Assignment 7
Due: Wednesday, July 2 at 11 AM ET to be submitted via Crowdmark
Note: Your submitted solution must be unique and written in your own words; if you collaborate with other students, external resources, and/or use AI tools, this must be clearly acknowledged in your submission.
This assignment covers material from Chapters 13-18 of DevOps for Data Science. Please complete the following exercises from the textbook:
Chapter 13 Q1: What are the parts of a FQDN? How does each of them get created?
Chapter 13 Q3: What are the different kinds of DNS records you’re likely to use?
Chapter 14 Q1: What are the two risks of using plain HTTP, and how does HTTPS mitigate them?
Chapter 15 Q1: What is the advantage of adopting a more complex networking setup over a server deployed directly on the internet? Are there advantages other than security?
Chapter 15 Q4: What are the most likely pain points for running a data science workbench that is fully offline/airgapped?
Chapter 16 Q2: What are some advantages of token-based auth? Why are most organizations adopting it? Are there any drawbacks?
Chapter 16 Q4: What are some different ways to manage permissions? What are the advantages and drawbacks of each?
Chapter 17 Q1: What is the difference between horizontal and vertical scaling? For each of the following examples, which one would be more appropriate?
You’re the only person using your data science workbench and run out of RAM because you’re working with very large datasets in memory.
Your company doubles the size of the team working on your data science workbench. Each person will be working with reasonably small data, but there will be a lot more of them.
You have a big modeling project that’s too large for your existing machine. The modeling you’re doing is highly parallelizable.
Chapter 17 Q2: What is the role of the load balancer in horizontal scaling? When do you need a load balancer and when can you go without?
Chapter 18 Q2: What are three tools IT/Admins might use to ensure packages are safe?