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Without practical experience in GCP or AWS in background and personal knowledge about technologies like AWS Lambda's, Apache Kafka or Apache Cassandra etc. it would be hard to understand half of this course, especially about different data storages. Too much water. Sometimes questions or answers to them are generally incomprehensible. Like "orchestration" as an answer to a question about Google Cloud Dataflow use cases. Orchestration, really? Of what? Compute instances? Linux servers? Is it the same as Ansible or Salt? Absolutely confusing and not clear why and when I need Dataflow and other services also. Transactions are not the only one thing that people should understand. We need more practical use cases. And there are some other questions with the same problems. 90% of people who really passed the GCP certification recommended Linux Academy but not Coursera for the preparation. Now I have to try it to compare personally.

Overall, you made a great work. The platform is good and the course also is good. I found a lot of new information about GCP and now I need to dive into them deeply.

Thank you.

I am really happy with the way labs were set up. It was easy to get into and not much of a hassle for me to set everything up - I could just focus on learning. I love that.

Other aspect of those labs was... There just wasn't that much things to do in them. I would really love if there were some projects that we could do, on our own, without exact instructions, to achieve something. I think that it would definitely cement the knowledge into us.

The videos were good. Speakers were engaging and they spoke clean English. Clean English is very important for me in courses, because I don't want to listen to the same sentence a few times, in order for me to understand it. Every teacher should strive to minimize their use of accent.

The tests often time asked questions that just weren't given enough attention. It would be really good if you added additional, reading, materials to this course - to bolster the knowledge acquisition and retention.

Most things worked out nicely, but there were issues with two of the labs:

"Developing, Deploying, and Monitoring in the Cloud": The monitoring part did not work for me, as using the monitoring service now requires installing some agent on the VM. I followed the instructions, but starting the agent failed due to missing certificates. I found no straight-forward way of making the agent work on the VM so I could not test out the monitoring services.

"Getting started with BigQuery": After loading the logdata table, viewing it in the console fails with error "Unknown error response from the server". Stackoverflow says this is a known issue due to the table being too big. Making queries in the console or shell works fine, but you cannot directly see anything about the logdata table / its rows in the console due to this error.

En general, la mejor parte esta en los laboratorios, sin una excelente experiencia. Hay ciertos videos que son dificiles de entender porque se nota que la voz es generada por AI y no cuenta con ciertos detalles como cambio de ritmo o pausas premeditadas como lo haria un locutor. Un detalle, es que preferiblemente recomienden comenzar el curso habiendo realizado un curso basico de redes y manejo de la terminal de comandos, si no se tiene eso no se entenderia el valor infinitamente pragmatico que tiene usar la Consola de Google Cloud y montar un web server con un par de clicks o comandos, o conectarte via SSH solo dandole click al boton sin ninguna necesidad de crear llaves y configurar un cliente.

Muy contento en general, una gran experiencia.

The content of the slides were sufficient for the basic intent of this offering. The overview and reasoning behind why the products are competitive for other contending services was clear. When performing the labs, the content is very recipe driven. While that works well for someone attempting to get the work done, there needs to be some visuals prepared that show how the GCP components are laid out and the relationship they have to each other ( simple architecture views ). The GCP products have very similar sounding names, so paragraphs describing what is happening have less effect, whereas some simple architecture views of what the lab is intending to do to go with that in the lab content itself, might help.

Major portion of course is fine. But, in some sessions, focus of discussion suddenly moved, and difficult to follow. For example, in IAM session, the explanation of policy suddenly turns into the discussion of role without explaining relation between them.And, some using terms in quizzes are ambiguous. For example, an quiz is something like "while still offering various choices of runtime". Within the context of a session, this is meaningful. But, if it's single quiz, because there is no story of explanation there, it is not really clear whether it's "runtime environment", "runtime language" or "runtime library" It's quite confusing unless if I remember "EXACTLY what the trainer said".

This course is a good introduction to Google Cloud Platform and it has an interactive labs that allows you to play directly with real GCP so is a good way to get hands dirty and learn but since is a real environment some times the steps will not work as expected so requires you to adapt a bit. In regards the video content there are some typos in the slides not matching what the speaker is saying or the videos showcasing the UI of GCP not matching with the new UI of GCP or the video explaining the steps for the lab not matching with the steps that you need to perform in the lab itself.

The labs are where it's it with this course since most of the lectures feel like sales pitches instead of lectures. For the stuff I didn't previously have any familiarity with, I picked up some useful info. There were a few times where temporary creds/sessions logged me out or were invalidated, but nothing was too hard to deal with. I'm also pretty sure one of the commands to write to a file just straight-up did not work, so having an understanding of terminal editing (nano) tools was beneficial as it allowed me to make the appropriate edits to the required files without the script.