Databricks Certification Q&A – Shortcut Notes (Exam Point of View)
In this post you will find short and clear Databricks questions and answers that are useful for Databricks certification exams such as Data Engineer, Data Analyst and Apache Spark based certifications. All answers are written in exam-oriented, one–two line format for quick revision.
1. Databricks Platform Basics
Q1. What is Databricks?
Databricks is a cloud-based unified analytics platform built on Apache Spark that allows teams to do data engineering, data analytics and machine learning in one workspace.
Q2. What is a Databricks Workspace?
A workspace is the UI environment where you manage notebooks, repos, data, jobs, clusters and other assets.
Q3. What is a Cluster in Databricks?
A cluster is a set of virtual machines used to run notebooks, jobs and workloads; it provides the compute for Spark and SQL operations.
Q4. Difference between All-Purpose Cluster and Job Cluster?
All-purpose clusters are interactive, multi-user and long-running; Job clusters are created for a specific job or workflow run and terminated after completion.
Q5. What is Databricks SQL?
Databricks SQL is a SQL-first environment with SQL warehouses (or endpoints) used to run dashboards, BI queries and ad-hoc SQL over Lakehouse data.
2. Lakehouse & Delta Lake
Q6. What is the Lakehouse architecture?
Lakehouse combines data lake flexibility with data warehouse reliability, using Delta Lake for ACID, governance and performance on low-cost storage.
Q7. What is Delta Lake?
Delta Lake is a storage layer that adds ACID transactions, schema enforcement, time travel and performance optimizations to data stored on cloud object storage.
Q8. What is Medallion (Bronze–Silver–Gold) Architecture?
It is a layered design where Bronze holds raw data, Silver holds cleaned and conformed data, and Gold holds business-ready, aggregated data for BI and ML.
Q9. What is Time Travel in Delta Lake?
Time Travel allows you to query or restore previous versions of a Delta table using a version number or timestamp.
Q10. What is Schema Enforcement vs Schema Evolution?
Schema enforcement blocks writes that do not match the table schema; schema evolution allows compatible schema changes such as adding new columns.
3. Ingestion, Auto Loader & DLT
Q11. What is Auto Loader?
Auto Loader is a Databricks feature that incrementally and efficiently ingests new files from cloud storage with schema inference and evolution support.
Q12. What are Delta Live Tables (DLT)?
Delta Live Tables is a framework for building reliable, declarative ETL pipelines with built-in data quality checks, lineage and automatic orchestration.
Q13. Benefits of DLT for production ETL?
DLT simplifies managing dependencies, handles retries, ensures data quality with expectations and automatically manages pipeline execution and monitoring.
4. Performance & Optimization
Q14. What is Z-Ordering in Delta?
Z-Ordering reorders data files based on specified columns to improve data skipping and speed up highly selective queries.
Q15. What does the OPTIMIZE command do?
OPTIMIZE compacts many small files into fewer large files, improving read performance and query efficiency.
Q16. What does VACUUM do in Delta Lake?
VACUUM removes old, unreferenced data files based on a retention period to free storage and maintain table health.
Q17. What is the Catalyst Optimizer?
The Catalyst Optimizer is Spark SQL’s query optimizer that generates efficient physical execution plans from logical SQL queries.
Q18. What is the Photon engine?
Photon is a vectorized, C++–based execution engine in Databricks that accelerates SQL and Delta Lake workloads, especially on Databricks SQL.
5. Jobs, Workflows & Scheduling
Q19. What is a Databricks Job?
A Job is a scheduled or on-demand execution of one or more tasks such as notebooks, JARs or DLT pipelines.
Q20. What is a Task in a Databricks Workflow?
A task is an individual step within a workflow, such as running a notebook, Python script, SQL query or DLT pipeline, optionally dependent on other tasks.
Q21. Why use task dependencies?
Task dependencies control order of execution, ensuring that downstream tasks only run after upstream tasks succeed.
Q22. Common best practices for Jobs in exams?
Use job clusters, enable retries, configure alerts, set timeouts, and separate development and production jobs.
6. Streaming Concepts
Q23. What is Structured Streaming?
Structured Streaming is Spark’s high-level streaming API that treats streaming data as an unbounded table and supports incremental processing.
Q24. Why are checkpoints important in streaming?
Checkpoints store progress and state so that streaming jobs can recover from failures and ensure exactly-once processing.
Q25. Can Delta Lake be used for streaming?
Yes, Delta tables support both streaming reads and streaming writes with exactly-once guarantees.
7. Governance, Security & Unity Catalog
Q26. What is Unity Catalog?
Unity Catalog is a unified governance layer that manages data, schemas, tables, permissions, lineage and auditing across workspaces and clouds.
Q27. What is the hierarchy in Unity Catalog?
The typical hierarchy is Metastore → Catalog → Schema → Table/View/Function.
Q28. How is access control handled?
Access is managed using fine-grained permissions (GRANT/REVOKE) on catalogs, schemas, tables, views and functions.
Q29. What is row-level and column-level security?
Row-level security restricts which rows a user can see, while column-level security restricts access to specific columns such as PII fields.
8. MLflow & Machine Learning
Q30. What is MLflow?
MLflow is an open-source platform integrated with Databricks for managing the ML lifecycle, including experiment tracking, model registry and deployment.
Q31. What is an MLflow Run?
An MLflow run is a single execution of training or evaluation where parameters, metrics, tags and artifacts are logged.
Q32. What is the Model Registry?
The Model Registry is a centralized store for ML models with versioning, stages (Staging, Production) and governance.
9. Delta Table Details
Q33. What are Delta constraints?
Delta constraints such as NOT NULL and CHECK validate data on write and prevent invalid rows from being inserted.
Q34. What are identity columns?
Identity columns automatically generate sequential numeric values, often used as surrogate primary keys.
Q35. How to create a Delta table from a DataFrame?
You can use df.write.format("delta").save(path) or df.write.saveAsTable("table_name") with Delta configured as the default.
10. Exam Strategy & Tips
Q36. Which topics are most important for Databricks certifications?
Lakehouse concepts, Delta Lake features, Unity Catalog, Auto Loader, DLT, cluster types, jobs/workflows, Structured Streaming and optimization (OPTIMIZE, Z-ORDER, VACUUM).
Q37. Best way to prepare for scenario questions?
Focus on understanding when to use each feature: Auto Loader vs COPY INTO, job clusters vs all-purpose, DLT vs manual ETL, Unity Catalog for governance, and Delta for reliability.
Q38. How to quickly revise before exam?
Review core definitions, Medallion architecture, key commands (OPTIMIZE, VACUUM, DESCRIBE HISTORY, GRANT), and common design patterns for ingestion, transformation and serving.
Conclusion
Databricks certifications mainly test your understanding of Lakehouse concepts, Delta Lake behavior, governance with Unity Catalog, and correct design choices for real-world data engineering scenarios. Use this short Q&A as a quick revision sheet before your exam and revisit the topics where you feel less confident.
