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Fabric DP-700 Cheat Sheet

April 24, 2026

Fabric DP-700 cheat sheet for key facts, traps, service mappings, and final review.

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Use this cheat sheet for Microsoft Certified: Fabric Data Engineer Associate (DP-700) when you need fast recall across Fabric data engineering decisions. The exam lane is about moving governed data from source to useful analytics, then proving the solution is secure, reliable, monitored, and optimized.

Read every Fabric scenario in this order

  1. Identify the artifact: lakehouse, warehouse, pipeline, dataflow, notebook, eventstream, KQL database, semantic model, or workspace setting.
  2. Identify the operation: ingest, transform, secure, orchestrate, monitor, optimize, or troubleshoot.
  3. Choose the engine or tool that fits latency, volume, skill set, and transformation complexity.
  4. Preserve governance: workspace roles, item permissions, lineage, sensitivity, endorsement, and data boundary.
  5. Prove the result with quality checks, refresh history, monitoring, and performance evidence.

DP-700 answer sequence

Use this when the stem mixes Fabric artifacts, governance, refresh, monitoring, and performance.

    flowchart TD
	  S["Scenario"] --> A["Identify the Fabric artifact"]
	  A --> O["Identify the operation"]
	  O --> T["Choose the engine or tool that fits"]
	  T --> G["Preserve governance and permissions"]
	  G --> V["Verify with refresh history, lineage, or performance evidence"]

Fabric artifact chooser

Requirement Strong starting point
store raw and curated files/tables in an analytics lake pattern lakehouse and OneLake
SQL-first enterprise warehousing warehouse
visual/low-code transformation Dataflow Gen2 when transformation complexity fits
code-first Spark transformation notebook or Spark job pattern
orchestration across steps data pipeline with dependencies, parameters, and monitoring
real-time or log-style analysis eventstream or KQL-oriented path when the source is streaming or operational telemetry
BI consumption semantic model and downstream reporting path, while DP-700 still focuses on engineering inputs

Ingestion and transformation map

If the stem emphasizes… Prefer thinking about…
repeatable scheduled load pipeline orchestration, credentials, dependencies, failure path, and monitoring
complex transformations notebook/Spark or SQL depending on data shape and team skills
source system abstraction shortcuts, mirroring, or connector pattern where supported and appropriate
low-code data prep Dataflow Gen2, refresh behavior, and ownership
streaming or near-real-time data event ingestion, windowing/latency needs, and KQL or streaming pipeline fit
medallion architecture raw/bronze, cleansed/silver, serving/gold separation with validation and lineage

OneLake, lakehouse, and warehouse rules

Topic Exam instinct
OneLake Treat as the shared storage foundation, but do not ignore workspace/item governance.
Shortcut Useful for referencing data without copying it, but permissions and source reliability still matter.
Lakehouse Best when files, Delta tables, Spark, and open analytics patterns are central.
Warehouse Best when relational SQL modeling, constraints, and SQL analytics are central.
Delta table Think ACID-style reliability, schema evolution controls, and table optimization.
Semantic model Consumption layer needs clean grain, relationships, measures, refresh, and security.

SQL, PySpark, and KQL chooser

Tool/language Use when… Watch for…
SQL relational transformations, joins, aggregations, warehouse logic, and analyst-friendly review poor query shape, missing filters, wrong grain, or expensive scans
PySpark large-scale distributed transformations, complex data prep, notebooks, and code reuse cluster/session cost, shuffle pressure, schema drift, and maintainability
KQL telemetry, event, log, or time-series analysis wrong time filter, wrong aggregation, and treating KQL like normal transactional SQL
Dataflow Gen2 low-code cleaning and shaping refresh dependencies, ownership, and transformation limits
Pipeline orchestration rather than transformation logic itself missing dependency, parameter, retry, alert, or failure path

Security and governance checklist

Control What the exam rewards
workspace roles assign broad workspace responsibilities intentionally; do not use them as a substitute for item-level thought
item permissions grant access to specific artifacts when that is the real boundary
sensitivity labels classify and protect data for downstream use and compliance
lineage prove where data came from and what consumed it
endorsement signal which data products are promoted or certified for trusted use
credentials secure connection credentials and avoid personal one-off ownership for production paths
tenant/workspace settings use platform-level governance when the requirement is organization-wide

Monitoring and troubleshooting

Symptom Check first
pipeline failed activity error, dependency, credential, parameter, source availability, and retry policy
refresh is slow source latency, transformation step, query shape, data volume, partitioning, and capacity pressure
data looks wrong schema change, join key, filter, late-arriving data, duplicate load, and validation rule
user cannot access data workspace role, item permission, source permission, sensitivity policy, and sharing path
query is expensive partitioning, file/table layout, predicate pushdown, statistics, and unnecessary columns
report shows stale results refresh schedule, semantic model dependency, pipeline completion, and cache behavior

Optimization rules

Goal Better first move
reduce query time inspect query plan/shape, filters, columns, table layout, and partitioning before scaling
reduce refresh failures add dependencies, validation checks, retries, alerts, and clear ownership
reduce storage duplication consider shortcuts or shared curated tables when governance permits
improve trust add data quality checks, lineage, endorsement, and documentation
reduce capacity pressure schedule heavy work, tune transformations, avoid unnecessary scans, and monitor utilization

Common traps

Trap Better instinct
Treating Fabric as one product button Name the artifact and engine the scenario actually needs.
Skipping medallion separation Keep raw, cleansed, and serving layers distinct when lifecycle matters.
Workspace access equals data access Check item permissions, source permissions, sharing, and sensitivity controls.
Pipeline equals transformation A pipeline orchestrates work; notebooks, SQL, dataflows, or other steps transform data.
Performance equals more capacity Measure source, query, layout, transformation, and scheduling bottlenecks first.

Final 15-minute review

If the stem says… Start here
ingest from source connector, credential, schedule, dependency, and failure handling
transform data SQL vs PySpark vs Dataflow Gen2 vs KQL based on shape and complexity
lakehouse or warehouse open file/table analytics vs SQL-first relational warehouse
govern or share workspace, item, sensitivity, lineage, endorsement, and source access
monitor or troubleshoot run history, error, dependency, metric, owner, and alert
optimize query shape, table layout, partitioning, refresh timing, and capacity evidence

Practice fit

Use IT Mastery for the exact product route, practice status, spaced review when available, and close-answer explanation practice as coverage expands.

Open the exact IT Mastery route here: DP-700 on MasteryExamPrep.

One-line decision rule

DP-700 answers should preserve the data engineering chain: ingest reliably, transform with the right engine, govern access, validate quality, monitor operations, and optimize from evidence.

Revised on Sunday, May 10, 2026
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