Snowflake ARA-C01 Exam With Confidence Using Practice Dumps

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. References:

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

: Snowflake Documentation | Creating Tasks

: Snowflake Documentation | Best Practices for Loading Data

: Snowflake Documentation | Using the Snowpipe REST API

: Snowflake Documentation | Scheduling Tasks

: SnowPro Advanced: Architect | Study Guide

: Creating Stages

: Loading Data Using Snowpipe

: Using Streams and Tasks for ELT

: [Creating Tasks]

: [Best Practices for Loading Data]

: [Using the Snowpipe REST API]

: [Scheduling Tasks]

An external table schema defines the columns and data types of the data stored in an external stage. All external tables include the following columns by default:

VALUE: A VARIANT type column that represents a single row in the external file.

METADATA$FILENAME: A pseudocolumn that identifies the name of each staged data file included in the external table, including its path in the stage.

METADATA$FILE_ROW_NUMBER: A pseudocolumn that shows the row number for each record in a staged data file.

You can also create additional virtual columns as expressions using the VALUE column and/or the pseudocolumns. However, the following columns are not valid for external tables and cannot be included in the schema:

METADATASROW_ID: This column is only available for internal tables and shows the unique identifier for each row in the table.

METADATASISUPDATE: This column is only available for internal tables and shows whether the row was inserted or updated by a merge operation.

METADATASEXTERNAL TABLE PARTITION: This column is not a valid column name and does not exist in Snowflake.

 Introduction to External Tables, CREATE EXTERNAL TABLE

According to the Snowflake documentation, unstructured data files can be shared by using a secure view and Secure Data Sharing. A secure view allows the result of a query to be accessed like a table, and a secure view is specifically designated for data privacy. A scoped URL is an encoded URL that permits temporary access to a staged file without granting privileges to the stage. The URL expires when the persisted query result period ends, which is currently 24 hours. A scoped URL is recommended for file administrators to give scoped access to data files to specific roles in the same account. Snowflake records information in the query history about who uses a scoped URL to access a file, and when. Therefore, a scoped URL is the best option to share unstructured data within Snowflake, as it provides security, accountability, and control over the data access. References:

Sharing unstructured Data with a secure view

Introduction to Loading Unstructured Data