Databricks DE Pro study guide · 2026

The 9 Databricks DE Pro domains, explained

9 domains and 45 official objectives. Developing Code for Data Processing using Python and SQL carries the most weight at 23%. Each guide explains what the domain covers, how its questions are framed and where candidates lose marks.

Start with Domain 1 See all domains
Questions
60 scored multiple-choice (unscored items may be added)
Time
120 minutes
Passing score
Not published
Outline
Exam guide for the exam live from 9 Oct 2026 (current)

Exam blueprint

Bar widths follow the official weights. Weight ranges are drawn at their midpoint.

  1. 1 23%
  2. 2 12%
  3. 3 12%
  4. 4 10%
  5. 5 15%
  6. 6 8%
  7. 7 5%
  8. 8 10%
  9. 9 5%
  1. 1 Developing Code for Data Processing using Python and SQL
  2. 2 Data Ingestion & Acquisition
  3. 3 Data Manipulation
  4. 4 Monitoring and Alerting
  5. 5 Cost & Performance Optimization
  6. 6 Ensuring Data Security and Compliance
  7. 7 Data Governance
  8. 8 Debugging and Deploying
  9. 9 Data Modeling

All 9 domains

45 official objectives 69–93 hrs suggested study

  1. Domain 01 23%

    Developing Code for Data Processing using Python and SQL

    The heaviest section at 23%. It tests how you write, package, test and schedule production pipeline code: bundle project layout, library fixes, UDFs, Lakeflow Pipelines with Auto Loader and AUTO CDC, Lakeflow Jobs control flow, compute choices and stateful streaming.

    Inside this domain

    • Implement scalable Python project structures for Declarative Automation Bundles that support modular development and CI/CD
    • Apply troubleshooting techniques to dependency conflicts and installation failures for external libraries across serverless, pipeline and bundle environments
    • Develop User-Defined Functions (UDFs) using Pandas, Python and SQL, including Unity Catalog functions, for given constraints
    • Create production-ready data pipelines for streaming data using Lakeflow Pipelines and Auto Loader
    • and 8 more objectives

    Watch for MERGE is always enough

    Objectives
    12
    Questions est.
    ~14
    Study time est.
    18–24 hrs
  2. Domain 02 12%

    Data Ingestion & Acquisition

    Worth 12%, this section asks how data gets into the lakehouse or is reached in place: files and message buses, CDC into Delta or Iceberg, Lakeflow Connect database connectors, OpenSharing and Clean Rooms, and governed Lakehouse Federation.

    Inside this domain

    • Develop data ingestion pipelines to ingest a variety of data formats, including Delta Lake, Parquet, JSON, Iceberg, CSV and Binary, from diverse sources such as message buses (Kafka, Kinesis, Pub/Sub) and cloud storage
    • Configure incremental CDC pipelines using Lakeflow Pipelines with Delta or Iceberg as the target table format
    • Configure CDC ingestion pipelines from relational database sources, including SQL Server, MySQL and PostgreSQL, using Lakeflow Connect
    • Implement OpenSharing (D2D and Databricks-to-Open) and Clean Rooms for privacy-preserving collaboration
    • and 1 more objectives

    Watch for Federation for ETL

    Objectives
    5
    Questions est.
    ~7
    Study time est.
    8–11 hrs
  3. Domain 03 12%

    Data Manipulation

    Section 3 is worth 12% and covers transforming data at scale: window functions, joins and aggregations in Spark SQL and PySpark, semi-structured data with VARIANT, AI functions such as ai_query inside pipelines, and expectations that drop, quarantine or fail bad records.

    Inside this domain

    • Apply advanced data transformations, including window functions, joins and aggregations, using Spark SQL and PySpark on large datasets
    • Develop a model and query semi-structured data using the VARIANT data type, including functions such as parse_json and variant_get
    • Apply AI functions, including ai_query, to perform model inference within data pipelines for enrichment and classification
    • Implement data quality expectations in Lakeflow Pipelines to quarantine, drop or fail on bad records

    Watch for Default expectation drops

    Objectives
    4
    Questions est.
    ~7
    Study time est.
    8–10 hrs
  4. Domain 04 10%

    Monitoring and Alerting

    Monitoring and Alerting is 10% of the exam. It covers watching cost, usage and job health through system tables, the REST API, CLI and SDK, pipeline event logs, alerts on governed metrics, and the Lakeflow Jobs UI and Jobs API.

    Inside this domain

    • Use Databricks system tables (billing, compute, access, lakeflow) for cost analysis, auditing and workload monitoring
    • Use Databricks REST APIs / CLI and SDK for monitoring and analyzing jobs and pipelines
    • Use Pipeline event logs to monitor pipeline health and data quality
    • Use Databricks Lakehouse alerts to monitor governed business metrics, data quality and anomaly signals, usage and cost, SQL warehouse and query health, audit and security events, AI agent quality, and Lakeflow Job branching
    • and 1 more objectives

    Watch for Cost in the Jobs UI

    Objectives
    5
    Questions est.
    ~6
    Study time est.
    6–8 hrs
  5. Domain 05 15%

    Cost & Performance Optimization

    At 15%, this is the second-largest section. It tests how managed tables, Predictive Optimization, Liquid Clustering, deletion vectors, disk caching and Change Data Feed cut cost and latency, and how to read a query profile to find bottlenecks.

    Inside this domain

    • Explain how Unity Catalog managed tables, Predictive Optimization and Liquid Clustering reduce operational overhead for a workload
    • Choose the appropriate Delta optimization technique (deletion vectors, Liquid Clustering, CLUSTER BY AUTO) for a given table access pattern
    • Understand how caching (Delta cache) improves query performance for repeated reads of the same data
    • Apply Change Data Feed (CDF) to expose row-level changes (updates and deletes) for efficient incremental downstream processing
    • and 2 more objectives

    Watch for ZORDER keeps up

    Objectives
    6
    Questions est.
    ~9
    Study time est.
    10–14 hrs
  6. Domain 06 8%

    Ensuring Data Security and Compliance

    Section 6 is worth 8% and focuses on protecting sensitive data: least-privilege grants, attribute-based policies with governed tags, anonymization and pseudonymization, PII detection and masking in pipelines, and purging data to meet retention and erasure rules.

    Inside this domain

    • Apply least-privilege access control lists (ACLs) to secure Unity Catalog securable objects and workspace resources
    • Apply attribute-based access control (ABAC) policies with governed tags to enforce row filters and column masks at scale
    • Apply anonymization and pseudonymization methods, such as hashing, tokenization, suppression and generalization, on confidential data
    • Implement a compliant data pipeline that enforces PII detection and masking controls across batch and streaming using Unity Catalog
    • and 1 more objectives

    Watch for DELETE is erasure

    Objectives
    5
    Questions est.
    ~5
    Study time est.
    6–8 hrs
  7. Domain 07 5%

    Data Governance

    The smallest sections share 5% each, and this one has just two objectives: using Unity Catalog tags and comments to make data discoverable, and understanding how permissions inherit from catalogs to schemas to objects.

    Inside this domain

    • Demonstrate understanding of Unity Catalog tags and comments as mechanisms for adding metadata to improve discoverability
    • Demonstrate understanding of the Unity Catalog permission inheritance model across catalogs, schemas and objects

    Watch for DENY exists

    Objectives
    2
    Questions est.
    ~3
    Study time est.
    3–4 hrs
  8. Domain 08 10%

    Debugging and Deploying

    Debugging and Deploying carries 10%. It covers finding diagnostic evidence in the Spark UI, logs, system tables and query profiles, repairing failed job runs, deploying with Declarative Automation Bundles, and Git-based CI/CD with Databricks Git folders.

    Inside this domain

    • Identify pertinent diagnostic information using Spark UI, cluster logs, system tables and query profiles to troubleshoot errors
    • Analyze the errors and remediate the failed job runs with job repairs and parameter overrides
    • Deploy Databricks resources using Declarative Automation Bundles (formerly Databricks Asset Bundles)
    • Configure Git-based CI/CD workflows using Databricks Git folders (formerly Repos) to deploy notebooks and code

    Watch for Full rerun

    Objectives
    4
    Questions est.
    ~6
    Study time est.
    6–8 hrs
  9. Domain 09 5%

    Data Modeling

    Data Modeling is worth 5% and has two objectives: designing scalable Delta and Iceberg table layouts for large data, and building dimensional models for analytics with materialized views and Unity Catalog metric views.

    Inside this domain

    • Design scalable Delta and Iceberg table layouts for large data assets, mapping partitioning to data grain, aligning clustering to access patterns, and managing compaction
    • Design dimensional models for analytical workloads, leveraging Materialized Views and Unity Catalog Metric Views for governed metric definitions

    Watch for Partition by ID

    Objectives
    2
    Questions est.
    ~3
    Study time est.
    4–6 hrs

Study the heaviest domains first

Marks follow weight. This order puts the most marks behind you first, across about 69–93 hrs of study.

  1. 1 Developing Code for Data Processing using Python and SQL 23% · 18–24 hrs
  2. 5 Cost & Performance Optimization 15% · 10–14 hrs
  3. 2 Data Ingestion & Acquisition 12% · 8–11 hrs
  4. 3 Data Manipulation 12% · 8–10 hrs
  5. 4 Monitoring and Alerting 10% · 6–8 hrs
  6. 8 Debugging and Deploying 10% · 6–8 hrs
  7. 6 Ensuring Data Security and Compliance 8% · 6–8 hrs
  8. 7 Data Governance 5% · 3–4 hrs
  9. 9 Data Modeling 5% · 4–6 hrs

Try one

Practice questionDomain 1: Developing Code for Data Processing using Python and SQL

A Lakeflow Pipeline receives CDC events for a products table from a message bus. Events can arrive several minutes out of order, and analysts need every historical price. Which approach meets the requirement?

One trap from every domain

The answer that looks right, and the clue that gives it away.

  • Domain 1

    MERGE is always enough

    Clue The scenario mentions late or out-of-order change records.

  • Domain 2

    Federation for ETL

    Clue The workload is large and recurring.

  • Domain 3

    Default expectation drops

    Clue The code uses expect without _or_drop or _or_fail.

  • Domain 4

    Cost in the Jobs UI

    Clue The stakeholder asks for money or DBUs.

  • Domain 5

    ZORDER keeps up

    Clue The table receives regular appends.

  • Domain 6

    DELETE is erasure

    Clue The requirement says data must be unrecoverable.

  • Domain 7

    DENY exists

    Clue An option uses DENY or REVOKE on an inherited privilege.

  • Domain 8

    Full rerun

    Clue Several upstream tasks already succeeded.

Other Data & Analytics certifications

Study guides for exams that overlap with Databricks DE Pro.

Practise what you read

Databricks DE Pro domains: common questions

How many domains are in the Databricks DE Pro exam?

Databricks DE Pro has 9 domains with 45 official objectives between them: Developing Code for Data Processing using Python and SQL, Data Ingestion & Acquisition, Data Manipulation, Monitoring and Alerting, Cost & Performance Optimization, Ensuring Data Security and Compliance, Data Governance, Debugging and Deploying and Data Modeling.

Which Databricks DE Pro domain carries the most weight?

Developing Code for Data Processing using Python and SQL is the heaviest at 23%, followed by Cost & Performance Optimization (15%). The lightest is Data Modeling (5%). Weights come from Databricks's official outline.

What is the Databricks DE Pro exam format?

Questions: 60 scored multiple-choice (unscored items may be added). Time allowed: 120 minutes. Passing score: Not published.

How long should I study for Databricks DE Pro?

Our planning estimate is 69–93 hrs across all domains, split by weight. It is an editorial estimate, not an official figure, so adjust it to your experience and to how you score on practice questions.

Where should I start?

Start with Developing Code for Data Processing using Python and SQL, because it carries the most marks, then work down by weight. Use the checklist on each domain page to confirm you can do what the domain asks before moving on.

Which outline is this guide based on?

This guide follows Exam guide for the exam live from 9 Oct 2026 (current), published by Databricks. It was last reviewed in October 2026.

Domain names, weights and objectives come from Databricks's official outline (Exam guide for the exam live from 9 Oct 2026 (current)). Question counts and study hours are editorial planning estimates, not official figures. Last reviewed October 2026.