Fabric DP-700 study plan

Provision a Fabric workspace and build a lakehouse, then load 2 distinct source types (a CSV file and an Azure SQL table) into the bronze layer

Create a Data pipeline with a Copy activity plus a parameterized ForEach loop, and configure a scheduled trigger

Build a Dataflow Gen2 that ingests and lightly cleanses a semi-structured source, then lands output in the lakehouse

Explain when to choose pipeline vs Dataflow Gen2 vs notebook vs Copy job for a given ingestion scenario, and write the decision down

Transform bronze data into silver using a Spark notebook with PySpark DataFrame operations and Delta writes

Implement a slowly changing dimension type 2 pattern (or upsert/merge) and verify row history is preserved

Model gold-layer tables with a star schema and validate joins against expected row counts

Create OneLake shortcuts to an external ADLS container and confirm the data is queryable without copying

Configure a shortcut or mirroring path for a second source and document the trade-offs

Design and implement workspace roles, item permissions, and row-level/column-level security on a gold table

Set up a deployment pipeline or Git integration and promote content from dev to test with parameterized connections

Configure sensitivity labels and endorse a certified item; document the governance choices

Query lakehouse data through the SQL analytics endpoint and compare results with a Spark query on the same table

Wire a Real-Time/KQL database or Eventstream end-to-end and query it with KQL to confirm live ingestion

Add orchestration dependencies, retry logic, and failure-path handling to an existing pipeline

Enable basic monitoring: review the Monitoring hub and capture a failed-run investigation end-to-end

Document the end-to-end solution architecture with data flow, compute choices, and security boundaries

Interpret the Fabric Capacity Metrics app to identify the top throttling/smoothing consumers over a 7-day window

Diagnose a slow Spark notebook using the Spark UI (skew, shuffle, spill) and apply one fix

Diagnose a slow warehouse/SQL query using the query insights view and rewrite it for improvement

Configure an alert or Data Activator rule that fires on a pipeline failure or threshold breach, and prove it fires

Apply Delta optimization techniques (file compaction, partitioning strategy, V-Order/target file sizing) and measure the effect on query duration

Right-size compute: compare capacity SKU behavior, choose between scaling up vs pausing/resuming, and justify the cost trade-off

Automate monitoring by scheduling a notebook or pipeline that logs run durations and failures to a table

Build a small dashboard or Power BI report over the monitoring log and add 2 alert thresholds

Rebuild one weak-area component from scratch without notes to prove retention

Complete 40+ practice items across all three domains and log every miss with the reason (knowledge gap vs misread vs unknown feature)

Re-study only the logged gaps and re-test yourself on those items after 48 hours

Re-run the full solution end-to-end after making one schema change and confirm nothing breaks

Take 2 full-length timed practice assessments and score them against the 70% target per domain

Review every remaining flagged topic and convert each into a 5-bullet summary card

Walk through the official exam skill outline item-by-item and mark each as confident or shaky

Do a final dry run of the end-to-end solution: ingest, transform, secure, monitor, and tune in one sitting