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Google Professional Cloud DevOps Engineer Exam - Topic 1 Question 64 Discussion

Actual exam question for Google's Professional Cloud DevOps Engineer exam
Question #: 64
Topic #: 1
[All Professional Cloud DevOps Engineer Questions]

You are performing a semi-annual capacity planning exercise for your flagship service You expect a service user growth rate of 10% month-over-month for the next six months Your service is fully containerized and runs on a Google Kubemetes Engine (GKE) standard cluster across three zones with cluster autoscaling enabled You currently consume about 30% of your total deployed CPU capacity and you require resilience against the failure of a zone. You want to ensure that your users experience minimal negative impact as a result of this growth o' as a result of zone failure while you avoid unnecessary costs How should you prepare to handle the predicted growth?

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Suggested Answer: A, D

The correct answers are A and D)

Examine the wall-clock time and the CPU time of the application. If the difference is substantial, increase the CPU resource allocation. This is a good way to determine if the application is CPU-bound, meaning that it spends more time waiting for the CPU than performing actual computation. Increasing the CPU resource allocation can improve the performance of CPU-bound applications1.

Examine the latency time, the wall-clock time, and the CPU time of the application. If the latency time is slowly burning down the error budget, and the difference between wall-clock time and CPU time is minimal, mark the application for optimization. This is a good way to determine if the application is I/O-bound, meaning that it spends more time waiting for input/output operations than performing actual computation. Increasing the CPU resource allocation will not help I/O-bound applications, and they may need optimization to reduce the number or duration of I/O operations2.

Answer B is incorrect because increasing the memory resource allocation will not help if the application is CPU-bound or I/O-bound. Memory allocation affects how much data the application can store and access in memory, but it does not affect how fast the application can process that data.

Answer C is incorrect because increasing the local disk storage allocation will not help if the application is CPU-bound or I/O-bound. Disk storage affects how much data the application can store and access on disk, but it does not affect how fast the application can process that data.

Answer E is incorrect because examining the heap usage of the application will not help to determine if the application needs performance tuning. Heap usage affects how much memory the application allocates for dynamic objects, but it does not affect how fast the application can process those objects. Moreover, low heap usage does not necessarily mean that the application is inefficient or unoptimized.


by Penney at Apr 10, 2024, 06:40 AM

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Paz
4 months ago
Wait, 80% more capacity? That seems excessive!
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Georgiana
4 months ago
D feels like overkill, but better safe than sorry, right?
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Anna
4 months ago
C seems risky, 30% usage doesn't mean you're safe for growth.
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Daniel
5 months ago
I disagree, B is too optimistic about autoscaling.
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Verlene
5 months ago
Sounds like A is the best option to verify resource needs!
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Lucy
5 months ago
I’m leaning towards option D, but adding 80% more capacity seems excessive. I wonder if we could just do a load test first to see what we actually need.
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Dona
5 months ago
I feel like option B is a bit misleading. Just because the cluster autoscaler is enabled doesn't mean it will handle all growth without any intervention.
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Kirk
5 months ago
I think option A sounds familiar from our practice questions. Verifying the max node pool size and using a Horizontal Pod Autoscaler seems like a good approach.
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Ma
6 months ago
I remember that with a 10% growth rate, we should definitely consider how much headroom we have, but I'm not sure if we need to add capacity right away.
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Gerri
6 months ago
I'd go with option A. Verifying the max node pool size and doing a load test seems like a prudent way to ensure you have the right capacity to handle the growth while maintaining resilience.
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Alesia
6 months ago
Wait, doesn't the question say the cluster is already set up with autoscaling? I'm a bit confused about whether we need to do anything else.
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Demetra
6 months ago
I think the key here is to verify the maximum node pool size and set up a Horizontal Pod Autoscaler. That way, the cluster can automatically scale up as needed to handle the growth.
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Launa
6 months ago
Okay, let's see. The service is already containerized and running on GKE, and the cluster has autoscaling enabled. That's a good starting point.
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Adrianna
6 months ago
Hmm, this is a tricky one. I'll need to carefully consider the details about the current setup and growth rate to determine the best approach.
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Ashley
6 months ago
This seems like a straightforward question about the pros and cons of using an open-source test automation tool versus a vendor tool. I'll need to carefully consider the licensing and cost implications.
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Shonda
6 months ago
I remember we practiced a question similar to this one, and deploying a service instance seemed like an important part. Maybe it's C?
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Lynelle
6 months ago
I'm a bit confused by the wording here. I'll need to re-read the question a few times to make sure I'm interpreting it correctly.
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Kallie
11 months ago
They should've included an option for 'Ask the Magic 8-Ball' - that's the only way to be sure!
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Tasia
9 months ago
Regularly monitor your resource usage and adjust your cluster capacity accordingly
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Ezekiel
9 months ago
Set up a multi-zone GKE cluster to ensure high availability in case of zone failure
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Leota
10 months ago
Implement horizontal pod autoscaling to automatically adjust the number of pods in your deployment based on CPU utilization
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Yoko
11 months ago
Hold up, I'm not about to underestimate that growth rate. Option D sounds like the safest bet to me.
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Ria
11 months ago
30% utilization? Piece of cake! I'm going with option C and calling it a day.
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Nichelle
9 months ago
User 3: Agreed, let's make sure we're using our capacity efficiently before scaling up.
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Chaya
9 months ago
User 2: Yeah, I think we should focus on optimizing our current resources before considering any major changes.
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Gearldine
10 months ago
User 1: Option C sounds good to me too.
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Gregoria
11 months ago
I'm betting on the cluster autoscaler to handle this. That's why we pay the big bucks for GKE, right?
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Kimberely
9 months ago
Monitor your cluster's resource usage regularly and adjust capacity as needed
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Cristina
10 months ago
Consider using preemptible VMs for non-critical workloads to save costs
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Jerry
10 months ago
Set up a multi-zone GKE cluster to ensure high availability in case of zone failure
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Raina
11 months ago
Enable horizontal pod autoscaling to automatically adjust the number of pods in your deployment based on CPU utilization
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Lenita
12 months ago
Ah, the good old capacity planning puzzle. Let's see what the experts have to say!
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Norah
10 months ago
B) Because you deployed the service on GKE and are using a cluster autoscaler your GKE cluster will scale automatically regardless of growth rate
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Vallie
11 months ago
A) Verify the maximum node pool size enable a Horizontal Pod Autoscaler and then perform a load lest to verify your expected resource needs
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Fairy
12 months ago
Adding 80% more node capacity seems like a safer option to ensure we have enough capacity for the predicted growth.
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Nakita
12 months ago
I agree with Barrie. Performing a load test to verify our expected resource needs is crucial.
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Barrie
12 months ago
I think we should verify the maximum node pool size and enable a Horizontal Pod Autoscaler.
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