the system can perform point lookups and substring queries more efficiently. This will allow for faster retrieval of weather statistics, which is critical for the drivers to receive timely updates.
NEW QUESTION # 20
Files arrive in an external stage every 10 seconds from a proprietary system. The files range in size from 500 K to 3 MB. The data must be accessible by dashboards as soon as it arrives.
How can a Snowflake Architect meet this requirement with the LEAST amount of coding? (Choose two.)
Answer: D,E
Explanation:
Explanation
These two options are the best ways to meet the requirement of loading data from an external stage and making it accessible by dashboards with the least amount of coding.
* Snowpipe with auto-ingest is a feature that enables continuous and automated data loading from an external stage into a Snowflake table. Snowpipe uses event notifications from the cloud storage service to detect new or modified files in the stage and triggers a COPY INTO command to load the data into the table. Snowpipe is efficient, scalable, and serverless, meaning it does not require any infrastructure or maintenance from the user. Snowpipe also supports loading data from files of any size, as long as they are in a supported format1.
* A materialized view on an external table is a feature that enables creating a pre-computed result set from an external table and storing it in Snowflake. A materialized view can improve the performance and efficiency of querying data from an external table, especially for complex queries or dashboards. A materialized view can also support aggregations, joins, and filters on the external table data. A materialized view on an external table is automatically refreshed when the underlying data in the external stage changes, as long as the AUTO_REFRESH parameter is set to true2.
References:
* Snowpipe Overview | Snowflake Documentation
* Materialized Views on External Tables | Snowflake Documentation
NEW QUESTION # 21
A group of Data Analysts have been granted the role analyst role. They need a Snowflake database where they can create and modify tables, views, and other objects to load with their own dat a. The Analysts should not have the ability to give other Snowflake users outside of their role access to this data.
How should these requirements be met?
Answer: D
NEW QUESTION # 22
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