Google Cloud Architect Exam Study Materials After recently completing the Google Cloud Architect certification, I wanted to share the preparation materials that I used. Due to the newness of the exam, one challenge is there is not the same abundance of preparation material as there is for other Cloud exams. The official Exam Guide leaves a bit to be desired and there is no official Practice Exam at the current time, so I hope this material is helpful for folks preparing. I prepared through a combination of methods outlined below (in addition to real-world GCP usage):
Official GCP Documentation
Google Cloud Next ’17 Conference Videos
Coursera
Linux Academy
This is probably a little overkill, but none of the aforementioned alone went into the depth in all of the areas that I had hoped. The answer was using the combination, and skipping areas in each that may have been redundant.
Side-note:One thing I liked about the exam is that I didn’t feel like any of the questions asked me for point-in-time questions (i.e. what did this feature do when the exam was released versus what it may do now as of July of 2017). As a result, you can prepare well by reviewing the most up-to-date documentation without a fear that your knowledge will be too accurate. That’s kind of a silly thing to say, but other exams from other vendors do have questions where you have to answer them based on a previous point in time, even if the version of the exam and product still match.
Most of these documents are overviews or FAQs. You may want to branch off of them into deeper areas, but I felt the Product Overview and FAQs were solid. I reviewed the product documentation last of all of the study material I used, and mostly used it to fill in gaps of knowledge, though; if you review this material first, you may want to go deeper.
The sessions from Google Cloud Next are on YouTube now (217 of them), and if there are any areas where you feel you’d like a little more depth (e.g. App Engine, Cloud Storage, Datastore, Stackdriver), these sessions can be helpful. They’re also just all really good sessions in general. Even if you’re not preparing for the exam, I’d recommend watching as many of the videos as you can. I like to watch them at 2.0x speed for maximum productivity.
The Coursera courses are made available by Google to partners but are available for anyone. They have a couple of options for courses, but if you are a current AWS Certified Architect Professional then there is a course available based on that, which is what I took. It compares GCP products with AWS products and is slimmed down from the other option.
I thought it was a valuable course, but lecture videos were fairly short. Total lecture time was 121 minutes, but there are quizzes and labs. The non-AWS specific course appears to be a little longer.
I subscribe to Linux Academy because they have a ton of great courses in general even outside of GCP, but they also have several Google Cloud Platform courses available. Honestly, I only skimmed through the course because I had done all of the other preparation prior, but the material seems solid.
Overall, I thought the exam was done well. It’s not too long, and the questions are good Architect-level questions: it’s about being able to architect solutions, not necessarily memorizing every low-level command. With that said, a few final thoughts as you prepare for the exam:
As the official exam guide notes, there are Case Studies as part of the exam. I recommend preparing yourself by reading those and figuring out how to define the requirements in each and how those requirements match up to GCP services.
Know this decision tree well, and understand when to use what:
Don’t forget about services like Dataflow, Dataproc, Pub/Sub, and other Big Data concepts.
Like with similar exams, there will be several good options and the best option. Make sure to really dive into each question to understand specific requirements that will help you determine which is best. You really need to know when to use X and when to use Y, even if Y would kind of work.
Get into Google Cloud Platform and build something! There’s nothing better than real-world experience, and Google makes it really easy to get in and use the platform for free.
Our aim was to choose a right cloud platform that would help us build, test, and deploy applications quickly in a scalable, reliable cloud environment. Although all large scale Cloud Platforms may seem similar in many ways, there are several fundamental differences in large scale cloud platforms.
We’re going to split this blog post into 2 parts:
Why we chose to move to GCP
Migrating to GCP without any downtime
Proof of ConceptWe started the process with a POC in which we considered existing running infra compatibilities with services offered by the Google Cloud Platform and also planned for elements in our future roadmap.
Key areas covered in POC:
⊹ Load Balancer
⊹ Compute Engine
⊹ Networking and Firewalls
⊹ Security
⊹ Cloud Resource Accessibility
⊹ Big Data
⊹ Billing
The POC included testing and verifying for VMs/Network/Load Balancer Throughput, Stability, Scalability, Security, Monitoring, Billing, Big Data and ML services. We took the big decision in June 2017 to migrate the entire infrastructure stack to the Google Cloud Platform.
We wanted to opt for a cloud platform that cloud take care of the myriad challenges we were facing:
⊹ Load Balancer:
We had faced many challenges while managing HAProxy inhouse clusters to handle a few tens of millions of daily active user base connections. Global Load Balancer (GLB) solved our many challenges.
Using GCP’s global load balancing, a single anycast IP can forward up to 1 million requests per second to various GCP back-ends such as Managed Instance Groups(MIG) and it didn’t require any pre-warming. Our overall response time improved to 1.7–2x as GLB’s utilizes a pool implementation that allows for the traffic to be distributed to multiple origins.
⊹ Compute Engine:
There was as such no big challenges in compute engines but we needed a performant platform at a viable cost. Google cloud VMs overall throughput has improved to 1.3–1.5x and thereby helping us to reduce the total number of running VM instances.
Redis benchmark tests were run across a cluster of 6 instances (8 core, 30GB each). From the results below, we concluded that GCP provides up to 48% better performance (on average) for most REDIS operations, and up to 77% better performance for specific REDIS operations.
Google Compute Engine (GCE) services added more values in our infrastructure management using:
● Managed Instance Group (MIG): MIGs help us to keep running our app services in a robust environment with multi-zone features instead of provisioning resource per zone. MIG automatically identifies and recreates unhealthy instances in a group to ensure that all of the instances are running optimally.
● Live Migration: Live migration helps us keep our VM instances running even when a host system down event occurs, like a software or hardware update. With our previous cloud partner, we used to get a schedule event notification for maintenance that forced us to stop and start VM to move on healthy VM.
● Custom VMs: In GCP’s we can create custom VM’s with the optimal amount of CPU and memory for workloads needed.
⊹ Networking and Firewalls:Manageability of multiple networks and firewall rules is not easy as this can lead to risk. GCP’s network VPC is global by default and enables inter-region communication with no extra setup and no change in network throughput. Firewall rules gives us flexibility within VPC for across projects using tag rule name.
For low-latency network and higher throughput we had to choose expensive 10G-capable instances and enabled enhanced networking on those instances.
⊹ Security:Security is most important aspect for any cloud provider. In our past experiences, security was either not available or optional to choose for most of services.
Google Cloud services are encrypted by default. GCP uses several layers of encryption to protect data. Using multiple layers of encryption adds redundant data protection and allows us to select the optimal approach based on application requirements. like Identity-Aware Proxy and encryption at rest by default.
GCP’s handling of the recent catastrophic speculative execution vulnerabilities in the vast majority of modern CPUs (Meltdown, Spectre) is also instructive. Google developed a novel binary modification technique called Retpoline which sidesteps the problem and transparently applied the change across running infrastructure without users noticing.
⊹ Cloud Resource Accessibility:GCP’s resource accessibility differs from other cloud providers as in GCP’s most resources, including the control panel, are either zonal or regional. We had to manage multiple VPCs for separate projects from separate accounts which needed VPC peering or VPN connection for private connectivity. We also had to maintain image replica in separate account too.
In Google Cloud most resources are either Global or Regional. This includes things like the control panel (where we can see all of our project’s VMs on a single screen), disk images, storage buckets (multi-region within a continent), VPC(but individual subnets are regional), Global Load Balancing, Pub/Sub etc.
⊹ Big Data:We went from a monolithic, hard to manage analytics setup to a full managed setup with BQ and resulted in 3 key areas of improvement:
● upto 50x Faster Querying
● Fully Managed & Autoscaled Data Systems
● Data Processing down to 15m from hours before.
⊹ Billing:It was tough to compare different cloud providers since many services were not similar or comparable, were different for different use cases, and dependent on unique use cases.
GCP’s advantages were:
● Sustained Use Discounts: Sustained These are applied on incremental VM use when they reach certain usage thresholds. We can automatically get up to 30%-off for workloads that run for a significant portion of the billing month.
● Per Minute Billing: GCE has a minimum slab of 10 minutes after billing per minute of actual VM usage. This provides a significant cost reduction given we don’t have to pay for the entire hour even when an instance runs for less than an hour.
● Superior Hardware, Fewer Instances: For almost all tiers and applications we found that it was possible to run the same workload at the same performance with fewer equivalent instances in GCP.
● Commitment Vs Reservation: One additional factor is GCP’s take on VM instance pricing. With AWS, the main way to cut VM instance costs is buying reserved instances for 1–3 years terms. If workload required change in VM configuration or we didn’t need the instance, we had to sell the instance on the Reserved Instance Marketplace with cheaper rate. With GCP’s, “Committed Use Discount” which is done for CPU and Memory reservation, it does not matter what kind of VM instances we are running.
Learning Cloud has been on my ToDo List for a very long time now. I even bought the trial version of Google Cloud Platform (GCP) but never knew where to start. Luckily, I came across the registration link for Cloud OnBoard. I registered immediately and attended the event on September 18, 2018, in Hyderabad. Read on to know why it was a life-changing experience for me.
The event covered all the widely used Cloud solutions across the industry i.e. compute, storage, big data, and machine learning. Overview, features and technical demonstration for each Google Cloud product were given in order to give an insight of what exactly GCP is capable of. Now that I have an idea about each cloud product available on GCP, I won’t have to research for suitable cloud solutions specific to the requirements of my projects.
I had a lot of expectations from the event and I was not disappointed at all. What I loved the most about the event is Google ensured the attendees get the opportunity to dive deep into GCP by providing various incentives like:
Access to the course, GCP Essentials, on Qwiklabs.
$200 credits in GCP account and waiver on registration fees of Google Cloud Certification upon successful completion of course on Qwiklabs.
Voucher for free access to Kubernetes course on Coursera.
Vouchers from Trainocate worth $100 valid for 6 months.
Wow! Google really helped me to get ‘On Board’. And of course, Google events are incomplete without loads of schwags, a cool t-shirt, and tasty food. I must say that deciding to attend the event was fruitful.
If Cloud OnBoard sounded interesting to you and you:
want to learn cloud but don’t know where to start from or, are looking for cloud solutions for the idea/project you’re working on then you can register here.
Just in case you are unable to register for the event, you can still attend the event. There is an on-the-spot registration booth set up just for you! Still, if you are unable to make to the event, don’t get disheartened! You can still access the event’s content here.
