A telecommunications company runs a Snowflake environment for network analytics. They have a large fact table (20 TB) with a clustering key on `event_time`. Queries frequently filter on `device_id` and `event_time`, but performance is poor. The team has already tried adding `device_id` as a second clustering key, but the table's clustering depth remains high, and queries are still slow. What is the most effective next step?

Increase the warehouse size to 2X-Large to scan the table faster.

Create a materialized view on the table that pre-aggregates data by `device_id` and `event_time`.

Use search optimization on the table to speed up point lookups.

Re-cluster the table using the ALTER TABLE ... RECLUSTER command.

A healthcare analytics company has a Snowflake environment with a multi-cluster warehouse (min 1, max 4) serving 50 concurrent users. Recently, a few users have been running long-running queries that consume all available clusters, causing other users' queries to queue. The team wants to ensure that short, interactive queries are not blocked by long-running jobs. What should they implement?

Set a statement timeout of 10 minutes on the warehouse to kill long-running queries.

Enable query acceleration service on the warehouse.

Create a separate warehouse for long-running batch queries and keep the existing warehouse for interactive queries.

Increase the maximum clusters to 10 to provide more capacity for all queries.

A media streaming company ingests clickstream data from millions of devices into Snowflake. Data arrives in small JSON files (1–5 MB) continuously throughout the day, with occasional spikes during major events. The current solution uses a single X-Small warehouse with auto-resume enabled to run COPY INTO statements every 5 minutes. During spikes, ingestion lags significantly. What is the most scalable approach to handle the variable ingestion volume?

Use Snowpipe with auto-ingest enabled and configure a multi-cluster warehouse with a maximum of 5 clusters for ingestion.

Keep the COPY INTO approach but run it every 1 minute to reduce lag.

Use Snowpipe with a dedicated X-Small warehouse and set auto-suspend to 1 minute.

Increase the warehouse size to 2X-Large and keep the current 5-minute COPY INTO schedule.

Which combination of warehouse settings allows Snowflake to automatically adjust compute resources to match fluctuating workloads without manual intervention?

Resource monitors with suspend action

Single-cluster warehouse with auto-suspend only

Multi-cluster warehouse with auto-scale and auto-suspend/resume enabled

Manual resizing of warehouse size based on daily schedules

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Domain 2Objective 1

Design Scalable Snowflake Solutions ARA-C01 Practice Questions (Page 3)

Part of the Architectural Design domain, which accounts for 26% of the ARA-C01 exam.

20questions here

4free pages

8concepts

26%of the exam

Questions 11–15

11expert · medium
A telecommunications company runs a Snowflake environment for network analytics. They have a large fact table (20 TB) with a clustering key on `event_time`. Queries frequently filter on `device_id` and `event_time`, but performance is poor. The team has already tried adding `device_id` as a second clustering key, but the table's clustering depth remains high, and queries are still slow. What is the most effective next step?
Select an answer first
12foundation · easy
Which Snowflake feature can be used to limit the total credit consumption of a virtual warehouse over a specified period?
Select an answer first
13application · medium
A healthcare analytics company has a Snowflake environment with a multi-cluster warehouse (min 1, max 4) serving 50 concurrent users. Recently, a few users have been running long-running queries that consume all available clusters, causing other users' queries to queue. The team wants to ensure that short, interactive queries are not blocked by long-running jobs. What should they implement?
Select an answer first
14application · medium
A media streaming company ingests clickstream data from millions of devices into Snowflake. Data arrives in small JSON files (1–5 MB) continuously throughout the day, with occasional spikes during major events. The current solution uses a single X-Small warehouse with auto-resume enabled to run COPY INTO statements every 5 minutes. During spikes, ingestion lags significantly. What is the most scalable approach to handle the variable ingestion volume?
Select an answer first
15foundation · easy
Which combination of warehouse settings allows Snowflake to automatically adjust compute resources to match fluctuating workloads without manual intervention?
Select an answer first

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Design Scalable Snowflake Solutions ARA-C01 Practice Questions (Page 3)

Questions 11–15

Which Snowflake feature can be used to limit the total credit consumption of a virtual warehouse over a specified period?

Which combination of warehouse settings allows Snowflake to automatically adjust compute resources to match fluctuating workloads without manual intervention?

Keep studying — Architectural Design