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| Section | Objectives |
|---|---|
| Data Architecture and Design | - Data modeling and schema design
|
| Security, Governance, and Compliance | - Access control and security model
|
| Data Sharing and Collaboration | - Secure data sharing
|
| Data Pipelines and Data Engineering | - Data ingestion and transformation
|
| Performance and Optimization | - Query performance tuning
|
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NEW QUESTION # 170
Which Snowflake objects can be used in a data share? (Select TWO).
Answer: A,B
Explanation:
Data sharing is a feature that allows you to share selected objects in a database in your account with other Snowflake accounts. You can share the following Snowflake database objects: external tables, dynamic tables, secure views, secure materialized views, secure UDFs, and tables. However, not all of these objects can be used in a data share. A data share is a named object that encapsulates the information required to share a database. You can grant privileges on objects to a share either via a database role or directly to a share. The objects that can be granted privileges directly to a share are: standard views, secure views, secure UDFs, and tables.
Therefore, the correct answer is A and B. The other options are incorrect because they cannot be granted privileges directly to a share. External tables, dynamic tables, and streams can only be shared via a database role. Stored procedures cannot be shared at all. Reference:
[Introduction to Secure Data Sharing] 1
[Working with Shares] 2
[Choosing How to Share Database Objects] 3
NEW QUESTION # 171
What actions are permitted when using the Snowflake SQL REST API? (Select TWO).
Answer: A,D
Explanation:
A: The Snowflake SQL REST API does support the use of a GET command, which can be used to retrieve the status of a previously submitted query or to fetch the results of a query once it has been executed.D. The use of a CALL command to a stored procedure is supported, which can return a result set, including a table. This allows the invocation of stored procedures within Snowflake through the SQL REST API.
NEW QUESTION # 172
A retail company has over 3000 stores all using the same Point of Sale (POS) system. The company wants to deliver near real-time sales results to category managers. The stores operate in a variety of time zones and exhibit a dynamic range of transactions each minute, with some stores having higher sales volumes than others.
Sales results are provided in a uniform fashion using data engineered fields that will be calculated in a complex data pipeline. Calculations include exceptions, aggregations, and scoring using external functions interfaced to scoring algorithms. The source data for aggregations has over 100M rows.
Every minute, the POS sends all sales transactions files to a cloud storage location with a naming convention that includes store numbers and timestamps to identify the set of transactions contained in the files. The files are typically less than 10MB in size.
How can the near real-time results be provided to the category managers? (Select TWO).
Answer: A,E
Explanation:
To provide near real-time sales results to category managers, the Architect can use the following steps:
Create an external stage that references the cloud storage location where the POS sends the sales transactions files. The external stage should use the file format and encryption settings that match the source files2 Create a Snowpipe that loads the files from the external stage into a target table in Snowflake. The Snowpipe should be configured with AUTO_INGEST = true, which means that it will automatically detect and ingest new files as they arrive in the external stage. The Snowpipe should also use a copy option to purge the files from the external stage after loading, to avoid duplicate ingestion3 Create a stream on the target table that captures the INSERTS made by the Snowpipe. The stream should include the metadata columns that provide information about the file name, path, size, and last modified time. The stream should also have a retention period that matches the real-time analytics needs4 Create a task that runs a query on the stream to process the near real-time data. The query should use the stream metadata to extract the store number and timestamps from the file name and path, and perform the calculations for exceptions, aggregations, and scoring using external functions. The query should also output the results to another table or view that can be accessed by the category managers. The task should be scheduled to run at a frequency that matches the real-time analytics needs, such as every minute or every 5 minutes.
The other options are not optimal or feasible for providing near real-time results:
All files should be concatenated before ingestion into Snowflake to avoid micro-ingestion. This option is not recommended because it would introduce additional latency and complexity in the data pipeline. Concatenating files would require an external process or service that monitors the cloud storage location and performs the file merging operation. This would delay the ingestion of new files into Snowflake and increase the risk of data loss or corruption. Moreover, concatenating files would not avoid micro-ingestion, as Snowpipe would still ingest each concatenated file as a separate load.
An external scheduler should examine the contents of the cloud storage location and issue SnowSQL commands to process the data at a frequency that matches the real-time analytics needs. This option is not necessary because Snowpipe can automatically ingest new files from the external stage without requiring an external trigger or scheduler. Using an external scheduler would add more overhead and dependency to the data pipeline, and it would not guarantee near real-time ingestion, as it would depend on the polling interval and the availability of the external scheduler.
The copy into command with a task scheduled to run every second should be used to achieve the near-real time requirement. This option is not feasible because tasks cannot be scheduled to run every second in Snowflake. The minimum interval for tasks is one minute, and even that is not guaranteed, as tasks are subject to scheduling delays and concurrency limits. Moreover, using the copy into command with a task would not leverage the benefits of Snowpipe, such as automatic file detection, load balancing, and micro-partition optimization. Reference:
1: SnowPro Advanced: Architect | Study Guide
2: Snowflake Documentation | Creating Stages
3: Snowflake Documentation | Loading Data Using Snowpipe
4: Snowflake Documentation | Using Streams and Tasks for ELT
5: Snowflake Documentation | Creating Tasks
6: Snowflake Documentation | Best Practices for Loading Data
7: Snowflake Documentation | Using the Snowpipe REST API
8: Snowflake Documentation | Scheduling Tasks
9: SnowPro Advanced: Architect | Study Guide
10: Creating Stages
11: Loading Data Using Snowpipe
12: Using Streams and Tasks for ELT
13: [Creating Tasks]
14: [Best Practices for Loading Data]
15: [Using the Snowpipe REST API]
16: [Scheduling Tasks]
NEW QUESTION # 173
An Architect would like to save quarter-end financial results for the previous six years.
Which Snowflake feature can the Architect use to accomplish this?
Answer: C
Explanation:
Explanation
Zero-copy cloning is a Snowflake feature that can be used to save quarter-end financial results for the previous six years. Zero-copy cloning allows creating a copy of a database, schema, table, or view without duplicating the data or metadata. The clone shares the same data files as the original object, but tracks any changes made to the clone or the original separately. Zero-copy cloning can be usedto create snapshots of data at different points in time, such as quarter-end financial results, and preserve them for future analysis or comparison. Zero-copy cloning is fast, efficient, and does not consume any additional storage space unless the data is modified1.
References:
* Zero-Copy Cloning | Snowflake Documentation
NEW QUESTION # 174
What integration object should be used to place restrictions on where data may be exported?
Answer: D
Explanation:
In Snowflake, a storage integration is used to define and configure external cloud storage that Snowflake will interact with. This includes specifying security policies for access control. One of the main features of storage integrations is the ability to set restrictions on where data may be exported. This is done by binding the storage integration to specific cloud storage locations, thereby ensuring that Snowflake can only access those locations. It helps to maintain control over the data and complies with data governance and security policies by preventing unauthorized data exports to unspecified locations.
NEW QUESTION # 175
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