Examworthyexamworthy.comMicrosoft Fabric Data Engineer (DP-700) cheat sheet
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At a glance
Format: Multiple choice and multiple response, at a Pearson VUE testing center or online proctored
Domain weight map
Heaviest first - spend your time hereHow this exam thinks
DP-700 rewards the answer that fits the Microsoft Fabric workload, the right store and engine for the read and write pattern, the most maintainable low-code-versus-code choice, and the incremental, idempotent load, not the most powerful or hand-rolled option.
Spot the trap
Tempting wrong answers, and why they failCommon misconception
That moving a Fabric workspace to a new capacity means exporting every item and rebuilding it in a fresh workspace. Reassigning the capacity in workspace settings moves everything in place instead.
Implement and Manage an Analytics Solution
Common misconception
That truncating and reloading an append-only source nightly is low overhead. It rereads the whole source every run, the exact cost a watermark avoids.
Ingest and Transform Data
Common misconception
That raising a Copy activity's timeout fixes a transient connection reset. Timeout only limits how long an attempt may run; it does not re-establish a dropped connection, so retry count and interval are needed instead.
Monitor and Optimize an Analytics Solution
Common misconception
That editing directly in a shared workspace and relying on undo is enough to isolate a risky change. Only branching out to a separate workspace gives real isolation.
Implement and Manage an Analytics Solution
Common misconception
That overwriting the whole table with a daily delta keeps it current. The delta lacks unchanged customers, so overwriting deletes every customer absent from that day's extract.
Ingest and Transform Data
Common misconception
That adding a retry count fixes a copy cut off by the activity timeout. Each retry re-runs the same multi-hour job from scratch and hits the same limit; raise the timeout instead.
Monitor and Optimize an Analytics Solution
Common misconception
That adding someone as a workspace Viewer is already the minimal way to share one report. Viewer exposes every item, while item-level sharing exposes only that report.
Implement and Manage an Analytics Solution
Common misconception
That a source business code makes a fine dimension primary key. When codes are reused or reassigned across regions, they cannot uniquely identify one historical row.
Ingest and Transform Data
Key terms
Exam-day rules
- Read the scenario for the read and write pattern and the stated constraint first, then match it to the Microsoft Fabric workload. Distractors are written to sound reasonable; the right answer is the store, engine, and load pattern that fit, not the most powerful tool on offer.
- When a question stresses low operational overhead or incremental only, choose the incremental and idempotent pattern. A high-water-mark watermark beats a truncate-and-reload for an append-only source with a monotonic column.
- When a question stresses maintainability, choose the lowest-code option that still meets the requirement: a Dataflows Gen2 transform over a hand-written notebook when the logic is simple, and a Data Factory pipeline to sequence and parameterise the rest.
- Match the security control to what must be hidden: object-level security to remove a table from a role, row-level security to filter rows, column-level security or dynamic data masking to obscure values. They are not interchangeable.
- Remember the store boundaries: the Lakehouse SQL analytics endpoint reads but does not write, so route UPDATE and DELETE through Spark or a Warehouse, and reach for an Eventhouse and a KQL database for high-volume streaming telemetry.
Revision schedule
- Day 1Map the blueprint and book a date
- Week 1Build the Fabric foundations hands-on
- Weeks 2 to 3Go deep on ingest and transform
- Week 4Master security and orchestration
- Week 5Drill monitoring and optimisation