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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is a cloud-based certification exam that is designed to validate the advanced skills and knowledge of Snowflake architects. SnowPro Advanced Architect Certification certification exam is intended for those professionals who possess a deep understanding of Snowflake data warehouses and their architecture, and who can design and implement complex Snowflake solutions using best practices. The SnowPro Advanced Architect Certification Exam is a vendor-neutral certification, which means that it is not affiliated with any particular vendor or technology.
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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Exam is a certification program designed for professionals working with Snowflake, a cloud-based data warehousing and analytics platform. SnowPro Advanced Architect Certification certification is designed for advanced-level architects who have a deep understanding of Snowflake's architecture, best practices, and functionality. ARA-C01 Exam measures the candidate's ability to design and implement complex Snowflake solutions in a variety of scenarios.
NEW QUESTION # 150
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: B,D
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 # 151
An Architect is designing a solution that will be used to process changed records in an orders table.
Newly-inserted orders must be loaded into the f_orders fact table, which will aggregate all the orders by multiple dimensions (time, region, channel, etc.). Existing orders can be updated by the sales department within 30 days after the order creation. In case of an order update, the solution must perform two actions:
1. Update the order in the f_0RDERS fact table.
2. Load the changed order data into the special table ORDER _REPAIRS.
This table is used by the Accounting department once a month. If the order has been changed, the Accounting team needs to know the latest details and perform the necessary actions based on the data in the order_repairs table.
What data processing logic design will be the MOST performant?
Answer: C
Explanation:
The most performant design for processing changed records, considering the need to both update records in the f_orders fact table and load changes into the order_repairs table, is to use one stream and two tasks. The stream will monitor changes in the orders table, capturing both inserts and updates. The first task would apply these changes to the f_orders fact table, ensuring all dimensions are accurately represented. The second task would use the same stream to insert relevant changes into the order_repairs table, which is critical for the Accounting department's monthly review. This method ensures efficient processing by minimizing the overhead of managing multiple streams and synchronizing between them, while also allowing specific tasks to optimize for their target operations.References: Snowflake's documentation on streams and tasks for handling data changes efficiently.
NEW QUESTION # 152
An Architect needs to allow a user to create a database from an inbound share.
To meet this requirement, the user's role must have which privileges? (Choose two.)
Answer: A,C
Explanation:
According to the Snowflake documentation, to create a database from an inbound share, the user's role must have the following privileges:
* The CREATE DATABASE privilege on the current account. This privilege allows the user to create a
* new database in the account1.
* The IMPORT DATABASE privilege on the share. This privilege allows the user to import a database from the share into the account2. The other privileges listed are not relevant for this requirement. The IMPORT SHARE privilege is used to import a share into the account, not a database3. The IMPORT PRIVILEGES privilege is used to import the privileges granted on the shared objects, not the objects themselves2. The CREATE SHARE privilege is used to create a share to provide data to other accounts, not to consume data from other accounts4.
References:
* CREATE DATABASE | Snowflake Documentation
* Importing Data from a Share | Snowflake Documentation
* Importing a Share | Snowflake Documentation
* CREATE SHARE | Snowflake Documentation
NEW QUESTION # 153
A company has an external vendor who puts data into Google Cloud Storage. The company's Snowflake account is set up in Azure.
What would be the MOST efficient way to load data from the vendor into Snowflake?
Answer: C
Explanation:
The most efficient way to load data from the vendor into Snowflake is to create an external stage on Google Cloud Storage and use the external table to load the data into Snowflake (Option B). This way, you can avoid copying or moving the data across different cloud platforms, which can incur additional costs and latency. You can also leverage the external table feature to query the data directly from Google Cloud Storage without loading it into Snowflake tables, which can save storage space and improve performance. Option A is not efficient because it requires the vendor to create a Snowflake account and a data share, which can be complicated and costly. Option C is not efficient because it involves copying the data from Google Cloud Storage to Azure Blob storage using external tools, which can be slow and expensive. Option D is not efficient because it requires creating a Snowflake account in the Google Cloud Platform (GCP), ingesting data into this account, and using data replication to move the data from GCP to Azure, which can be complex and time-consuming. References: The answer can be verified from Snowflake's official documentation on external stages and external tables available on their website. Here are some relevant links:
* Using External Stages | Snowflake Documentation
* Using External Tables | Snowflake Documentation
* Loading Data from a Stage | Snowflake Documentation
NEW QUESTION # 154
A user named USER_01 needs access to create a materialized view on a schema EDW. STG_SCHEMA. How can this access be provided?
Answer: D
Explanation:
The correct answer is A because it grants the specific privilege to create a materialized view on the schema EDW.STG_SCHEMA to the user USER_01 directly.
Option B is incorrect because it grants the privilege to create a materialized view on the entire database EDW, which is too broad and unnecessary. Also, there is a typo in the user name (USERJD1 instead of USER_01).
Option C is incorrect because it grants the privilege to create a materialized view on a different schema (ECW.STG_SCHEKA instead of EDW.STG_SCHEMA). Also, there is no need to create a new role for this purpose.
Option D is incorrect because it grants the privilege to create a materialized view on an invalid object (EDW.STG_SCHEMA is not a valid schema name, it should be EDW.STG_SCHEMA). Also, there is no need to create a new role for this purpose. Reference:
Snowflake Documentation: CREATE MATERIALIZED VIEW
Snowflake Documentation: Working with Materialized Views
[Snowflake Documentation: GRANT Privileges on a Schema]
NEW QUESTION # 155
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