Free Snowflake ARA-R01 Exam Questions

Question 1

What is a characteristic of event notifications in Snowpipe?

A : The load history is stored In the metadata of the target table.

B : Notifications identify the cloud storage event and the actual data in the files.

C : Snowflake can process all older notifications when a paused pipe Is resumed.

D : When a pipe Is paused, event messages received for the pipe enter a limited retention period.

Answer: D

Question 2

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).

A : All files should be concatenated before ingestion into Snowflake to avoid micro-ingestion.

B : A Snowpipe should be created and configured with AUTO_INGEST = true. A stream should be created to process INSERTS into a single target table using the stream metadata to inform the store number and timestamps.

C : A stream should be created to accumulate the near real-time data and a task should be created that runs at a frequency that matches the real-time analytics needs.

D : 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

E : The copy into command with a task scheduled to run every second should be used to achieve the near-real time requirement.

Answer: B,C

Question 3

A Snowflake Architect is designing a multi-tenant application strategy for an organization in the Snowflake Data Cloud and is considering using an Account Per Tenant strategy. Which requirements will be addressed with this approach? (Choose two.)

A : There needs to be fewer objects per tenant.

B : Security and Role-Based Access Control (RBAC) policies must be simple to configure.

C : Compute costs must be optimized.

D : Tenant data shape may be unique per tenant.

E : Storage costs must be optimized.

Answer: D,E

Question 4

A company's Architect needs to find an efficient way to get data from an external partner, who is also a Snowflake user. The current solution is based on daily JSON extracts that are placed on an FTP server and uploaded to Snowflake manually. The files are changed several times each month, and the ingestion process needs to be adapted to accommodate these changes. What would be the MOST efficient solution?

A : Ask the partner to create a share and add the company's account.

B : Ask the partner to use the data lake export feature and place the data into cloud storage where Snowflake can natively ingest it (schema-on-read).

C : Keep the current structure but request that the partner stop changing files, instead only appending new files.

D : Ask the partner to set up a Snowflake reader account and use that account to get the data for ingestion.

Answer: A

Question 5

A company is designing a process for importing a large amount of loT JSON data from cloud storage into Snowflake. New sets of loT data get generated and uploaded approximately every 5 minutes. Once the loT data is in Snowflake, the company needs up-to-date information from an external vendor to join to the data. This data is then presented to users through a dashboard that shows different levels of aggregation. The external vendor is a Snowflake customer. What solution will MINIMIZE complexity and MAXIMIZE performance?

A : 1. Create an external table over the JSON data in cloud storage. 2. Create a task that runs every 5 minutes to run a transformation procedure on new data, based on a saved timestamp. 3. Ask the vendor to expose an API so an external function can be used to generate a call to join the data back to the loT data in the transformation procedure. 4. Give the transformed table access to the dashboard tool. 5. Perform the aggregations on the dashboard tool.

B : 1. Create an external table over the JSON data in cloud storage. 2. Create a task that runs every 5 minutes to run a transformation procedure on new data based on a saved timestamp. 3. Ask the vendor to create a data share with the required data that can be imported into the company's Snowflake account. 4. Join the vendor's data back to the loT data using a transformation procedure. 5. Create views over the larger dataset to perform the aggregations required by the dashboard. 6. Give the views access to the dashboard tool.

C : privileges to create or manage shares), they can add an Enterprise edition account as a consumer. This feature enables flexibility in data sharing across different Snowflake account editions, facilitating broader data collaboration and accessibility.References: Snowflake's data sharing documentation and the specifics of edition-based capabilities discussed in SnowPro Advanced: Architect certification materials. Question #:47 A company is designing a process for importing a large amount of loT JSON data from cloud storage into Snowflake. New sets of loT data get generated and uploaded approximately every 5 minutes. Once the loT data is in Snowflake, the company needs up-to-date information from an external vendor to join to the data. This data is then presented to users through a dashboard that shows different levels of aggregation. The external vendor is a Snowflake customer. What solution will MINIMIZE complexity and MAXIMIZE performance? 1. Create an external table over the JSON data in cloud storage. 2. Create a task that runs every 5 minutes to run a transformation procedure on new data, based on a saved timestamp. 3. Ask the vendor to expose an API so an external function can be used to generate a call to join the data back to the loT data in the transformation procedure. 4. Give the transformed table access to the dashboard tool. 5. Perform the aggregations on the dashboard tool. 1. Create an external table over the JSON data in cloud storage. 2. Create a task that runs every 5 minutes to run a transformation procedure on new data based on a saved timestamp. 3. Ask the vendor to create a data share with the required data that can be imported into the company's Snowflake account. 4. Join the vendor's data back to the loT data using a transformation procedure. 5. Create views over the larger dataset to perform the aggregations required by the dashboard. 6. Give the views access to the dashboard tool. 1. Create a Snowpipe to bring the JSON data into Snowflake. 2. Use streams and tasks to trigger a transformation procedure when new JSON data arrives. 3. Ask the vendor to expose an API so an external function call can be made to join the vendor's data back to the loT data in a transformation procedure. 4. Create materialized views over the larger dataset to perform the aggregations required by the dashboard. 5. Give the materialized views access to the dashboard tool

D : 1. Create a Snowpipe to bring the JSON data into Snowflake. 2. Use streams and tasks to trigger a transformation procedure when new JSON data arrives. 3. Ask the vendor to create a data share with the required data that is then imported into the Snowflake account. 4. Join the vendor's data back to the loT data in a transformation procedure 5. Create materialized views over the larger dataset to perform the aggregations required by the dashboard. 6. Give the materialized views access to the dashboard tool.

Answer: D