Exam AWS Certified Solutions Architect - Associate SAA-C03 topic 1 question 342 discussion

B is NOT correct. the question said "The company does not have the resources to analyze the required capacity trends for the Auto Scaling group counts.". answer B said "Set the appropriate desired capacity, minimum capacity, and maximum capacity". how can someone set desired capacity if he has no resources to analyze the required capacity. Read carefully Amigo

A scheduled scaling policy allows you to set up specific times for your Auto Scaling group to scale out or scale in. By creating a scheduled scaling policy for the Auto Scaling group, you can set the appropriate desired capacity, minimum capacity, and maximum capacity, and set the recurrence to weekly. You can then set the start time to 30 minutes before the batch jobs run, ensuring that the required capacity is provisioned before the jobs run. Option C, creating a predictive scaling policy for the Auto Scaling group, is not necessary in this scenario since the company does not have the resources to analyze the required capacity trends for the Auto Scaling group counts. This would require analyzing the required capacity trends for the Auto Scaling group counts to determine the appropriate scaling policy.

B is correct answer ...........................................

Option A - Dynamic scaling policy is reactive not proactive. Option B - Scheduled policy is not suitable as it says the work loads can vary Option C - Correct Option D - Irrelevant

"The company does not have the resources to analyze the required capacity trends for the Auto Scaling group counts. " ==> this means this company can take advantage of cloudwatch metric and auto scaling on AWS to set up a predictive scaling group, so that no need to have any resources for this kind of task any more.??

Answer should be B Pref: https://docs.aws.amazon.com/autoscaling/ec2/userguide/ec2-auto-scaling-predictive-scaling.html Pref: https://docs.aws.amazon.com/autoscaling/ec2/userguide/predictive-scaling-policy-overview.html keyword for this question: "s. The company does not have the resources to analyze the required capacity trends for the Auto Scaling group counts"

B or C. I think C because the company needs an automated way to modify the autoscaling desired capacity

How does C works with : transactions can vary, clearly C is designed for workloads that are predictable, if the transactions can vary then predictive scaling will not work. The only one that will work is scheduled since its based on time not workload intensity.

C per https://docs.aws.amazon.com/autoscaling/ec2/userguide/predictive-scaling-create-policy.html. B is out because it wants the company to 'set the desired/minimum/maximum capacity' but "the company does not have the resources to analyze the required capacity".

Lambda did not appear to take over scripting/batch job, what a surprise

From GPT4: mong the provided options, creating a scheduled scaling policy (Option B) is the most direct and efficient way to ensure that the necessary capacity is provisioned 30 minutes before the weekly batch jobs run, with the least operational overhead. Here's a breakdown of Option B: B. Create a scheduled scaling policy for the Auto Scaling group. Set the appropriate desired capacity, minimum capacity, and maximum capacity. Set the recurrence to weekly. Set the start time to 30 minutes before the batch jobs run. Scheduled scaling allows you to change the desired capacity of your Auto Scaling group based on a schedule. In this case, setting the recurrence to weekly and adjusting the start time to 30 minutes before the batch jobs run will ensure that the necessary capacity is available when needed, without requiring manual intervention.

Predictive scaling: increases the number of EC2 instances in your Auto Scaling group in advance of daily and weekly patterns in traffic flows. If you have regular patterns of traffic increases use predictive scaling, to help you scale faster by launching capacity in advance of forecasted load. You don't have to spend time reviewing your application's load patterns and trying to schedule the right amount of capacity using scheduled scaling. Predictive scaling uses machine learning to predict capacity requirements based on historical data from CloudWatch. The machine learning algorithm consumes the available historical data and calculates capacity that best fits the historical load pattern, and then continuously learns based on new data to make future forecasts more accurate.

should be C. Question does not say how long the job will run. don't know when to set the end time in the schedule policy.

C is correct!

if the baseline CPU utilization is 60%, then that's enough information needed to determaine you to predict some aspect of the usage in the future. So key word "predictive" judging by past usage.

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B. you can make a vague estimation according to the resources used; you don't need to make machine-learning models to do that. You only need common sense.