Snowflake ARA-C01 - SnowPro Advanced: Architect Certification Exam

This scenario tests understanding of Snowflake Time Travel and operational efficiency, both key topics in the SnowPro Architect exam. When a table is replaced using CREATE OR REPLACE TABLE, Snowflake treats the original table version as dropped but still recoverable within the Time Travel retention period. The goal is to recover the original data with the least effort and risk.

Option A is the most efficient solution. Using Time Travel with the BEFORE (STATEMENT => …) clause allows the Architect to query the exact state of the table immediately before the replacement occurred. This approach precisely targets the moment before the destructive operation, avoids guessing timestamps, and minimizes operational steps. It is also highly accurate, which is critical in production recovery scenarios.

Option B relies on a timestamp, which introduces risk if the timestamp is incorrect or ambiguous. Option C is invalid because UNDROP TABLE only works when a table has been explicitly dropped, not replaced. Option D introduces unnecessary steps and operational overhead, increasing the risk of mistakes.

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]

The masking policy defined in the image indicates that if a user has the PI_ANALYTICS role, they will be able to see the last 4 characters of the CREDITCARDNO column data in clear text. Otherwise, they will see ‘MASKED’. Since Snowflake system roles have not been granted any additional roles, they won’t have the PI_ANALYTICS role and therefore cannot view the last 4 characters of credit card numbers.

To apply a masking policy on a column in Snowflake, you need to use the ALTER TABLE … ALTER COLUMN command or the ALTER VIEW command and specify the policy name. For example, to apply the creditcardno_mask policy on the CREDITCARDNO column of the CREDITCARDINFO table, you can use the following command:

ALTER TABLE CREDITCARDINFO ALTER COLUMN CREDITCARDNO SET MASKING POLICY creditcardno_mask;

For more information on how to create and use masking policies in Snowflake, you can refer to the following resources:

CREATE MASKING POLICY: This document explains the syntax and usage of the CREATE MASKING POLICY command, which allows you to create a new masking policy or replace an existing one.

Using Dynamic Data Masking: This guide provides instructions on how to configure and use dynamic data masking in Snowflake, which is a feature that allows you to mask sensitive data based on the execution context of the user.

ALTER MASKING POLICY: This document explains the syntax and usage of the ALTER MASKING POLICY command, which allows you to modify the properties of an existing masking policy.

In Snowflake, the characteristics of result set caches include persistence of data results for 24 hours (B), each use of persisted results resets the 24-hour retention period (C), and result set caches are not shared between different warehouses (F). The result set cache is specifically designed to avoid repeated execution of the same query within this timeframe, reducing computational overhead and speeding up query responses. These caches do not contribute to storage costs, and their retention period cannot be extended beyond the default duration nor up to 31 days, as might be misconstrued.