Databricks Data Engineer Professional study guide
Databricks Data Engineer Professional · Work through your exam, one topic at a time.
The Databricks Certified Data Engineer Professional is a proctored certification exam that validates advanced skills in building, optimising, and maintaining production-grade data engineering solutions on the Databricks Data + AI Platform, a multi-cloud environment. It is aimed at practitioners who already have hands-on experience with the platform rather than at newcomers: the source lists no formal prerequisites but states that hands-on experience performing the data engineering tasks outlined in the exam guide is recommended, and that related training is highly recommended. The Databricks Data Engineer Professional study guide has 10 sections and about 2,997 words.
01Developing Code for Data Processing using Python and SQL
Most marks here are lost by reaching for a user-defined function when a built-in exists. Before writing a UDF, check pyspark.sql.functions (including higher-order functions such as transform , filter , exists , aggregate , and try variants for safe casting).
320 words, about 2 min02Data Ingestion & Acquisition
COPY INTO and Auto Loader are the two managed paths into Delta. Both are idempotent by tracking which files were already processed, so a retried job does not duplicate rows.
312 words, about 2 min03Data Transformation, Cleansing, and Quality
dropDuplicates keeps an arbitrary row; when the question needs the latest record, use a window ( row number() over a partition ordered by an ingestion timestamp descending) or dropDuplicatesWithinWatermark for streaming. Tie-breaking matters: if the ordering column is not unique, results are non-deterministic.
299 words, about 1 min04Data Sharing and Federation
These are different problems and questions deliberately mix them. Delta Sharing moves data out to a recipient who reads it with the Delta Sharing protocol (open sharing, any client) or Databricks-to-Databricks sharing (Unity Catalog to Unity Catalog, no token handling by the user).
314 words, about 2 min05Monitoring and Alerting
Match the symptom to the observability source - that is the whole game in this domain. Job-level failures and durations: job run history and notifications.
276 words, about 1 min06Cost & Performance Optimisation
File layout dominates. Small files inflate metadata and task counts; fix with OPTIMIZE (compaction) and, where appropriate, VACUUM with a retention that respects readers.
278 words, about 1 min07Ensuring Data Security and Compliance
Privileges are granted on securables: metastore, catalog, schema, table, view, volume, function. Understand inheritance (a grant on a schema applies to its objects) and USE CATALOG / USE SCHEMA - a user can hold SELECT on a table yet still be denied because they lack USE on the parent schema.
286 words, about 1 min08Data Governance
The Unity Catalog hierarchy - metastore → catalog → schema → table/view/volume/function - is the backbone of governance questions. Know that managed tables live in the managed storage root of the metastore and are dropped with their data, while external tables point at an external location and survive a DROP TABLE as files.
302 words, about 2 min09Debugging and Deploying
Production code should be a wheel or a repo file, versioned in Git, tested outside the notebook. Declarative Automation Bundles ( databricks.yml with resources and targets ) describe jobs, pipelines, and their dependencies so the same definition is validated and deployed across dev/staging/prod - bundle validate , bundle deploy , bundle run .
309 words, about 2 min10Data Modelling
The medallion pattern is the default vocabulary: bronze (raw, append-only, schema-on-read tolerant), silver (conformed, deduplicated, typed), gold (dimensional or wide aggregates serving BI). Justify each layer by consumer, not habit - an exam question may ask why silver should not simply be a copy of bronze (answer: conformance, dedup, and stable contracts).
301 words, about 2 min