Technical practice
Designing Lakehouse Architectures
What to coverExplain how you would design a data architecture that unifies data engineering, data science, and SQL analytics on the Databricks Lakehouse platform. Focus on the trade-offs between batch and streaming processing and how to ensure data consistency using Delta Lake.
Optimizing Apache Spark Workloads
What to coverDescribe your approach to troubleshooting and optimizing a slow-running Spark job. Discuss techniques like partition tuning, handling data skew, and leveraging broadcast joins to improve performance in distributed systems.
Integrating Cloud Services with Databricks
What to coverDiscuss the challenges of integrating Databricks with native cloud services (AWS, Azure, or GCP) for security and storage. Suggest how to manage identity, access control, and network connectivity in a hybrid cloud environment.
Machine Learning Lifecycle with MLflow
What to coverExplain how you would use MLflow to manage the end-to-end machine learning lifecycle, from experiment tracking to model deployment. Address how this improves reproducibility and collaboration in a team setting.
Data Engineering Pipeline Design
What to coverDescribe how you would build a robust, scalable data pipeline that handles schema evolution and data quality checks. Suggest how to implement these features using Delta Lake's capabilities.
Comparing Big Data Technologies
What to coverCompare the use cases for Spark versus traditional Hadoop-based systems or NoSQL databases like Cassandra. Suggest when it is appropriate to migrate legacy workloads to a modern Lakehouse architecture.
Validating Third-Party Integrations
What to coverWhen validating an integration between Databricks and a third-party application, what criteria do you use to ensure performance and reliability? Suggest a structured approach for conducting a proof of concept.
Contributing to Open Source
What to coverDiscuss the importance of open-source projects like Spark and Delta Lake in the current data landscape. Suggest how you would effectively promote these technologies to developers and technical stakeholders.
Behavioural practice
Managing Strategic Customer Relationships
What to coverDescribe a situation where you had to act as a technical advisor to a client with complex requirements. Explain how you balanced the customer's needs with the technical limitations of the platform to achieve a successful outcome.
Influencing Product Roadmap
What to coverProvide an example of how you have advocated for a customer's needs to an internal product or engineering team. Detail the steps you took to communicate the business impact and how you influenced the final decision.
Handling Technical Objections in Sales
What to coverDescribe a time you faced significant technical skepticism from a prospect during a pre-sales cycle. Explain how you used data, proof of concepts, or architectural demonstrations to build trust and win the account.
Collaborating Across Teams
What to coverGive an example of a time you collaborated with a sales team to develop an account strategy. Focus on how you translated complex technical concepts into business value for the client.