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Exam AWS Certified Solutions Architect - Associate SAA-C02 topic 1 question 479 discussion

Exam question from Amazon's AWS Certified Solutions Architect - Associate SAA-C02
Question #: 479
Topic #: 1
[All AWS Certified Solutions Architect - Associate SAA-C02 Questions]

A company is running an application on Amazon EC2 instances. Traffic to the workload increases substantially during business hours and decreases afterward.
The CPU utilization of an EC2 instance is a strong indicator of end-user demand on the application. The company has configured an Auto Scaling group to have a minimum group size of 2 EC2 instances and a maximum group size of 10 EC2 instances.
The company is concerned that the current scaling policy that is associated with the Auto Scaling group might not be correct. The company must avoid over- provisioning EC2 instances and incurring unnecessary costs.
What should a solutions architect recommend to meet these requirements?

  • A. Configure Amazon EC2 Auto Scaling to use a scheduled scaling plan and launch an additional 8 EC2 instances during business hours.
  • B. Configure AWS Auto Scaling to use a scaling plan that enables predictive scaling. Configure predictive scaling with a scaling mode of forecast and scale, and to enforce the maximum capacity setting during scaling.
  • C. Configure a step scaling policy to add 4 EC2 instances at 50% CPU utilization and add another 4 EC2 instances at 90% CPU utilization. Configure scale-in policies to perform the reverse and remove EC2 instances based on the two values.
  • D. Configure AWS Auto Scaling to have a desired capacity of 5 EC2 instances, and disable any existing scaling policies. Monitor the CPU utilization metric for 1 week. Then create dynamic scaling policies that are based on the observed values.
Show Suggested Answer Hide Answer
Suggested Answer: B 🗳️
by CobraBoy at Aug. 19, 2021, 9:02 p.m.
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suhas16c
Highly Voted 3 years, 7 months ago
Its can be B as well https://aws.amazon.com/about-aws/whats-new/2021/05/amazon-ec2-auto-scaling-introduces-predictive-scaling-native-scaling-policy/
upvoted 35 times
patriktre
3 years, 7 months ago
It is tricky question and more answers seems to be correct. I would go also with B: https://docs.aws.amazon.com/autoscaling/ec2/userguide/ec2-auto-scaling-predictive-scaling.html Predictive scaling is well suited for situations where you have: Cyclical traffic, such as high use of resources during regular business hours and low use of resources during evenings and weekends. It can use CPU as a metric for scaling in and out and requires the least effort to setup and maintain.
upvoted 10 times
gargaditya
3 years, 5 months ago
Predictive scaling can help you scale faster by launching capacity in advance of forecasted load, compared to using only dynamic scaling, which is reactive in nature. "It can use CPU as a metric for scaling in and out and requires the least effort to setup and maintain." I could not find this line in the link. CPU as a metric refers to 'The percentage of CPU that each instance should ideally use'. Notheless, I would choose C over B because B is more based on past trend/forecast and scales in advance based on that.
upvoted 2 times
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etheng1970
2 years, 10 months ago
after reading the article, i buy in the idea that this is a good use case of predictive scaling. so i go for B.
upvoted 2 times
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swadeey
3 years, 7 months ago
Since it is specifically mentioned "The CPU utilization of an EC2 instance is a strong indicator" it means it is referring to target tracking and more inclined towards step scaling. Step Scaling: With step scaling and simple scaling, you choose scaling metrics and threshold values for the CloudWatch alarms that invoke the scaling process. You also define how your Auto Scaling group should be scaled when a threshold is in breach for a specified number of evaluation periods. We strongly recommend that you use a target tracking scaling policy to scale on a metric like average CPU utilization or the RequestCountPerTarget metric from the Application Load Balancer. Predictive scaling in AWS With predictive scaling, AWS is introducing a new parameter called predicted capacity. Every day, predictive scaling forecasts the hourly capacity needed for each of the next 48 hours. Then, at the beginning of each hour, the predicted capacity value is set to the forecasted capacity needed for that hour.
upvoted 21 times
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CobraBoy
Highly Voted 3 years, 7 months ago
C - Step Scaling
upvoted 24 times
farciarz212
3 years, 5 months ago
my concern is with 90%. 90% is simply too high, it takes time to trigger alert on basic monitoring (every 5 minutes) and auto scale. Therefore my answer is B
upvoted 5 times
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BECAUSE
Most Recent 1 year, 11 months ago
Selected Answer: B
B is the answer
upvoted 1 times
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Mahadeva
2 years, 3 months ago
Selected Answer: D
Both Option A and C seem correct (Scheduled Scaling and Target Tracking using CPU utilization). Both, the question clearly states that the current scaling mechanism may not be good leading to over-provisioning and cost escalation. Left over choices: B and D. B is a predictive scaling, D is experimental. But B suggests that we provision maximum capacity during the scaling (if the forecast is not accurate, this may also lead to over provisioning). Keyword is maximum capacity setting. The leftover choice is option D.
upvoted 1 times
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jw1806
2 years, 7 months ago
Selected Answer: A
schedule makes sense because it only gets busy during business hours.
upvoted 1 times
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emmanuelodenyire
2 years, 8 months ago
Selected Answer: B
Let predictive scaling do it's work. Why guess the requirements yet this can be perfectly be handled using ML capabilities in this new scaling feature? I stand with B
upvoted 1 times
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rahularyan500
2 years, 8 months ago
Why not A? before actual traffic loads our auto-scaling group is ready for load
upvoted 1 times
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ahaz
2 years, 9 months ago
Selected Answer: B
Considerations: 1- There is already an auto scaling policy in place. So, there is no need to gather information to decide => D is out. 2- Between B and C, since the question is talking about a certain cyclical pattern (high spike during business hours), B seems to be a better answer. One might argue that C is better because the question clearly says that the max number of instances should be 10. I would then say it is still possible to have that max number for the predictive by setting the right value for Max capacity behavior. https://docs.aws.amazon.com/autoscaling/ec2/userguide/ec2-auto-scaling-predictive-scaling.html
upvoted 2 times
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queen101
2 years, 9 months ago
BBBBBBBBBBBBBBB
upvoted 1 times
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Nachiket_22_91
2 years, 9 months ago
Selected Answer: D
I would go with D Reason:- Organization is unsure about the upper limit of scaling and they need to determine what the upper limit should be. Hence they should analyze which can only be done by collecting data for a certain no of days. Point to focus on is:- " I would go with D Reason:- Organization is unsure about the upper limit of scaling and they need to determine what the upper limit should be. Hence they should analyze which can only be done by collecting data for a certain no of days. The point to focus on is:- " The organization must prevent excessive EC2 instance provisioning and paying unneeded fees. "
upvoted 2 times
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Tonero2016
2 years, 10 months ago
I will go for C. From the question they are more concerned about incurring unnecessary fees due to excessive provision. Enforcing a maximum capacity during scaling may not be the right option
upvoted 1 times
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slcheng
2 years, 10 months ago
Selected Answer: C
Vote C. question mentioned maximum group size of ten EC2 instances for an Auto Scaling group. answer C seen more relevant to the statement as asking which answer is satisfy it. B: too general.
upvoted 1 times
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blackwhites
2 years, 10 months ago
Best on B Predictive Scaling, now natively supported as an EC2 Auto Scaling policy, uses machine learning to schedule the right number of EC2 instances in anticipation of approaching traffic changes. Predictive Scaling predicts future traffic, including regularly-occurring spikes, and provisions the right number of EC2 instances in advance. Predictive Scaling’s machine learning algorithms detect changes in daily and weekly patterns, automatically adjusting their forecasts. This removes the need for manual adjustment of Auto Scaling parameters as cyclicality changes over time, making Auto Scaling simpler to configure. Auto Scaling enhanced with Predictive Scaling delivers faster, simpler, and more accurate capacity provisioning resulting in lower cost and more responsive applications.
upvoted 1 times
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naveenagurjara
2 years, 10 months ago
Selected Answer: B
It just states traffic increases and decreases.. does not specify step levels...
upvoted 2 times
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Longma98
2 years, 11 months ago
C is incorrect because if cpu is stuck at 90%, there is no ceiling how many instances will be added.
upvoted 2 times
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VijiTu
2 years, 11 months ago
Predictive scaling is well suited for situations where you have: Cyclical traffic, such as high use of resources during regular business hours and low use of resources during evenings and weekends Recurring on-and-off workload patterns, such as batch processing, testing, or periodic data analysis Applications that take a long time to initialize, causing a noticeable latency impact on application performance during scale-out events Answer is B as per the AWS docs
upvoted 1 times
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bighedgedog
2 years, 11 months ago
Selected Answer: B
I'd go for B. Predictive scaling is well suite for this and you don't have to guess specific CPU values or number of instances to add.
upvoted 3 times
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