I was asked to review several solutions that I had led previously, explaining from an architecture perspective and then from a project management perspective. I was also asked to explain how I might improve upon those project's solutions with Databricks, or with the advances in Databricks since that solution was implemented.
Length, breadth hight depth on spark core, DLT, Unity catalog, code optimisation, scenarios, your project issues and how it was resolved
Give an example on how you tell a story for a complex technical discussion.
Technical interview to gauge experience with data engineering, SQL, and big data concepts
Q: How would you integrate the data from a large number of databases within a mesh of microservices. Q: How do you ensure data consistency in streaming processing systems.
how would you suggest a customer implements Databricks on their cloud infrastructure
different types of data and cloud architectures
Q: How would you get the data from the client's local ftp server to AWS s3? Q: What is so random about random forest? Q: How will you handle imbalanced classes in a particular classification problem?
Describe a typical IoT ingest pattern. What are databricks cluster types and how do they function?
How would you delete PII from a data lake based on requests to do so?
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