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Snowflake ARA-C01 Exam - Topic 6 Question 51 Discussion

The Business Intelligence team reports that when some team members run queries for their dashboards in parallel with others, the query response time is getting significantly slower What can a Snowflake Architect do to identify what is occurring and troubleshoot this issue?A)B)C)D)
A) Option A
B) Option B
C) Option C
D) Option D

Snowflake ARA-C01 Exam - Topic 6 Question 51 Discussion

Actual exam question for Snowflake's ARA-C01 exam
Question #: 51
Topic #: 6
[All ARA-C01 Questions]

The Business Intelligence team reports that when some team members run queries for their dashboards in parallel with others, the query response time is getting significantly slower What can a Snowflake Architect do to identify what is occurring and troubleshoot this issue?

A)

B)

C)

D)

Show Suggested Answer Hide Answer
Suggested Answer: A

The image shows a SQL query that can be used to identify which queries are spilled to remote storage and suggests changing the warehouse parameters to address this issue. Spilling to remote storage occurs when the memory allocated to a warehouse is insufficient to process a query, and Snowflake uses disk or cloud storage as a temporary cache. This can significantly slow down the query performance and increase the cost. To troubleshoot this issue, a Snowflake Architect can run the query shown in the image to find out which queries are spilling, how much data they are spilling, and which warehouses they are using.Then, the architect can adjust the warehouse size, type, or scaling policy to provide enough memory for the queries and avoid spilling12.Reference:

Recognizing Disk Spilling

Managing the Kafka Connector


by Chantay at Jun 16, 2025, 08:04 PM

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Tawna
7 months ago
Not sure if that's the only reason, could be other factors at play too.
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Ashton
7 months ago
Option B seems like the best way to troubleshoot this issue.
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Daniel
7 months ago
I totally agree, it's all about resource contention!
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Daron
7 months ago
Query response time can definitely slow down with parallel runs.
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Maxima
8 months ago
Surprised to hear this, I thought Snowflake handled concurrency better.
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Laila
8 months ago
I vaguely remember something about resource contention and how it impacts response times, but I'm not confident about the best approach to troubleshoot it.
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Franklyn
8 months ago
I feel like option C might be the right choice, but I can't recall the exact details about how to analyze the workload.
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Chery
8 months ago
I think we practiced a similar question about monitoring query performance in Snowflake. Was it about looking at the query history or something else?
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Terrilyn
8 months ago
I remember we discussed how query performance can be affected by concurrency, but I'm not sure which specific metrics to check first.
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Kristel
9 months ago
I'm not sure about this one. I'll need to review the Snowflake documentation to make sure I understand the different tools and features that could help with this issue.
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Art
9 months ago
This seems straightforward. I'm pretty confident that Option C is the right approach to troubleshoot the parallel query performance problem.
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Kristian
9 months ago
Okay, I think I've got a strategy here. I'll focus on identifying the root cause of the performance issue, then evaluate the options to see which one best addresses that.
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Rodolfo
9 months ago
Hmm, I'm a bit confused by the different options here. I'll need to make sure I understand what each one is suggesting before I can decide.
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Olen
9 months ago
This looks like a tricky one. I'll need to carefully review the options and think through the potential causes of the slow query response times.
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Lashaun
12 months ago
I'm going with Option A. It's the easiest solution, and as a Snowflake Architect, I'm all about taking the path of least resistance. Lazy, but efficient!
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Linwood
11 months ago
User 2: Yeah, I agree. Sometimes the simplest solution is the best.
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Kenneth
11 months ago
User 1: I think Option A is the way to go. Quick and easy fix.
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Emerson
1 year ago
Option D is the way to go! Who needs performance optimization when you can just throw more money at the problem? That's the Snowflake way!
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Asuncion
11 months ago
True, throwing more money at the problem might not always be the best approach. It's worth exploring other options first.
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Detra
11 months ago
I agree, it's important to identify the root cause of the issue before deciding on a solution.
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Alica
11 months ago
Option D is definitely a quick fix, but it might not be the most cost-effective solution in the long run.
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Nieves
1 year ago
In addition to that, we can review the Snowflake warehouse configuration to ensure it is optimized for parallel queries.
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Pearlie
1 year ago
I agree with Heike. We can also check the system resource utilization to see if there are any bottlenecks.
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Heike
1 year ago
I think we should start by analyzing the query execution plans to see if there are any inefficiencies.
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Reuben
1 year ago
Hmm, I'm torn between B and C. Might as well just spin up another Snowflake cluster and see if that fixes it. You know what they say, 'more is better!'
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Miss
12 months ago
Agreed, adding more resources could be the solution. Give it a try and see if it resolves the problem.
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Viola
12 months ago
Yeah, more clusters could definitely improve the performance. Maybe try option B or C to troubleshoot the issue.
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Sharita
12 months ago
I think spinning up another Snowflake cluster might help with the slow query response time.
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Sheridan
1 year ago
Option C looks like the right choice here. Monitoring query performance and identifying resource constraints is key to troubleshooting the slowdown.
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Oliva
1 year ago
Yes, Option C seems to be the best choice for troubleshooting the slowdown.
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Ty
1 year ago
I agree, monitoring query performance is crucial in identifying the issue.
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