DP-700 Exam Flashcards

Q: What do DP-700 flashcards help you study?

These cards reinforce Microsoft Fabric data-engineering concepts across lakehouses, warehouses, OneLake, pipelines, Dataflow Gen2, notebooks, Spark, KQL, security, governance, deployment, and optimization.

Q: Are flashcards enough for DP-700 preparation?

Flashcards are useful for active recall, but DP-700 also requires hands-on Fabric practice and scenario-based review across all three official skill areas.

Q: How should I use DP-700 flashcards?

Use them after hands-on practice to check whether you can explain what each Fabric feature does, when to use it, and what operational risk it controls.

Question 1

Fabric workspace

Accepted Answer

A workspace is the collaboration and security boundary where Fabric items such as lakehouses, warehouses, notebooks, pipelines, and semantic models are created and managed.

Question 2

Workspace role

Accepted Answer

A workspace role controls what a user can do with items in that workspace. Use the least privileged role that supports the user's job task.

Question 3

Deployment pipeline

Accepted Answer

A deployment pipeline promotes Fabric item definitions through lifecycle stages such as development, test, and production. It moves metadata and configuration, while environment-specific data and credentials still need separate handling.

Question 4

Deployment rule

Accepted Answer

A deployment rule changes stage-specific settings, such as connections or parameters, so promoted items point to the correct environment after deployment.

Question 5

Git integration

Accepted Answer

Git integration lets teams version supported Fabric workspace items, review changes, and use branch-based development practices before deployment.

Question 6

OneLake

Accepted Answer

OneLake is Fabric's tenant-wide data lake. It gives Fabric items a shared storage foundation so data can be reused without unnecessary copies.

Question 7

Lakehouse

Accepted Answer

A lakehouse stores files and Delta tables for analytics. It is the common Fabric choice when engineers need open file storage plus table-oriented processing.

Question 8

Files area versus Tables area

Accepted Answer

The Files area holds raw or semi-structured files, while the Tables area exposes managed Delta tables for query and analytics workloads.

Question 9

Shortcut

Accepted Answer

A shortcut references data stored in another location without copying it into the lakehouse. Use shortcuts to simplify access while respecting the source location's security and performance constraints.

Question 10

Delta table

Accepted Answer

A Delta table combines data files with a transaction log. The log supports reliable table operations, schema handling, and consistent reads for analytics workloads.

Question 11

Fabric warehouse

Accepted Answer

A Fabric warehouse is a SQL analytics store for relational modeling, T-SQL development, and serving curated data to reporting and downstream consumers.

Question 12

SQL analytics endpoint

Accepted Answer

The SQL analytics endpoint lets users query lakehouse tables with SQL. It is useful for read-oriented exploration and reporting over lakehouse data.

Question 13

Fact table grain

Accepted Answer

The grain defines what one fact row represents. Clear grain prevents double counting and keeps measures consistent across dimensions.

Question 14

Dimension table

Accepted Answer

A dimension table stores descriptive attributes such as customer, product, date, or region. Dimensions make facts easier to filter, group, and understand.

Question 15

Stored procedure in orchestration

Accepted Answer

A stored procedure can package repeatable SQL transformation logic for a pipeline or warehouse workflow. It is useful when the logic belongs close to relational tables.

Question 16

Full load

Accepted Answer

A full load replaces or reloads the whole target dataset. It is simple, but it can waste time and capacity when only a small portion of the source changes.

Question 17

Incremental load

Accepted Answer

An incremental load moves only new or changed data since the last successful run. It reduces processing work when the source provides a reliable change indicator.

Question 18

Watermark

Accepted Answer

A watermark records the point up to which source data has already been processed. Pipelines and transformations use it to resume without reloading everything.

Question 19

Late-arriving data

Accepted Answer

Late-arriving data reaches the pipeline after related records or reporting windows have already been processed. A resilient design loads it safely and updates affected tables instead of silently dropping it.

Question 20

Mirroring

Accepted Answer

Mirroring brings supported operational source data into Fabric for analytics with less custom ingestion code. It is best when the goal is ongoing replication rather than one-time file movement.

Question 21

Fabric pipeline

Accepted Answer

A pipeline orchestrates data movement and processing activities. It coordinates tasks such as copy operations, notebooks, dataflows, stored procedures, and dependency flow.

Question 22

Copy activity

Accepted Answer

Copy activity moves data between supported sources and destinations. It is a practical choice when the main task is data movement rather than complex transformation.

Question 23

Pipeline parameter

Accepted Answer

A pipeline parameter supplies values at run time, such as dates, folder paths, or environment names. Parameters make one pipeline reusable across runs and stages.

Question 24

Activity dependency

Accepted Answer

Activity dependencies define the order and conditions for pipeline execution. They help separate success paths, failure handling, and cleanup work.

Question 25

Notebook activity

Accepted Answer

A notebook activity runs Spark code as part of an orchestrated workflow. Use it when transformations need PySpark, Scala, or notebook-based engineering logic.

Question 26

Fabric notebook

Accepted Answer

A Fabric notebook is an interactive development item for Spark code, SQL, markdown, and exploratory engineering. It can be run manually or orchestrated in a pipeline.

Question 27

Spark session

Accepted Answer

A Spark session is the execution context for notebook commands. Session startup, configuration, and reuse affect both developer experience and workload cost.

Question 28

PySpark DataFrame

Accepted Answer

A PySpark DataFrame represents distributed tabular data. It is used for scalable transformations that are too large or complex for simple row-by-row processing.

Question 29

Partitioning in Spark

Accepted Answer

Partitioning controls how data is divided for distributed processing. Good partition choices improve parallelism, while poor choices can cause skew or unnecessary shuffling.

Question 30

Checkpoint in streaming

Accepted Answer

A checkpoint stores streaming progress and state. It lets a streaming job resume after interruption without losing its place or blindly starting over.

Question 31

Dataflow Gen2

Accepted Answer

Dataflow Gen2 uses Power Query to prepare and transform data in Fabric. It is a strong fit for low-code shaping, cleansing, and loading tasks.

Question 32

Power Query step order

Accepted Answer

Power Query applies steps in sequence. Changing an early step can affect later filters, joins, data types, and destination mapping.

Question 33

Append versus merge

Accepted Answer

Append stacks rows from compatible tables. Merge joins related tables by key to add columns from another dataset.

Question 34

Dataflow destination

Accepted Answer

A dataflow destination defines where transformed results are loaded, such as a lakehouse table or warehouse table. The destination choice should match how the data will be queried and governed.

Question 35

Schema drift in low-code ingestion

Accepted Answer

Schema drift happens when source columns or types change unexpectedly. Dataflows should handle expected changes deliberately so refresh failures do not hide data-quality problems.

Question 36

Eventstream

Accepted Answer

An eventstream ingests and routes event data in Fabric. It is used when records arrive continuously and need real-time processing or storage.

Question 37

Eventhouse

Accepted Answer

An eventhouse stores and serves high-volume event and telemetry data for KQL querying. It is suited to time-oriented operational analytics.

Question 38

KQL

Accepted Answer

KQL is a query language for exploring event, log, and telemetry data. DP-700 candidates should recognize when KQL is the right tool instead of T-SQL or PySpark.

Question 39

Windowed aggregation

Accepted Answer

A windowed aggregation groups streaming events by time window. It supports metrics such as counts, averages, and anomaly signals over recent activity.

Question 40

Streaming destination

Accepted Answer

A streaming destination is where processed events land, such as an eventhouse, lakehouse, or other supported Fabric target. Choose it based on query pattern, latency needs, and retention requirements.

Question 41

Least privilege

Accepted Answer

Least privilege means granting only the access needed for a task. In Fabric, this applies across workspace roles, item permissions, data access, and source credentials.

Question 42

OneLake security

Accepted Answer

OneLake security controls data access within the lake. It matters because seeing a Fabric item does not always mean a user should read every underlying file or table.

Question 43

Sensitivity label

Accepted Answer

A sensitivity label identifies protected or regulated data. Labels help users and governance tools understand handling requirements for Fabric items.

Question 44

Endorsement

Accepted Answer

Endorsement marks trusted content for reuse. Promoted content signals owner confidence, while certified content usually reflects stronger organizational review.

Question 45

Fabric domain

Accepted Answer

A domain organizes data and analytics content by business area. It improves discovery and ownership clarity without automatically granting access.

Question 46

Monitoring hub

Accepted Answer

The monitoring hub helps track runs, refreshes, and workload activity across Fabric items. Use it to find failures, long-running jobs, and operational trends.

Question 47

Pipeline run history

Accepted Answer

Pipeline run history shows activity status, timing, and errors. It is the first place to check when an orchestration fails or misses its expected completion window.

Question 48

Query tuning

Accepted Answer

Query tuning reduces unnecessary scans, joins, shuffles, and transformations. In Fabric, the right fix depends on whether the workload is SQL, Spark, KQL, or semantic-model driven.

Question 49

Small file problem

Accepted Answer

Too many small files can slow lakehouse reads because engines spend extra work opening and planning files. Compaction and better write patterns can improve scan efficiency.

Question 50

Capacity-aware optimization

Accepted Answer

Fabric workloads share capacity resources. Efficient schedules, right-sized compute, and workload monitoring help prevent one engineering job from disrupting other analytics users.

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