As the current market demand for Machine Learning engineers keeps rocketing, and the immense amount of learning content keeps getting created. The Machine Learning Sphere is becoming more and more competitive. It is even harder for recruiters to evaluate the soundness and authenticity of a candidate’s application, especially for non-domain experts. Even Tech Recruiters can have a difficult time evaluating candidates as thresholds, references, and third-party courses aren’t always reliable and audited, especially when the need is either niche or very very specific.

That’s why there is an effort by known and trusted Tech Entities to create Methods to objectively certify a person's technical skills. These methods are also known as Certificates and are very helpful to source and select the right candidates in record time. Be it tech giants like Google, Amazon, Microsoft that have a broad list of skills or specialized companies like Oracle, that focus on a few things, everyone is trying their best to keep up with the demand and help us tech enthusiasts get recognized for our skills and get our dream jobs.

In Machine Learning specifically, we have the Tensorflow Developer Certificate, which we detailed in another post, and then company-specific certifications. For Google, there’s the Google Cloud Professional Data Engineer and the Professional Machine Learning Engineer that we will detail in this post. These certifications focus on Data & ML on the Google Cloud Platform or GCP.

Yes, Google Cloud Platform (GCP). It is one of the most popular and used Cloud Platforms that allow you to rent infrastructure & services on the Cloud. You can use GCP to rent storage, servers, cloud functions, containers and even notebooks and models. This is not counting the already existing AI services that you can roll out to your apps in some clicks. Since it is made by Google and thus natively supports Tensorflow as their ML language of choice.

GCP thus has a ton of features that can work together and is made so that both beginners and experts have an easy time exploiting it. It is thus one of the go-to solutions for many startups and tech companies. However, the sheer number of features and customizations is also a problem, as choosing the best options and combinations for each problem requires in-depth knowledge of GCP. Companies wanting to use the platform will therefore need someone that knows it well and architects solutions quickly and in a cost-effective way, especially at scale. Google Cloud Certificates allow these companies to identify and contact mentioned experts and hire them for their services fast, bypassing a whole period of trouble and stress. The Cloud Certified Directory lists all the currently certified people, and for machine learning, they’ll be looking at the Google Machine Learning Professional

Google Cloud Professional Machine Learning Engineer

Quite a handful as a name. Not only does this certificate prove that you have a deep understanding of Machine Learning and the Google Cloud Platform, but also that you have what it takes in terms of architecture, infrastructure, costs, deployment and that you can take strategic decisions regarding company Growth. All of the previously mentioned points will be in the test, and oh boy will it be difficult. That is one of the reasons why this certificate is highly valued, especially among Google Employees (For instance, my technical interview to become a Google Developer Expert became easier when I mentioned I passed this certificate).

Sounds cool — More details, please!

The certificate in itself is 2 hours long and will have about 120 questions, meaning you have about 1 minute per question. You can flag questions and come back to them later to double-check before submissions.

Questions are also randomly selected so don’t worry if you get very difficult questions at the beginning and don’t become too confident if you get easy questions. It is also easy to miss a single detail that can sabotage your answer, so stay on your toes and flag any question that you’re not 90% sure of.

You can NOT use any external resource during the test. You also can NOT have anyone with you in the room. You can NOT exit the room you are in either (I'll explain later). This means that for the duration of the exam, you will have to be 1000% focused on the exam. You can bring food and drinks with you BEFORE the exam so be sure to have anything you need ready beforehand.

Finally, results will not be shown to you after submission. You will have to wait for a couple of weeks before receiving an email, so if you need it for something (application, audit, training…) make sure to take it at least 3 weeks in advance.

Alright, no problem yet. What’s on the exam?

Nice try. I’d love to tell you exactly what’s on the exam, but like all certified individuals, I signed an NDA prohibiting me from sharing details. What I CAN share are somewhat general pieces of information.

The good thing is that Google regularly updates their content. When I took the exam, the exam guide was vague and I thought it would be easy. What a shock I got when I actually took the test. The current Exam guide however is very detailed and similar to what’s on the test really so I really recommend reading it in detail. To not repeat what’s already there however, here is my personal recollection of the type of questions:

1- Machine Learning Development with Tensorflow: These types of questions test your knowledge of actual coding with TF and Keras. They can tackle modeling, preprocessing, optimization, deployment and can either be to choose the right snippets to tell what’s wrong with the code. These are some of the easier ones and you’d have no problems if you have enough TF/Keras experience, are Tensorflow Certified, or watched and practiced along the Tensorflow Courses made by Google’s Laurence Moroney.

2- GCP services: These types of questions are very tricky if you’re not used to GCP’s services like AI Platform, Notebooks, ML APIs, Kubeflow, Composer… They’re tricky because the test supposes you already worked with them and will ask you what service is the best option given a situation, what pipeline of services is most optimal given a constraint, which service to choose given some features… For these questions, it is recommended you get hands-on experience with GCP. Luckily, there’s a Free Tier that gives you 300$ worth of credits, more than enough to test everything out.

3- Infrastructure, Deployment, and Automation: These types of questions require in-depth hands-on experience with projects as they test your ML Architecture skills. They pin you with constraints and features and ask you to choose the optimal infrastructures, deployment methods, and automation pipelines for different use cases. You will need to take into consideration costs, bottlenecks, Cloud space, Inference Speed, communication protocols, and even application architectures. Recommended preparation is simply to get real-life experience on challenging projects or get somehow involved in advanced hackathons and projects.

4- Strategic Decision Making: These types of question tests your ability to take long-term, delicate company decisions. They are not about code but real-life situations where your decision will affect a company strategy for years to come. You will need to take into consideration scalability, distribution, potential technical debt, team size, architecture and technology, costs, performance, public opinion… This is for seasoned CTOs and Tech Leads who saw the impact of their work in real life and saw how each variable affects the whole system. The only way to get ready for this is to be exposed to these projects.