18 lessons rebuilt from scratch for the new May 4, 2026 exam guide — including ABAC, Lakeflow Connect, Liquid Clustering and Automation Bundles. Scenario-based mock exams, executable notebooks, and our Athena AI tutor on WhatsApp citing the exact lesson when you ask.
Each lesson's depth matches its real exam weight. No fluff on low-weight areas, no shortcuts on the heavy ones.
Workspace, compute types, Delta basics — the foundation.
COPY INTO patterns, Auto Loader deep dive, Lakeflow Connect.
Bronze→Silver→Gold with PySpark/SQL, joins, dedup, tuning.
DAGs, tasks, dependencies, control flow and triggers.
Git folders, branches, PRs, Automation Bundles, CLI.
Spark UI, skew, shuffle, spilling, run history, Liquid Clustering.
UC managed/external grants, masking, row filters, ABAC.
Order of the course = order of the official exam guide. You build a coherent mental model, lesson by lesson.
Why study Databricks, the platform and Workspace, Free Edition for practice, notebooks + PySpark + Spark SQL as the first contact, high-level view of Delta Lake, Unity Catalog, Lakeflow Jobs and SDP.
OpeningCore components of the Data Intelligence Platform, Delta Lake as the operational base (ACID, time travel, schema enforcement), Unity Catalog as governance, compute types: all-purpose, job, serverless, SQL warehouse — when to use each.
Platform · 6%Batch, streaming and incremental loading. COPY INTO incremental from cloud object storage (ADLS / S3 / GCS) into UC-governed tables. When to use COPY INTO vs Auto Loader vs Connect.
Ingestion · 21%Auto Loader with schema enforcement and schema evolution. Directory listing vs file notification. Rescued data column. Ingestion of semi-structured / nested JSON into UC Delta.
Ingestion · 21%Lakeflow Connect: standard, fully-managed, partner connectors. Choosing between Auto Loader, Connect and partner connectors. JDBC/ODBC/REST in notebooks orchestrated by Jobs. Land data straight into UC tables or cloud storage.
Ingestion · 21%Read bronze tables with PySpark/SQL. Clean nulls, standardize types. Write new governed silver tables. Medallion architecture in practice.
Transformation · 22%Inner, left, broadcast, multi-key, cross, union, union all. Manipulate columns, rows, structures (add, drop, split, rename, filter, explode). Dedup. Aggregations: count, approximate count distinct, mean, summary.
Transformation · 22%Tuning knobs: shuffle.partitions, parallelism, executor/driver memory, broadcast threshold. Gold: materialized views, streaming tables, views, tables — when each one for BI in UC. Data quality checks for Silver and Gold.
Transformation · 22%DAG-based task graph, common tasks (notebook, SQL, dashboard, pipeline), task dependencies, repair and rerun.
Jobs · 16%Control flows (retries, branching, looping), conditional tasks, schedules (time-based, file arrival, table update), choosing between time-based and data-driven triggers.
Jobs · 16%Git Folders (formerly Repos), branches in the workspace UI, commit, push, PRs via Databricks Git integration.
CI/CD · 10%Declarative Automation Bundles (formerly DAB). Structure: databricks.yml, resources, targets, variables, overrides. Promote one codebase across dev/test/prod. Package Lakeflow Jobs, SDPs, other assets. CLI for validate, deploy, manage in CI/CD.
CI/CD · 10%Find performance bottlenecks from stage-level metrics in the Spark UI. Diagnose data skew, excessive shuffle and disk spilling. Read Min/Median/Max shuffle metrics. (Sample question Q1 in the official exam guide.)
Troubleshooting · 10%Lakeflow Jobs run history vs historical baseline, Jobs UI for status, DAG blockers, run times and failure rates. Diagnose cluster startup failures, library conflicts, OOM. Liquid Clustering and predictive optimization.
Troubleshooting · 10%Managed vs external tables in Unity Catalog. Create, modify, delete, convert between them. GRANT, REVOKE, DENY. Apply privileges to users, groups and service principals. UC security hierarchy.
Governance · 15%Column-level masking. Row-level security by user groups. ABAC policies in UC (NEW topic): central control of row filtering and column masking. Differences and when to use each.
Governance · 15%Spark Declarative Pipelines in modern Python: classes, tests, multi-file projects, production best practices, reusable pipeline example.
BonusReview by domain (7 sections). Full mock exam. Elimination strategy. Most common traps. How to manage 90 minutes for 45 questions. What to study in the last 3 days.
ClosingYes — notebooks, study guides, and mock exams are all available in English. Video lectures are being recorded in PT first; EN narration follows in waves. Athena (our AI tutor) answers in the language you ask her.
Comfortable SQL helps. Python at "I can write a function and use a library" level is plenty. If you're starting from zero, our PySpark Free course is the warm-up.
With 6-10h/week, most students reach exam readiness in 2-3 months. With less time, plan for 4-5 months. We adapt the path inside the course based on your starting level.
Databricks Academy is excellent reference material — comprehensive, official, free. Our course is exam-focused, opinionated, and built by an engineer working in production. We answer "why" and "what trade-offs", not just "how the feature works". Plus: 16 scenario-based mock exams, executable notebooks, AI tutor on WhatsApp.
Yes. Teams of 5+ get custom pricing and onboarding. Reach out on WhatsApp.
7 calendar days from purchase, no questions asked. WhatsApp us with the email used.
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