Google Database Engineer Beta Exam Thoughts
Being one who is trying to “catch them all”, I decided I was hardcore enough (or a glutton for punishment, I will let you decide…) and opted to do it after a bank holiday weekend where I was able to do a little additional study. So without further ado, this is my breakdown, augmented with some thoughts from one of my colleagues had who sat it a few hours before me.
General Exam Thoughts
I am going to start by simply stating that this, like all the professional level Google Exams, isn’t trivial. I took approximately 3 hours to answer the 120 questions, including some comments on a few of them (which is encouraged for beta exams).
I generally found the content to be very much on point, with only a couple of questions deviating into what I felt was the DevOps/SRE space (specifically around connectivity and monitoring). It covered a spectrum of database topics I have summarised below, though I found it favouring CloudSQL Postgres and Spanner on the SQL side along with Bigtable, and Firestore on the NoSQL side. I was slightly surprised by the references in the exam to ancillary (though I guess common) DBA tools; these included but were not limited to RMAN, Actifio, pgbouncer, and mysqldump, so I would recommend reading up on these and understanding common use cases.
In terms of learning material though I found the Google Database Engineer path to be a little lacking in the detail required for this exam, though it did provide a good refresher on topics such as bare metal solution for Oracle which I haven’t had reason to use in production yet. It also helped cover off several of the patterns and tools to migrate databases into GCP (such as the Database Migration Service). For a good understanding of the databases themselves, I would recommend the Cloud Guru course for the Data Engineer Certification, which I found myself relying upon, especially for the NoSQL and Cloud Spanner topologies and structures.
High-Level Exam Topics
In terms of high-level topics, this is some of what came up (from what I remember — it won’t be exhaustive!).
- Understand which databases to use in certain situations (recommend Priyanka’s sketch note above). Eg: Latency tradeoffs, Relational vs Non-Relational, OLTP vs OLAP etc.
- A LOT on HA/replica topologies both for Cloud SQL and Cloud Spanner, with a touch of Bigtable if I remember correctly.
- Database IAM best practices — I found myself weak on Cloud Spanner roles for example, as I haven’t yet worked on a project which justified its use.
- Understand performance ramifications of certain actions and topologies, e.g. increase disk for improved IO performance, when and how to provision read replicas etc.
- A LOT about migrating databases, I would recommend reading up about the database migration service and what it can and can’t do.
Recommended Exam Prep Materials
This is by no means an exhaustive list again but these are some of the things I found helpful:
- Official Exam Guide
- Official Learning Path
- Blog Post Introducing Exam
- Priyanka’s sketch notes
- GCP Database Decision Tree
- Cloud Guru Data Engineering Course
- Sathish VJ’s Awesome GCP Certifications Github Repo
So to conclude, this was a good, if tough exam that I wish I had been slightly better prepared for. I have no idea if I passed or not (with beta exams results take 6–8 weeks) though I am cautiously optimistic. Other than that — if trying to choose between Cloud Spanner, Cloud SQL, Firestore, Bigtable, BigQuery, and Memorystore is what makes you excited to get up in the morning, my company CTS is always looking for talented engineers that share our passion for all things Google Cloud. But until next time — keep it Googley ;-)
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