DP-600 study plan
DP-600 · Give each week a clear focus.
8 weeks · About 6 hours per week
Plan, implement, and manage a solution for data analytics & Prepare and serve data
Provision a Fabric capacity (trial or paid) and create a workspace bound to that capacity with Git integration enabled.
Create a lakehouse and a warehouse side by side, then write a decision rule for when each store is the right target.
Assign workspace roles (Admin, Member, Contributor, Viewer) for a three-person team and verify effective permissions per role.
Map five given workload scenarios to the correct Fabric component and license tier (capacity SKU vs. per-user).
Your checkpoint
A working workspace containing one lakehouse and one warehouse, documented role assignments, and a one-page scenario-to-component decision matrix covering five cases.
Prepare and serve data
Build a Data pipeline with a Copy activity that lands three CSV sources into the lakehouse Files area.
Parameterize the pipeline and implement a watermark-based incremental load for one source table.
Create a Dataflow Gen2 that applies cleansing steps and writes results into a warehouse table.
Configure a OneLake shortcut to an external ADLS Gen2 container and confirm the data is queryable without copying.
Schedule the pipeline and confirm at least two successful scheduled runs in run history.
Your checkpoint
An orchestrated pipeline that incrementally loads three source tables with a watermark pattern, plus run history showing two green scheduled runs.
Prepare and serve data
Write a PySpark notebook that promotes bronze data to silver with deduplication, schema enforcement, and type casting.
Implement a T-SQL stored procedure in the warehouse that builds a gold aggregation table.
Apply Delta maintenance: run OPTIMIZE with Z-ORDER on a high-cardinality column and record query timing before and after.
Chain the notebook and stored procedure inside one pipeline using activity dependencies and failure paths.
Your checkpoint
A bronze/silver/gold medallion pipeline running end to end, with a before/after timing table proving the Delta optimization improved a representative query.
Prepare and serve data & Implement and manage semantic models
Create the default semantic model over the gold Delta table and mark a dedicated date table.
Reshape the model into a star schema: define fact grain, split dimensions, and hide surrogate keys from report view.
Select a storage mode (Direct Lake, Import, or DirectQuery) per table and justify each choice in writing.
Add data quality checks that fail the pipeline when null or duplicate key thresholds are breached.
Your checkpoint
A report-ready semantic model over the gold layer with a marked date table, at least four correct relationships, and a written storage-mode justification per table.
Implement and manage semantic models
Author 12 or more DAX measures covering base aggregation, year-to-date, and year-over-year using the marked date table.
Build a calculation group that switches reporting currency or time period without duplicating measures.
Implement two RLS roles, one with a dynamic filter based on the signed-in user, and validate each with the View as role feature.
Configure scheduled refresh and verify the refresh history shows success with expected row counts.
Your checkpoint
A semantic model with 12+ tested measures, one working calculation group, and two RLS roles validated through View as role with filtered-result evidence.
Implement and manage semantic models & Explore and analyze data
Write DAX queries in DAX query view that reproduce the key number behind each report visual and reconcile any mismatch.
Optimize at least two slow measures by reducing iterator scope or removing unnecessary context transitions.
Build a three-page Power BI report on the model with cross-filtering, drill-through, and a slicer synced across pages.
Query the lakehouse through the SQL analytics endpoint to confirm the report totals match the source aggregates.
Your checkpoint
A three-page report plus a DAX query view script set that reproduces every headline metric, with a short reconciliation note resolving all discrepancies.
Explore and analyze data & Plan, implement, and manage a solution for data analytics
Configure a deployment pipeline that promotes the workspace across development, test, and production stages with a documented rollback step.
Set up Git integration on a feature branch, commit changes, and merge through a pull request to the main branch.
Review capacity consumption in the Capacity Metrics app and identify the top three consuming items.
Apply governance controls: sensitivity labels, endorsement, and a domain assignment for the production workspace.
Your checkpoint
A deployment pipeline promoting artifacts across three stages, a merged pull request, and a written monitoring and governance summary naming the top three capacity consumers and the rollback procedure.
Plan, implement, and manage a solution for data analytics & Prepare and serve data & Implement and manage semantic models & Explore and analyze data
Complete a full-length timed practice assessment and score at least 80 percent overall and 70 percent in every domain.
Rebuild a minimal end-to-end solution (ingest, transform, model, report) in 90 minutes without notes.
Re-drill every missed objective and rewrite condensed notes for each weak area.
Walk the objective checklist for all four domains and close every remaining gap.
Your checkpoint
A timed practice score report at or above the 80/70 thresholds, a 90-minute unassisted rebuild completed, and a remediation list with zero open items.
Milestones
Week 2: Incremental pipeline loads three sources on schedule and run history shows two consecutive successes.
Week 4: Data flows from source file to a report-ready star-schema semantic model without manual steps, with storage modes justified in writing.
Week 6: Report totals reconciled against SQL analytics endpoint output, and at least two measures measurably faster after optimization.
Week 8: Timed practice assessment scores at least 80 percent overall and 70 percent or higher in each of the four domains.