ExamSprintLab

DP-700 study plan: pass in 4 weeks

Updated October 9, 2026

Four weeks is realistic for DP-700 if you already write SQL and some PySpark and can study 8–10 hours a week. Spend the first week on the Fabric workspace, security and orchestration, the second on batch ingestion and transformation, the third on streaming and KQL, and the last on monitoring, error resolution and optimization plus practice questions. If you have never used Spark or SQL, double the timeline and start with those languages.

Before you start

  • Read the official skills outline once. It lists every objective in the three domains, and the version effective October 19, 2026 is the one to use.
  • Get a Fabric capacity you can experiment on: a trial or a small paid capacity. Pause or delete capacity when you finish a session so you do not pay for idle time.
  • Take the free Microsoft practice assessment cold. The score matters less than the per-domain breakdown.
  • Make sure you can read basic KQL (where, summarize, bin, project) before week 3. It is the language most data engineers have not used.

Week 1: implement and manage (30–35%, first half)

Goal: set up a workspace, secure it and orchestrate with the right tool.

  • Workspace settings: Spark pools and environments, domains, OneLake settings, Apache Airflow job settings.
  • Lifecycle: connect a workspace to Git, create a database project, build a deployment pipeline with dev, test and production stages and a deployment rule.
  • Security: workspace roles versus item permissions, then row-level, column-level, object-level and folder-level controls, dynamic data masking, sensitivity labels, endorsement, audit logs and OneLake security.
  • Orchestration: when to choose a Dataflow Gen2, a pipeline or a notebook; schedules versus event-based triggers; parameters and dynamic expressions.

Lab: a workspace with a lakehouse and a warehouse, a Git connection, a deployment pipeline, a row-level security predicate and a masked column, and a pipeline that runs a notebook with a parameter.

Week 2: batch ingestion and transformation (30–35%, first half)

Goal: load and reshape data reliably.

  • Loading patterns: full versus incremental loads with a watermark, preparing data for a dimensional model, slowly changing dimensions.
  • Ingestion: the Copy activity and Copy job, OneLake shortcuts, mirroring.
  • Transformation: PySpark and SQL for joins, grouping, aggregation, denormalizing, deduplication, missing values and late-arriving data; Delta MERGE.
  • Choosing the tool: Dataflows Gen2, notebooks, KQL or T-SQL.

Lab: an incremental load from a source table into a Delta table using a watermark parameter, a shortcut to external storage, and a MERGE that deduplicates.

Week 3: streaming and Real-Time Intelligence (30–35%, second half)

Goal: process a stream three ways and know when to pick each.

  • Eventstream: sources, transformations, destinations such as an Eventhouse or a lakehouse.
  • Spark structured streaming: reading a stream, watermarks, checkpoints, writing to Delta.
  • KQL: summarize with bin(), joins, update policies, native tables versus OneLake shortcuts, query acceleration.
  • Windowing: tumbling, hopping, sliding, session and snapshot windows.

Lab: an Eventstream feeding an Eventhouse, a KQL query that aggregates per five-minute window, and the same aggregation in Spark structured streaming.

Week 4: monitor and optimize (30–35%), then practice

Goal: diagnose failures and make things faster.

  • Monitoring: the Monitor hub, ingestion and transformation runs, semantic model refresh, alerts.
  • Errors: pipelines, Dataflows Gen2, notebooks, Eventhouse, Eventstream, T-SQL and shortcuts. Deliberately break each one in your lab and read the error.
  • Optimization: lakehouse table maintenance (OPTIMIZE, V-Order, VACUUM), warehouse statistics, pipeline parallelism, Spark configuration, Eventhouse caching and retention.

Finish with the 20-question practice test, the Microsoft practice assessment again, and a pass over the cheat sheet.

If you are already experienced

If you work with Fabric daily, cut the plan to two weeks. Take the practice assessment on day one and spend time only on weak domains. Experienced engineers typically have gaps in three places: Real-Time Intelligence and KQL, the security layers (OneLake security and dynamic data masking), and deployment pipelines.

How to know you are ready

  • You score 80% or more on a realistic practice test in every domain, not just on average.
  • You can say, for any scenario, why a pipeline, notebook or Dataflow Gen2 is the right tool.
  • You can write a windowed aggregation in both KQL and PySpark without looking it up.
  • You can find the cause of a failed run in the Monitor hub without guessing.

For where to find material, see study resources; for the mechanics, exam format.