Medium — DP-700 Practice Quiz

Question 1

A data engineering team wants to migrate their existing Directed Acyclic Graphs (DAGs) written in Python into Microsoft Fabric to orchestrate a complex multi-stage ETL process that includes calls to external REST APIs and dependencies across multiple Fabric items. They require native support for their current DAG definitions with minimal rewriting. Which Fabric capability should they configure to meet this requirement?

Accepted Answer

Create an Apache Airflow job in the workspace and upload the existing DAG files

Answer

Create a Data pipeline and use the Web activity to replicate each DAG task manually

Answer

Configure a Dataflow Gen2 with sequential query dependencies to mimic the DAG structure

Answer

Use a notebook with the Fabric REST API and mssparkutils to chain notebook executions

Question 2

A governance team at Contoso must produce a monthly report showing which users viewed or downloaded a specific certified Power BI report hosted in a Fabric workspace, including exact timestamps and IP addresses. The Fabric Data Engineer needs to identify the correct source of this activity data and the role required to retrieve it. Which approach should be used?

Accepted Answer

Query the Microsoft Purview / Microsoft 365 unified audit log, which requires an Exchange Online audit role such as View-Only Audit Logs assigned to the retrieving account

Answer

Enable the workspace-level 'Usage metrics' report, which any workspace Member can open to see per-user view events with IP addresses

Answer

Assign the retrieving account the Fabric Admin role and read the OneLake diagnostic logs stored in the workspace's default lakehouse

Answer

Turn on Git integration for the workspace so that all report view and download events are committed to the connected repository

Question 3

A data engineering team needs to ingest CSV files from an Azure Data Lake Gen2 source, apply complex multi-step transformations using PySpark logic that reuses existing custom Python libraries, and write the results to a Lakehouse Delta table. The process must run every night at 2:00 AM and the team wants to minimize the number of components in the solution. Which Fabric item should they use to author and orchestrate this workload?

Accepted Answer

A notebook scheduled directly, or invoked by a pipeline on a schedule

Answer

A Dataflow Gen2 with a scheduled refresh

Answer

A pipeline using only the Copy data activity with a schedule trigger

Answer

An Apache Airflow job calling a stored procedure

Question 4

A data engineering team is building a new analytics solution in Microsoft Fabric. The bronze and silver layers require heavy transformation of large volumes of semi-structured JSON and Parquet files using PySpark. The gold layer will serve a set of downstream analysts and BI developers who are only comfortable writing T-SQL and require full multi-table INSERT/UPDATE/DELETE transactional support with multi-statement (ACID) transactions. The team wants to minimize the number of engines they manage while satisfying both groups. Which storage design should they choose?

Accepted Answer

Use a Lakehouse for the bronze and silver layers with Spark, then load the gold layer into a Warehouse for full T-SQL DML support.

Answer

Use a Lakehouse for all layers and expose the gold layer through the Lakehouse SQL analytics endpoint for the analysts.

Answer

Use a Warehouse for all layers and run the PySpark transformations against the Warehouse using the Spark connector.

Answer

Use a KQL database for the bronze and silver layers and a Lakehouse for the gold layer.

Question 5

A retail company has a Fabric lakehouse containing raw sales files in deeply nested JSON. The data engineering team must flatten and enrich this data on a nightly schedule. The transformation logic is complex, involving multiple joins, custom Python functions for currency conversion, and reuse of libraries already published to a Spark environment. The team consists of experienced Python developers who need full control over the transformation code and expect data volumes to grow to terabytes. Which transformation approach best fits these requirements?

Accepted Answer

Author a PySpark notebook that uses the shared Spark environment and schedule it in a pipeline

Answer

Build the transformation in a Dataflow Gen2 using the Power Query M interface

Answer

Load the JSON into a warehouse and use T-SQL OPENJSON with stored procedures

Answer

Ingest the JSON into a KQL database and transform with an update policy

Question 6

A Fabric warehouse contains a table named dbo.Employees that includes a Salary column. The HR analytics team must be able to query all other columns in the table, but they must be denied access to the Salary column specifically. You want to enforce this at the object level using T-SQL without creating a separate view or masking the data. What should you do?

Accepted Answer

Use DENY SELECT ON dbo.Employees(Salary) TO the HR analytics role while granting SELECT on the table.

Answer

Apply dynamic data masking to the Salary column for the HR analytics team.

Answer

Create a row-level security predicate function that filters out the Salary column.

Answer

Assign the HR analytics team the Viewer role on the workspace to hide the Salary column.

Question 7

A data engineer must load 200 large CSV files from an Azure Data Lake Storage Gen2 container into a Fabric Lakehouse Delta table on a nightly schedule. The files require no transformation—they must be landed exactly as-is into the table with the highest possible throughput and lowest compute overhead. Which approach should the engineer choose within a Fabric data pipeline?

Accepted Answer

Use a Copy activity in the pipeline to move the files directly into the Lakehouse table

Answer

Use a Dataflow Gen2 with staging enabled to read and write each file to the table

Answer

Use a Notebook activity running PySpark to read each CSV individually and append rows

Answer

Use a KQL query in an Eventhouse to ingest the CSV files into the Lakehouse table

Question 8

A data engineering team uses a Dataflow Gen2 in Fabric to ingest a large sales transaction table into a lakehouse. The source table grows by roughly 500,000 rows daily, but historical rows are never modified. Each scheduled refresh currently reprocesses the entire table, causing long run times and high capacity consumption. The team wants to reduce refresh duration while keeping the destination data accurate. Which approach should they configure?

Accepted Answer

Enable incremental refresh on the Dataflow Gen2 query using a DateTime column, defining a bucket range and a filter to only load newly arrived data

Answer

Disable staging on all queries in the Dataflow Gen2 so data is written directly to the destination

Answer

Convert the Dataflow Gen2 to a Copy activity in a pipeline with the Overwrite write behavior

Answer

Increase the Spark pool node size in the workspace Spark settings to speed up the refresh

Question 9

A Dataflow Gen2 that loads customer data into a lakehouse has started failing intermittently. The data engineer needs to determine the specific query and error message that caused the most recent failed refresh so it can be fixed. Which action provides the fastest, most detailed root-cause information for the failed run?

Accepted Answer

Open the Dataflow Gen2 refresh history and view the detailed error for the failed refresh, including the failing query and error message

Answer

Enable diagnostic logging on the destination lakehouse and inspect the Delta transaction log

Answer

Review the Capacity Metrics app to check whether the capacity was throttled during the refresh window

Answer

Re-run the entire dataflow with staging disabled and watch the applied-steps preview for errors

Question 10

A retail analytics team needs to combine a monthly Excel workbook, a SharePoint-hosted CSV, and a small on-premises SQL Server table into a single cleansed table in a Fabric Lakehouse. The transformation involves column renaming, type conversion, and merging the three sources. The team consists of business analysts who are comfortable with Power Query but have no experience writing PySpark or SQL code, and the data volumes are small (under 100,000 rows total). Which ingestion and transformation approach best fits this team and workload?

Accepted Answer

Author a Dataflow Gen2 that connects to all three sources, applies the transformations in the Power Query editor, and sets the Lakehouse table as the output destination

Answer

Create a Spark notebook that reads each source with the appropriate connector and merges them using PySpark DataFrame joins

Answer

Write a T-SQL stored procedure in a Fabric Warehouse to join and cleanse the sources, then copy the result to the Lakehouse

Answer

Build a KQL query in an Eventhouse to ingest and transform the three sources before landing them in the Lakehouse

Question 11

A data engineering team ingests raw CSV files into a lakehouse, applies complex PySpark transformations that reuse a shared library, and then loads curated data into a warehouse. Business analysts also need a low-code way to reshape a small reference dataset from a SharePoint list before the Spark job runs. The team wants a single orchestration that: runs the reference reshaping first, then the heavy transformation, then the warehouse load, with each step only starting after the previous one succeeds. Which combination best fits these requirements?

Accepted Answer

Create a pipeline that calls a Dataflow Gen2 for the SharePoint reference reshaping, then a notebook for the PySpark transformations, then a Copy/warehouse load activity, chained with success dependencies.

Answer

Build one Dataflow Gen2 that performs the SharePoint reshaping, the PySpark transformations, and the warehouse load in sequence.

Answer

Schedule three independent notebooks with staggered start times so they run in the correct order.

Answer

Use a single notebook that reads the SharePoint list, runs the Spark transformations, and writes to the warehouse, triggered on file arrival.

Question 12

A data engineer is preparing data in a Fabric lakehouse to feed a star-schema dimensional model. The source is a set of highly normalized operational tables (Customers, Addresses, Regions, and SalesReps) that must be combined into a single wide Customer dimension table with columns from all four source tables. The transformation involves multiple joins and needs to run nightly over hundreds of millions of rows. Which approach best prepares this denormalized dimension for the dimensional model?

Accepted Answer

Create a PySpark notebook that joins the four source tables and writes the denormalized result to a Delta table in the Gold layer

Answer

Create a OneLake shortcut to each of the four source tables so the dimensional model can reference them directly without physical joins

Answer

Use a KQL query in an Eventhouse to aggregate the four tables into a materialized view for the dimension

Answer

Keep the four normalized tables as-is and build the join logic inside the semantic model's relationships at query time

Question 13

A data engineering team uses a Fabric deployment pipeline with Development, Test, and Production stages. Before promoting changes from Test to Production, the release manager wants to see exactly which items differ between the two stages so only intended changes are deployed. Which action lets the release manager view the differences between Test and Production before deploying?

Accepted Answer

Use the comparison indicator shown between the Test and Production stages in the deployment pipeline, which flags items as different, new, or identical

Answer

Open the Git integration panel in the Production workspace and review uncommitted changes

Answer

Run a Test-to-Production deployment with 'compare only' mode, which validates without publishing

Answer

Export both workspaces to a database project and use schema compare to diff the items

Question 14

Your team uses a Fabric deployment pipeline with Development, Test, and Production stages. A data engineer updated three notebooks and one semantic model in the Development stage, but a colleague warns that a fourth notebook still in Development is experimental and must NOT reach Test yet. Before deploying, you want to verify exactly which items differ between Development and Test and push only the approved changes. Which approach meets these requirements with the least risk?

Accepted Answer

Use the deployment pipeline's compare view to identify changed items, then deploy only the selected approved items to the Test stage.

Answer

Deploy the entire Development stage to Test, then manually delete the experimental notebook from the Test stage afterward.

Answer

Create a Git branch from Development containing only the approved items and merge it directly into the Test workspace.

Answer

Configure a deployment rule on the experimental notebook so it is skipped during the deployment to Test.

Question 15

A data engineering team maintains a Fabric warehouse whose schema (tables, views, stored procedures) changes frequently. They want developers to author schema changes locally in Visual Studio Code using SQL, review those changes through pull requests before merging, and validate the resulting schema against a target before publishing. Which lifecycle management approach best satisfies these requirements?

Accepted Answer

Use a Fabric database project (SQL project) integrated with Git, allowing developers to author and build schema changes locally and publish validated changes to the warehouse.

Answer

Use a Fabric deployment pipeline with three stages and rely on the automatic schema comparison to author changes directly in the production stage.

Answer

Connect the workspace to Git and have developers edit warehouse tables directly in the Fabric portal, committing changes after each edit.

Answer

Export the warehouse as a Dataflow Gen2 and version the query definitions in an external repository.

Question 16

Your team uses a Fabric deployment pipeline with three stages: Development, Test, and Production. A data pipeline in the workspace uses a copy activity that connects to a SQL source. In Development the connection points to a dev SQL server, but after deploying to Production the item must automatically point to the production SQL server without any manual edits after each deployment. What should you configure to ensure the correct server is always used in the Production stage?

Accepted Answer

Create a deployment rule on the Production stage that parameterizes the data pipeline's connection to the production SQL server.

Answer

Manually update the connection in the Production workspace after every deployment.

Answer

Enable a Git branch for the Production stage and change the server value in the pipeline JSON before each merge.

Answer

Add a sensitivity label to the Production data pipeline so it uses the correct server.

Question 17

Your team uses a Fabric deployment pipeline with Development, Test, and Production stages. After finishing several updates in Development, a data engineer notices that only two of the five modified items are ready to promote to Test; the other three are still being validated. The engineer wants to move just the two finished items to Test without affecting the unvalidated items already in Test. What should the engineer do?

Accepted Answer

Use the deployment pipeline compare view to select only the two ready items and perform a selective deployment to the Test stage.

Answer

Deploy all changed items to Test, then manually delete the three unvalidated items from the Test stage afterward.

Answer

Create a separate deployment pipeline containing only the two items and deploy that pipeline to Test.

Answer

Commit only the two items to the Git-connected branch, then deploy the entire Development stage to Test.

Question 18

A data engineering team maintains a Fabric workspace containing a Data Factory pipeline that ingests raw files, a notebook that transforms them, and a semantic model refresh. The transformation notebook must run only after the ingestion pipeline completes successfully, and business users need the entire flow to run automatically every night at 2:00 AM. The team wants a single orchestration artifact that controls the end-to-end run order and can pass the ingestion date as a parameter into the notebook. What should the team implement?

Accepted Answer

Create a Data Factory pipeline that chains a Copy activity, a Notebook activity (with a base parameter for the ingestion date), and a Semantic model refresh activity, then attach a scheduled trigger for 2:00 AM.

Answer

Schedule the ingestion pipeline, the notebook, and the semantic model refresh independently, each with its own 2:00 AM schedule so they all start at the same time.

Answer

Use a Dataflow Gen2 to perform ingestion and transformation together, then rely on the semantic model's automatic refresh to complete the flow.

Answer

Add the notebook's transformation code as a cell inside the ingestion pipeline's Copy activity and enable Airflow to trigger it hourly.

Question 19

You manage a Fabric deployment pipeline with three stages: Development, Test, and Production. Each stage is assigned to a separate workspace. After a colleague made changes in the Development workspace, you open the deployment pipeline view and notice an orange 'X' comparison indicator between the Development and Test stages, while the Test-to-Production comparison shows a green checkmark. You need to promote only the reviewed changes and verify what will move. What does the orange indicator signify, and what is the correct next step?

Accepted Answer

There are differences between the Development and Test stages; select the changed items and deploy from Development to Test to synchronize them.

Answer

The stages are identical; you must unassign and reassign the Test workspace before any deployment can occur.

Answer

The Test workspace has unsupported items; you must delete those items in Test and then run a full backward deployment from Production.

Answer

The deployment pipeline is broken; you must recreate the pipeline and re-import all items from Git to clear the indicator.

Question 20

A data engineering team wants to formalize their release process for Fabric items. They need three separate environments: one for active development, one where QA testers validate changes before release, and one that serves production reports to business users. They plan to use a Fabric deployment pipeline to move content between these environments. What is the minimum configuration they should set up to meet this requirement?

Accepted Answer

Create a deployment pipeline with three stages (Development, Test, Production), each assigned to a separate workspace.

Answer

Create three separate deployment pipelines, one per environment, and manually copy items between them.

Answer

Create a single workspace and use Git branches to represent Development, Test, and Production.

Answer

Create a deployment pipeline with two stages and reuse the Test stage workspace for production reporting.

Question 21

You manage a Fabric Warehouse containing a customer table with an EmailAddress column. Compliance requires that most analysts see only masked email values, but a small group of fraud investigators must see the full unmasked values. You apply dynamic data masking to the EmailAddress column. After applying the mask, the fraud investigators report they still cannot see the real email addresses. What must you do so that the fraud investigators see unmasked data while other analysts continue to see masked values?

Accepted Answer

Grant the UNMASK permission to the fraud investigators' principals in the Warehouse.

Answer

Add the fraud investigators to the workspace Admin role so masking no longer applies to them.

Answer

Remove the mask from the column and instead create a column-level security rule that hides it from analysts.

Answer

Assign the fraud investigators the Viewer role on the workspace and re-run the masking definition.

Question 22

Your Fabric tenant has multiple domains, and a governance team wants to formally recognize high-quality datasets. A data engineer has built a semantic model that has passed all quality reviews and should be marked as 'Certified' so it appears as the authoritative source in the OneLake data hub. The engineer attempts to set the certification but only sees the 'Promoted' option available. What must happen for the model to be marked as Certified?

Accepted Answer

The engineer must be added to the list of authorized certifiers defined by a Fabric administrator in the tenant or domain settings.

Answer

The engineer must be assigned the Admin role on the workspace containing the semantic model.

Answer

The semantic model must first be endorsed as 'Promoted' before it becomes eligible to be certified.

Answer

A sensitivity label of 'Confidential' or higher must be applied to the semantic model before certification is allowed.

Question 23

A data engineering team receives sales files from partners at unpredictable times throughout the day. The files are uploaded to a specific folder in a OneLake lakehouse Files section. The team wants a Fabric Data pipeline to run automatically within moments of a new file arriving, without polling on a fixed schedule. Which approach should they configure to trigger the pipeline?

Accepted Answer

Create a Fabric event-based trigger (using Real-Time hub / Fabric events) that listens for OneLake events and starts the pipeline on file creation

Answer

Configure a scheduled trigger on the pipeline set to run every 5 minutes and add a Get Metadata activity to check for new files

Answer

Enable Git integration on the workspace so that commits trigger the pipeline automatically

Answer

Use a Dataflow Gen2 with incremental refresh, which automatically detects and processes new files as they land

Question 24

You manage an Eventhouse (KQL database) that ingests IoT telemetry through a data connection. Overnight, downstream dashboards show no new rows for one table, while other tables continue to update normally. You need to quickly determine whether ingestion for that specific table is failing and see the underlying error messages. Which action should you take first?

Accepted Answer

Run the .show ingestion failures command against the affected table to review recent failed ingestion operations and their error reasons

Answer

Increase the hot cache retention period on the affected table to force the ingested data to reappear in queries

Answer

Drop and recreate the table so that the ingestion pipeline starts writing fresh data

Answer

Create a materialized view over the table to deduplicate the missing records

Question 25

An Eventhouse in Microsoft Fabric receives a continuous stream of small event batches from an Eventstream. Analysts complain that newly ingested data does not appear in query results for several minutes. On investigation, you find the KQL database table uses the default ingestion batching settings. You need to reduce the end-to-end latency between event arrival and query availability without significantly increasing ingestion cost per record. Which action should you take?

Accepted Answer

Lower the batching policy's time and size thresholds so batches are sealed and ingested sooner.

Answer

Increase the hot cache retention period on the table so recent data is cached longer.

Answer

Create a materialized view over the table to accelerate the analysts' queries.

Answer

Enable an update policy to transform the records as they are ingested.