Free Snowflake ARA-R01 Exam Questions

Question 1

The following DDL command was used to create a task based on a stream:

Assuming MY_WH is set to auto_suspend – 60 and used exclusively for this task, which statement is true?

A : The warehouse MY_WH will be made active every five minutes to check the stream.

B : The warehouse MY_WH will only be active when there are results in the stream.

C : The warehouse MY_WH will never suspend.

D : The warehouse MY_WH will automatically resize to accommodate the size of the stream.

Answer: B

Question 2

The Data Engineering team at a large manufacturing company needs to engineer data coming from many

sources to support a wide variety of use cases and data consumer requirements which include:

1) Finance and Vendor Management team members who require reporting and visualization

2) Data Science team members who require access to raw data for ML model development

3) Sales team members who require engineered and protected data for data monetization

What Snowflake data modeling approaches will meet these requirements? (Choose two.)

A : Consolidate data in the company’s data lake and use EXTERNAL TABLES.

B : Create a raw database for landing and persisting raw data entering the data pipelines.

C : Create a set of profile-specific databases that aligns data with usage patterns.

D : Create a single star schema in a single database to support all consumers’ requirements.

E : Create a Data Vault as the sole data pipeline endpoint and have all consumers directly access the Vault.

Answer: B,C

Question 3

When loading data into a table that captures the load time in a column with a default value of either CURRENT_TIME () or CURRENT_TIMESTAMP() what will occur?

A : All rows loaded using a specific COPY statement will have varying timestamps based on when the rows were inserted.

B : Any rows loaded using a specific COPY statement will have varying timestamps based on when the rows were read from the source.

C : Any rows loaded using a specific COPY statement will have varying timestamps based on when the rows were created in the source.

D : All rows loaded using a specific COPY statement will have the same timestamp value.

Answer: D

Question 4

Following objects can be cloned in snowflake

A : Permanent table

B : Transient table

C : Temporary table

D : External tables

E : Internal stages

Answer: A,B,D

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