Amazon SAA-C03 Exam - Topic 2 Question 58 Discussion

Actual exam question for Amazon's SAA-C03 exam

Question #: 58
Topic #: 2

A solutions architect needs to optimize a large data analytics job that runs on an Amazon EMR cluster. The job takes 13 hours to finish. The cluster has multiple core nodes and worker nodes deployed on large, compute-optimized instances.

After reviewing EMR logs, the solutions architect discovers that several nodes are idle for more than 5 hours while the job is running. The solutions architect needs to optimize cluster performance.

Which solution will meet this requirement MOST cost-effectively?

AIncrease the number of core nodes to ensure there is enough processing power to handle the analytics job without any idle time.

BUse the EMR managed scaling feature to automatically resize the cluster based on workload.

CMigrate the analytics job to a set of AWS Lambda functions. Configure reserved concurrency for the functions.

DMigrate the analytics job core nodes to a memory-optimized instance type to reduce the total job runtime.

Show Suggested Answer

Suggested Answer: B

EMR managed scaling dynamically resizes the cluster by adding or removing nodes based on the workload. This feature helps minimize idle time and reduces costs by scaling the cluster to meet processing demands efficiently.

Option A: Increasing the number of core nodes might increase idle time further, as it does not address the root cause of underutilization.

Option C: Migrating the job to Lambda is infeasible for large analytics jobs due to resource and runtime constraints.

Option D: Changing to memory-optimized instances may not necessarily reduce idle time or optimize costs.

AWS Documentation Reference:

EMR Managed Scaling

by Yvonne at Apr 06, 2025, 12:57 PM

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Skye

3 months ago

D might help with runtime, but is it really cost-effective?

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Yuonne

3 months ago

Wait, can Lambda really handle that kind of job?

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Alexia

4 months ago

I disagree, increasing core nodes (A) seems more straightforward.

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Angella

4 months ago

Definitely go with B, managed scaling is a game changer!

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Armanda

4 months ago

B is the best option for cost-effectiveness!

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Vallie

4 months ago

I recall that memory-optimized instances can improve performance, but I wonder if just changing instance types is enough to address the idle nodes problem.

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Kendra

5 months ago

I think migrating to AWS Lambda could be a solution, but I'm not clear if it would handle the same workload effectively as EMR.

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Irma

5 months ago

I'm not entirely sure, but increasing the number of core nodes seems like it could lead to more costs without necessarily solving the idle time issue.

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Leonora

5 months ago

I remember we discussed how EMR managed scaling can help optimize costs by adjusting resources based on demand. That might be a good option here.

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Tyisha

5 months ago

Migrating to Lambda functions could be interesting, but I'm not sure if that would be the best fit for a large data analytics job. I'll need to weigh the pros and cons.

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Leigha

5 months ago

Increasing the core nodes could work, but that might not be the most cost-effective approach. I'll need to do some research on the other options.

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Martha

6 months ago

Hmm, the idle nodes are a clear sign that the cluster is not optimized. I'm leaning towards the EMR managed scaling feature to automatically resize the cluster.

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Novella

6 months ago

This seems like a tricky one. I'll need to carefully consider the options and think about the most cost-effective solution.

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Gearldine

10 months ago

I'm feeling like a solutions architect needs to have a spreadsheet the size of Texas to figure out the most cost-effective option here. It's like a choose-your-own-adventure, but with dollar signs!

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Ilene

9 months ago

Consider using spot instances for worker nodes to reduce costs

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Georgene

9 months ago

Use automatic scaling to adjust the number of core and worker nodes based on workload

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Sheridan

10 months ago

Add more tasks to the job to fully utilize all nodes

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Laurel

11 months ago

You know, I bet the solutions architect is wishing they had a crystal ball to see which option would work best. Maybe they should just throw a dart and see where it lands!

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Antonio

10 months ago

Upgrade to larger instances to increase processing power

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Roxane

10 months ago

Use automatic scaling to add or remove nodes based on workload

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Cherelle

10 months ago

Add more tasks to the job to fully utilize all nodes

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Fausto

11 months ago

Ooh, optimizing the instance type could be a game-changer! Cutting down the runtime by using memory-optimized instances might be the way to go.

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Dalene

10 months ago

I agree, optimizing the instance type seems like a cost-effective solution.

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Catrice

10 months ago

Maybe we should consider resizing the cluster to better utilize resources.

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Sylvia

11 months ago

Using memory-optimized instances could definitely help speed things up.

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Veronika

11 months ago

Migrating to Lambda functions? Interesting idea, but will that really be the best fit for a large data analytics job? I'm not sure the reserved concurrency would be enough.

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Iluminada

11 months ago

Hmm, increasing the core nodes sounds promising, but will it really be the most cost-effective solution? I wonder if the EMR managed scaling feature could handle this more efficiently.

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Renay

10 months ago

Definitely. It can help optimize cluster performance without the need for manual intervention.

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Novella

10 months ago

It sounds like a more efficient solution compared to manually adding more core nodes.

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Desire

11 months ago

That's a good point. Managed scaling can automatically adjust the number of core and task nodes based on workload.

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Malinda

11 months ago

I think using EMR managed scaling could be more cost-effective than increasing core nodes.

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Alishia

11 months ago

That's a good point, Charlesetta. Option D could indeed be a more efficient solution in terms of cost and performance.

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Charlesetta

11 months ago

I disagree, I believe option D is more cost-effective. Migrating to a memory-optimized instance type will reduce the total job runtime, saving both time and money.

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Alishia

11 months ago

I think option B is the best choice. It will automatically resize the cluster based on workload, optimizing performance and cost.

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