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My experience with AWS Certified Security – Specialty

Last week I took the AWS Certified Security – Specialty exam — and I passed with a score of 930 (Woohoo!!)

In this post I cover why I took it, what I did to pass, my overall exam experience, and some tips I learnt along the way.

So let’s go.

Why?

Why would anybody pay good money, subject themselves to hours of studying, only to end up sitting in a cold exam room for hours answering many multiple choice questions!

And the reward for that work is an unsigned PDF file claiming you’re ‘certified’, and ‘privilege’ access to buy AWS branded notebooks and water bottles!! Unless those water bottles come with a reserved instance for Microsoft SQL server in Bahrain, I’m not interested.

But, jokes asides, I did this for fun and profit, and fortunately I really did enjoy the preparing for this exam. It exposed me to AWS services that I barely knew — and forced me to level-up my skills even on those that I knew.

The exam has a massive focus on VPC, KMS, IAM, S3, EC2, Cloudtrail and Cloudwatch. While lightly touching Guardduty, Macie, Config, Inspector, Lambda, Cloudfront, WAF, System Manager and AWS Shield.

You need to catch you breath just reading through that list!

But for those diligently keeping count — you’d notice that the majority of those services are serverless — meaning the exam combined my two technological love-affairs … security and serverless!

I wasn’t lying when I said it was fun. So what about the profit.

I’m not sure how good this would be for my career (I literally got the cert last week), but for $300, it’s is relatively cheap, with a tonne of practical value. So trying to get an ROI on this, isn’t going to be hard.

For comparison, the CCSP certification cost nearly twice as much, is highly theoretical and requires professional experience.

The results also help me validate my past years of working on serverless projects, proving I wasn’t just some rando posting useless hobby projects on GitHub. Instead, I’m now a certified AWS professional, posting useless hobby projects on GitHub (it’s all about how you market it!)

So now that we’ve covered the why, let’s move onto how.

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Lambda functions in a VPC

In my honest (and truly humble) opinion, VPCs don’t make much sense in a serverless architecture — it’s not that they don’t add value, it’s that the value the add isn’t worth the complexity you incur.

After all, you can’t log into a lambda function, there are no inward connections allowed. And it isn’t a persistent environment, some functions may timeout after just 2-3 seconds. Sure, network level security is still worthy pursuit, but for serverless, tightly managing IAM roles and looking after your software supply chain for vulnerabilities would be better value for your money.

But if you’ve got a fleet of EC2s already deployed in a VPC, and your Lambda function needs access them. Then you have no choice but to deploy that function in a VPC as well. Or, if your org requires full network logging of all your workloads, then you’ll also need VPC (and their flow logs) to comply with such requests.

Don’t get me wrong, there is value in having your functions in a VPC, just probably not as much as you think.

Put that aside though, let’s dive into the wonderful world of Lambda functions and VPCs

Working Example

First, imagine we deploy a simple VPC with 4 subnets.

  1. A Public Subnet with a Nat Gateway inside it.
  2. A Private Subnet which routes all traffic through that NAT Gateway
  3. A Private Subnet without internet (only local routing)
  4. A Private Subnet without internet but with a SSM VPCe inside it

Let’s label these subnets (1), (2) ,(3) and (4) for simplicity.

Now we write some Lambda functions, and deploy each of them to each subnet. The functions have an attached security group that allows all outgoing connections, and similarly each subnet has very liberal NACLs that allow incoming and outgoing connections.

Then we create a gateway S3 VPC-endpoint (VPCe), and route subnet (4) to it.

Finally, we enable private DNS on the entire VPC. And then outside the subnet we create a bucket and an System Manager Parameter Store Parameter (AWS really need better terms for these things).

The final network looks like this:

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Amazon KMS: Intro

Amazon KMS is one of the most integrated AWS services, but probably also the least understood. Most developers know about it, and what it can do, but never really fully realize the potential of the service. So here’s a rundown…

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Why?!

The system, which was introduced on the first day of the 2020 school session yesterday, takes only two seconds to scan a pupil’s face before his personal information, such as full name, pupil number and class, is stored into the…

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Multi-Accounts for AWS with Google '+' emails.

Last week, I launched a new pipeline for Klayers to build Python3.8 Lambda layers in addition to Python3.7. For this, I needed a separate pipeline because not only is it a new runtime, but under the hood this Lambda uses a new Operating System (Amazon Linux 2 vs. Amazon Linux 1)

So I took the opportunity to make things right from an account hierarchy perspective. Klayers for Python3.7 lived in it’s own separate account from all my other hobby projects on AWS — but I kept all stages in it (default, dev and production). [note:Default is an odd-name, but it ties to the Terraform nomenclature]. This afforded some flexibility, but the account felt bloated from the weight of the different deployments — even though they existed in different regions.

It made no sense to have default and dev on the same account as production — especially since accounts were free. Having entirely separate accounts for prod & non-prod incurred no cost, and came with the benefit of additional free-tiers and tidier accounts with fewer resources in them — but the benefits don’t stop there.

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Keith @ PyconSG 2019

Had a blast at PyConSG 2019, really cool to be in the presence of so many pythonistas. Would definitely recommend, especially since python is one of the more broadly used languages (AI, Blockchain, RPA, etc). My talk was on AWS…

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Cloud Run — is it the ultimate Fat lambda?

Everyone knows that I’m a Lambda fanboy, and to be fair Lambda deserves all the praise it gets, it is **the** gold-standard for serverless functions. But yesterday, I gave Google Cloudrun a spin, and boy(!) is Lambda is going to get a run for its money.

Which is surprising given Google has traditionally lagged in this area — isn’t it quaint that we use words like ‘traditional’ in the serverless world!

But I digress.

The Lambda equivalent in the Google world, is Google cloud functions … which is (generously speaking) what lambda was 2 years ago– pretty boring. The only advantage I saw it having over Lambda, was the ability to build python packages natively in the requirements.txt file. But that incurred a build during deploy, which in turn had a limit.

And while, it did allow for a larger package size (double what AWS Lambda offers) it was severely more complex to understand. Just looking at it’s limit and pricing models can make you dizzy.

In short, Google Cloud functions lacked the simplicity of Lambda, with little benefit for incurring all that additional complexity.

But Cloud Run is something else. It’s still more complex than lambda, but here the trade-off seems worth it. So let’s take a peek at Google’s new serverless Golden Boy!

Containers vs. Functions

In Lambda the atomic unit of compute is the function, which for an interpreted language like Python is just plaintext code uploaded to AWS. But in Cloud Run the atomic unit is the container — and that can be a container for just the one function, or the container for the entire app itself — with all the routing logic embedded within it.

Now why would you need apps for the serverless world?! You ask indignantly. Aren’t these all supposed to be function based?

Well actually lots of people have legacy code written at the application level, and re-writing an entire application takes a long time, and very rarely succeeds on the first try.

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Multiprocessing in Lambda Functions

Lambda functions are awesome, but they only provide a single dimension to allocate resources – memorySize. The simplicity is refreshing, as lambda functions are complex enough — but AWS really shouldn’t have called it memorySize if it controls CPU as well.

Then again this is the company that gave us Systems Manager Session Manager, so the naming could have been worse (much worse!).

Anyway….I digress.

The memorySize of your lambda function, allocates both memory and CPU in proportion. i.e. twice as much memory gives you twice as much CPU.

The smallest lambda can start with minimum of 128MB of memory, which you can increment in steps of 64MB, all the way to 3008MB (just shy of 3GB).

So far, nothing special.

But, at 1792MB, something wonderful happens — you get one full vCPU. This is Gospel truth in lambda-land, because AWS documentation says so. In short, a 1792MB lambda function gets 1 vCPU, and a 128MB lambda function gets ~7% of that. (since 128MB is roughly 7% of 1792MB).

Using maths, we realize that at 3008MB, our lambda function is allocated 167% of vCPU.

But what does that 167% vCPU mean?!

I can rationalize anything up to 100%, after all getting 50% vCPU simply means you get the CPU for 50% of the time, and that makes sense up to 100%, but after that things get a bit wonky.

After all, why does having 120% vCPU mean — do you get 1 full core plus 20% of another? Or do you get 60% of two cores?