DP-600 Exam Flashcards

Q: What does DP-600 certify?

DP-600 is the Microsoft Certified: Fabric Analytics Engineer Associate exam. It validates the ability to maintain and secure a Fabric analytics solution, prepare data across lakehouses, warehouses, and eventhouses, and design enterprise semantic models.

Q: What is the DP-600 passing score?

DP-600 uses a scaled score out of 1000, and 700 is required to pass. Microsoft does not publish public pass-rate percentages for the exam.

Q: How is the DP-600 exam weighted?

The current outline has three domains: Maintain a data analytics solution (25-30%), Prepare data (45-50%), and Implement and manage semantic models (25-30%). Data preparation carries the largest weight.

Q: What happens if I fail DP-600?

Microsoft lets you retake after 24 hours following a first failure. After a second failure, a 14-day wait applies between further retakes, with a maximum of five attempts in a 12-month period.

Q: Are flashcards enough to pass DP-600?

Flashcards build active recall of Fabric terminology and decision rules, but DP-600 also requires hands-on Fabric practice and scenario-based review across all three domains, especially data preparation.

Q: How should I use these DP-600 flashcards?

Use them after hands-on Fabric work to check whether you can explain what each feature does, when to choose it over a confusable alternative, and what governance or performance consequence it controls.

Question 1

What is a Fabric workspace and what boundary does it define?

Accepted Answer

A workspace is the collaboration container and primary security boundary for Fabric items such as lakehouses, warehouses, notebooks, pipelines, and semantic models. Workspace roles set baseline access; narrower item permissions can refine it.

Question 2

Order the four Fabric workspace roles from most to least privileged.

Accepted Answer

Admin (full control including settings and access), then Member (manage users and content), then Contributor (create and edit content), then Viewer (read-only). Grant the least privileged role that still supports the user's task.

Question 3

Workspace role vs single-item access — which should you use first when sharing?

Accepted Answer

Prefer single-item access when only one asset needs to be shared; it limits exposure. A workspace role grants access to every item in the workspace, so it is broader and riskier for narrow sharing needs.

Question 4

What is a Fabric domain (governance), and does it grant data access?

Accepted Answer

A domain groups workspaces and content by business area to improve discovery and ownership clarity. It organizes content but does not by itself grant access to data — permissions are still controlled separately.

Question 5

RLS vs CLS vs OLS — what does each restrict?

Accepted Answer

RLS (row-level security) filters which rows a user sees. CLS (column-level security) hides specific columns. OLS (object-level security) hides entire tables or objects so they do not appear in the model at all.

Question 6

Does a sensitivity label control who can access data?

Accepted Answer

No. A sensitivity label classifies and can protect data (encryption, handling guidance, downstream inheritance), but it is not an access-control mechanism. Use workspace roles, item permissions, and RLS/CLS/OLS to control who can read data.

Question 7

What is OneLake file/folder security used for?

Accepted Answer

It restricts access to specific paths (files or folders) in OneLake. It matters because being able to see a Fabric item does not automatically mean a user should read every underlying file or table behind it.

Question 8

List the access layers DP-600 expects you to reason about, broad to fine.

Accepted Answer

Workspace role, then item permission, then file/folder security, then row-level (RLS), column-level (CLS), and object-level (OLS) security. Combine the broad and fine layers to enforce least privilege.

Question 9

Promoted vs Certified endorsement — what is the difference?

Accepted Answer

Promoted marks content the owner recommends for reuse. Certified is a stronger signal: the content passed a formal organizational review and is treated as an authoritative enterprise source. Endorsement signals trust; it does not grant access.

Question 10

What does lineage / impact (dependency) analysis show, and when do you use it?

Accepted Answer

It maps how items depend on each other across the workspace (sources, models, reports). Review impact analysis before changing a schema or deleting an asset so you do not silently break downstream reports or models.

Question 11

Endorsement vs deployment pipeline — what does each one do?

Accepted Answer

Endorsement marks content as trusted (Promoted/Certified) for discovery and reuse. A deployment pipeline promotes item definitions through dev → test → production stages. Trust signaling and lifecycle promotion are separate concerns.

Question 12

What is a PBIP project and why does it matter for the analytics lifecycle?

Accepted Answer

PBIP (Power BI Project) saves a report and its semantic model as plain, source-control-friendly files. It enables Git versioning, code review, and CI/CD instead of opaque single-file PBIX binaries.

Question 13

What does Fabric Git integration give a team?

Accepted Answer

It connects a workspace to a Git repository so supported items can be versioned, branched, reviewed, committed, and synced. It brings branch-based development discipline before changes reach test or production.

Question 14

A deployment pipeline promotes items, but connections still point to dev. What fixes this?

Accepted Answer

A deployment rule. Deployment rules override stage-specific settings (data source connections, parameters) so a promoted item points to the correct test or production environment instead of the dev source.

Question 15

What is the XMLA endpoint used for in Fabric semantic models?

Accepted Answer

The XMLA endpoint allows external tools and scripted/automated processes to read and write semantic model metadata — used for advanced model management, scripted deployment, and partition or refresh automation.

Question 16

What is OneLake?

Accepted Answer

OneLake is Fabric's single, tenant-wide data lake. Every Fabric item stores data in OneLake, giving the tenant one logical lake so data can be reused across items without creating unnecessary copies.

Question 17

What is a OneLake shortcut, and how does it differ from copying data?

Accepted Answer

A shortcut is a virtual reference to data in another location (another lakehouse, OneLake, or external storage) without physically copying it. The data stays at the source and inherits source location security and performance characteristics.

Question 18

Shortcut vs Mirroring — when do you choose each?

Accepted Answer

Use a shortcut to virtually reference existing data in place with no copy. Use mirroring to continuously replicate a supported operational database into OneLake for analytics. Shortcut = no copy, virtual; mirroring = managed near-real-time replica.

Question 19

OneLake catalog vs Real-Time hub — what does each help you discover?

Accepted Answer

The OneLake catalog helps you discover and govern stored data items (lakehouses, warehouses, semantic models). The Real-Time hub helps you discover and connect to streaming sources and event data. One is for stored data, one for streams.

Question 20

When do you need an on-premises data gateway in Fabric?

Accepted Answer

When Fabric must reach a data source that is not directly cloud-accessible, such as an on-premises database. The gateway provides the secure bridge for those connections; cloud-to-cloud sources usually do not need it.

Question 21

Lakehouse vs Warehouse — what workload does each fit?

Accepted Answer

A lakehouse stores files plus Delta tables and suits open-file, Spark, and notebook-centric analytics. A warehouse is a relational T-SQL analytics store for SQL development and serving curated relational data. Pick by workload, not preference.

Question 22

When do you choose an Eventhouse instead of a warehouse?

Accepted Answer

Choose an eventhouse for high-volume, time-series telemetry and event/log data queried with KQL. A warehouse handles structured relational analytics with T-SQL. Streaming/observability favors the eventhouse; relational reporting favors the warehouse.

Question 23

What is the SQL analytics endpoint of a lakehouse, and what can it NOT do?

Accepted Answer

It lets you query lakehouse Delta tables with T-SQL for read-oriented exploration and reporting. It is read-only over lakehouse tables — to write with T-SQL (INSERT/UPDATE/stored procedures over managed tables) you use a warehouse.

Question 24

Files area vs Tables area in a lakehouse — what goes where?

Accepted Answer

The Files area holds raw or semi-structured files (landing/bronze data). The Tables area exposes managed Delta tables for query and analytics. Promote cleaned data from Files into Tables for reliable downstream consumption.

Question 25

Why is a Delta table preferred over loose files for analytics tables?

Accepted Answer

A Delta table pairs data files with a transaction log, giving ACID-style reliability, schema enforcement, time travel, and consistent reads. Loose files lack the transaction log, so concurrent reads/writes can be inconsistent.

Question 26

Dataflow Gen2 vs a Notebook — when do you pick each for transformation?

Accepted Answer

Use Dataflow Gen2 (Power Query, low-code) for visual cleansing, shaping, and loading. Use a notebook when transformations need code (PySpark/Scala), complex logic, or fine-grained Spark control. Code-heavy work needs the notebook.

Question 27

Dataflow Gen2 vs a Pipeline — what is each one's job?

Accepted Answer

A Dataflow Gen2 transforms and loads data. A pipeline orchestrates activities (copy, dataflow, notebook, stored procedure) with dependencies and scheduling. A dataflow shapes data; a pipeline coordinates the overall workflow.

Question 28

In Power Query, why does step order matter?

Accepted Answer

Power Query applies applied steps sequentially. Changing or inserting an early step (a filter, type change, or rename) can ripple into later joins, filters, and the destination mapping, so edits must respect step dependencies.

Question 29

Full load vs Incremental load — what is the trade-off?

Accepted Answer

A full load reloads the whole dataset: simple but wastes time/capacity when little changed. An incremental load moves only new or changed rows since the last run: efficient, but it requires a reliable change indicator (watermark, timestamp, or CDC).

Question 30

Append vs Merge when combining query data — what does each produce?

Accepted Answer

Append stacks rows from compatible tables (more rows, same columns). Merge joins tables on a key to bring columns from another table (more columns). Need more rows → append; need more columns → merge.

Question 31

Why is a star schema the preferred analytical model shape?

Accepted Answer

A star schema separates a central fact table (measures/events) from surrounding dimension tables (descriptive context). It simplifies relationships, improves query and filter performance, and avoids ambiguous paths common in snowflaked or normalized designs.

Question 32

What is the grain of a fact table and why define it explicitly?

Accepted Answer

The grain is what one fact row represents (for example, one order line per day). Defining it prevents double counting and keeps measures consistent across every dimension you slice by.

Question 33

Surrogate key vs business key — why do warehouses add a surrogate key?

Accepted Answer

A business (natural) key comes from the source system. A surrogate key is a model-generated identifier that stays stable, enables slowly changing dimension history, and decouples the model from source-key changes or reuse.

Question 34

What is schema drift and why must it be handled deliberately?

Accepted Answer

Schema drift is an unexpected change in source columns or data types. If unhandled, it causes silent data-quality issues or hidden refresh failures, so transformations should detect and react to expected changes intentionally.

Question 35

How should late-arriving data be handled in a robust pipeline?

Accepted Answer

Late-arriving data reaches the pipeline after its related records or reporting window were already processed. A resilient design loads it safely and updates the affected tables rather than silently dropping it, keeping totals accurate.

Question 36

When should you query with SQL vs KQL vs DAX in Fabric?

Accepted Answer

Use T-SQL for relational warehouse/lakehouse table queries, KQL for high-volume telemetry and log data in an eventhouse/KQL database, and DAX for calculations and measures inside a semantic model. Match the language to the asset.

Question 37

SQL GROUP BY vs KQL summarize — what is the parallel?

Accepted Answer

Both aggregate rows into groups. SQL uses GROUP BY with aggregate functions over relational tables; KQL uses the summarize operator over telemetry/log data. Same intent, different language for a different store.

Question 38

What does the Visual Query Editor do, and who is it for?

Accepted Answer

The Visual Query Editor builds queries graphically (select, filter, join, aggregate) without writing SQL, generating the underlying query for you. It suits quick exploration and users who prefer a no-code path over hand-written T-SQL.

Question 39

In DAX, what do CALCULATE and EVALUATE each do?

Accepted Answer

CALCULATE evaluates an expression in a modified filter context — the core function for measure logic. EVALUATE returns a table result from a DAX query. CALCULATE shapes filter context; EVALUATE returns a queryable table.

Question 40

Import vs DirectQuery vs Direct Lake — how does each get data?

Accepted Answer

Import copies data into the model's in-memory (VertiPaq) cache and refreshes on a schedule. DirectQuery sends live queries to the source at report time. Direct Lake reads OneLake Delta/Parquet files directly — near-import speed with high freshness and no scheduled import.

Question 41

What is Direct Lake fallback and what triggers it?

Accepted Answer

Fallback is when a Direct Lake model temporarily switches to DirectQuery against the SQL endpoint for a query it cannot serve from OneLake directly (for example, unsupported features or limits). Performance then behaves like DirectQuery for those queries.

Question 42

What is framing in Direct Lake, and why does it matter for freshness?

Accepted Answer

Framing is the operation that updates which OneLake data files a Direct Lake model points to. Until reframing occurs, the model may serve the previously framed version, so framing controls how current Direct Lake results are.

Question 43

What is a composite model and when is it useful?

Accepted Answer

A composite model mixes storage modes (for example, Import plus DirectQuery, or a Direct Lake source plus an imported table) in one semantic model. Use it to combine fast cached data with live or supplemental sources in a single model.

Question 44

What problem does a bridge table solve in a semantic model?

Accepted Answer

A bridge table resolves a many-to-many relationship between two dimensions or between a fact and a dimension by sitting between them, restoring clean one-to-many filter paths and avoiding ambiguous or incorrect filtering.

Question 45

Calculation group vs Field parameter — what does each let users do?

Accepted Answer

A calculation group applies reusable measure variants (such as time-intelligence or formatting) across many measures without duplicating them. A field parameter lets a user switch which dimension or measure a visual uses. One reuses measure logic; the other switches fields.

Question 46

Measure vs Calculated column — when do you choose each?

Accepted Answer

A calculated column is computed and stored per row at refresh, increasing model size. A measure is computed at query time in the current filter context. Prefer measures for aggregations; use calculated columns only when a stored per-row value is required (for example, to slice or relate by).

Question 47

What is context transition in DAX?

Accepted Answer

Context transition is when row context is converted into an equivalent filter context — triggered by CALCULATE (and implicitly by measures referenced inside an iterator). It is essential for correct per-row measure aggregation and a common source of subtle DAX bugs.

Question 48

Why use VAR in DAX measures?

Accepted Answer

A VAR stores a sub-expression once so it is evaluated a single time and reused, improving readability and performance. It also locks the value at its evaluation context, avoiding accidental recomputation under shifting filter context.

Question 49

What does incremental refresh do for a large Import semantic model?

Accepted Answer

Incremental refresh partitions data by date so only recent partitions refresh while historical partitions stay cached. It shortens refresh time and reduces source load for large models instead of reloading all history every refresh.

Question 50

Why does high column cardinality hurt an Import (VertiPaq) model, and how does star schema help?

Accepted Answer

VertiPaq compresses columns; high-cardinality columns compress poorly, inflating model size and slowing queries. A star schema moves descriptive, repeating attributes into dimensions and keeps the fact narrow, reducing cardinality pressure and improving compression and speed.

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