Databricks Interview Questions Part 2

Here’s a list of tricky Azure Databricks interview questions that can help assess a candidate's deep understanding of the platform:

1. Cluster Configuration and Management:

  - What are the key differences between a standard cluster and a high-concurrency cluster in Azure Databricks? When would you use one over the other?

  - How does Azure Databricks autoscaling work? Can you configure autoscaling to handle sudden spikes in workload?

  - Explain the process of optimizing cluster utilization in Azure Databricks. How would you manage cost efficiency while maintaining performance?

2. Data Engineering and Processing:

  - How would you optimize a Spark job running on Azure Databricks that is experiencing performance bottlenecks?

  - Explain how to manage and optimize the use of Delta Lake in Azure Databricks for large-scale data processing.

  - Describe the role of Z-Order and Optimize commands in Delta Lake. When should you use them, and how do they impact performance?

3. Security and Governance:

  - **How do you secure sensitive data within Azure Databricks notebooks? What best practices would you implement?

  - What is the purpose of Azure Databricks Access Control Lists (ACLs), and how would you use them to manage permissions?

  - **How would you integrate Azure Databricks with Azure Key Vault for managing secrets and credentials securely?

4. Data Integration and Connectivity:

  - Explain the process of integrating Azure Databricks with Azure Data Factory. How would you orchestrate a pipeline that involves multiple Databricks notebooks?

  - How do you handle data ingestion from various sources into Azure Databricks? Discuss any challenges and how you would address them.

  - What are the key considerations when connecting Azure Databricks to external databases, and how would you optimize data transfer?

5. Advanced Analytics and Machine Learning:

  - How do you implement a machine learning model lifecycle in Azure Databricks, from experimentation to production deployment?

  - Explain the use of MLflow in Azure Databricks for tracking machine learning experiments. How do you manage different versions of models?

  - What challenges might you face when deploying a large-scale machine learning model in Azure Databricks, and how would you overcome them?

6. Performance Tuning and Optimization:

  - How would you diagnose and resolve a performance issue in a distributed Spark job running on Azure Databricks?

  - Describe the role of caching in Azure Databricks. When would you use the `cache()` function, and how does it impact job performance?

  - What are some common pitfalls that lead to inefficient Spark job execution in Azure Databricks, and how would you avoid them?

7. Monitoring and Troubleshooting:

  - How do you monitor and troubleshoot a long-running Azure Databricks job? What tools and metrics would you use?

  - What strategies would you employ to debug an intermittent issue in a notebook or pipeline running on Azure Databricks?

  - How would you approach troubleshooting a failed job in Azure Databricks? Walk through your process from start to finish.

8. Best Practices and Architecture:

  - What are some best practices for managing a multi-tenant Azure Databricks environment?

  - Explain the benefits and drawbacks of using Azure Databricks over other big data processing platforms like HDInsight or Synapse Analytics.

  - How would you design a scalable architecture in Azure Databricks to handle petabytes of data efficiently?

9. Integration with Other Azure Services:

  - How do you integrate Azure Databricks with Azure Synapse Analytics? What are the common use cases for such integration?

  - Describe how Azure Databricks can be used in a Data Lakehouse architecture. What advantages does it offer over traditional data warehouse solutions?

  - How would you set up an end-to-end ETL pipeline using Azure Databricks, Azure Data Lake Storage, and Azure SQL Database?

10. Version Control and Collaboration:

  - How do you manage version control in Azure Databricks? What are the challenges of using Git integration with notebooks?

  - Explain the process of collaborating on a Databricks notebook with multiple team members. How do you manage conflicts and versioning?

  - What are the best practices for organizing notebooks and code in Azure Databricks to ensure maintainability and scalability?

These questions are designed to evaluate a candidate’s ability to not only understand Azure Databricks but also to apply that knowledge in real-world scenarios, troubleshooting, and optimization.