DP 700-Microsoft Certified Fabric Data Engineer Sample Q / A

Discover Microsoft Fabric, an end-to-end data analytics platform unifying data integration, engineering, science, and visualization on one lake, with services like Data Factory, Spark, Synapse, Purview, and Power BI.

OneLake provides unified, centralized storage as the backbone of Microsoft Fabric, a single logical data lake automatically available to all Fabric workloads.

One Lake brings a centralized data lake approach to Fabric, enabling cross-department access via a single source for Power BI reports and machine learning models without data duplication.

Create an Azure account by starting free, forming a Microsoft account with a new email, verifying phone and captcha, then a small-charge credit card verification to access the portal.

Learn to create a Microsoft Fabric account using a work or school email, by creating a new Active Directory user, configuring two-step verification, and activating the 60-day free trial.

Explore the fabric portal and workspace, navigate the admin and settings panels, manage capacity and trial status, and learn to create workspaces, deploy pipelines, and connect with one leg catalog.

Explore the differences between database, data warehouse, data lake, and Lakehouse, and learn how Delta Lake enables consistency and manage vs external tables in Lakehouse architectures.

Identify the four fabric workspace roles—admin, member, contributor, and viewer—and their permissions, then learn how to manage access and add new users with assigned roles.

Discover how to create a lakehouse in fabric, switch to sql analytics mode, manage tables and files, upload data, and understand sql endpoint and semantic model.

Upload data to lakehouse by adding files or folders, create subfolders, preview data, and load into a delta table stored as parquet with overwrite or append options.

Upload a folder into lakehouse by selecting the sales details directory, including subfolder 2024, to create a delta managed table from csv files with the same schema and data.

Explore the read-only SQL analytical endpoint, navigate lakehouse databases and dbo schema, and learn to create views and stored procedures while querying data without updates.

Open OneLake with file explorer to view the lakehouse, folders, and tables like sales January, then see csvs convert to parquet and generate logs during updates.

Create a workspace and a lakehouse in Fabric, upload files, and build tables using those files; access them via File Explorer and follow along with the demo.

Create a second workspace and lakehouse, upload January and February order detail csv files, and build a managed delta table from the data stored as parquet.

Learn how to use shortcuts in fabric to access data without copying, including internal shortcuts that stay within a lake and external shortcuts to sources like S3 or Adls.

Create internal shortcuts to access files and tables across microsoft fabric lakehouses, using folders like order details for quick, non-replicating data previews.

Create internal shortcuts within a table session in fabric, selecting lakehouses and delta format tables, and manage table versus file shortcuts to access and refresh order details from parquet data.

Create an external shortcut to Azure Data Lake Storage Gen2 by provisioning an ADLS Gen2 storage account in Azure Portal, enabling hierarchical namespace, and connecting via its URL.

Create an external shortcut to ADLs from fabric, set organizational account authentication, grant the fabric user storage blob data contributor access, and manage shortcuts to access data without duplication.

Enable one link cache for adls in fabric workspace settings, set a retention period up to 28 days, and understand that cached data updates when the source changes.

Learn fabric data factory by building a demo data pipeline from a blank canvas, configuring a wait activity, managing execution, triggers, and history, and noting Azure Data Factory differences.

Copy order details January from a data lake to another lakehouse using Fabric Data Factory, with a configurable copy data pipeline and mapping.

Learn to create dynamic pipelines with parameters in Azure Data Factory, passing values to wait activities and using for each to tailor execution.

Execute the for each activity in Azure Data Factory and Fabric to process lists of items, choosing sequential or parallel execution and performing actions such as copy or delete.

Build a pipeline with a filter activity, set a parameter for file names, and filter items whose name ends with .csv to output only csv files.

Explore using switch activity to route items by category inside a for each loop, filter files ending with csv, and handle default cases with case statements and dynamic content.

Use a lookup activity to read data from a file or table, choose first row only or all rows, and feed results into a filter for csv data in lakehouse.

Learn how to use the get metadata activity in Fabric to read table and file details, verify existence, and retrieve metadata such as column counts and structures from a lakehouse.

Use the get metadata activity to inspect a folder or file, list all child items, and view metadata such as item type, size, and last modified dates.

Learn to use the get metadata output in downstream activities, filter by child item names, and extract a category from file names with underscores to drive a metadata-driven pipeline.

Configure a copy operation inside a switch activity to route delimited data into category-based subfolders in the lakehouse, with three user-defined copy operations for HR, orders, and sales.

Demonstrate a dynamic copy flow routing data by category into subdirectories (inventory, customer, other) using foreach and switch, with category-based file names and lakehouse validation.

Monitor a source location for a load completed file, validate the date in its name, then copy data to the target and delete the source file.

Create a pipeline using a pipeline activity with an until file arrive condition, set a file_assist boolean variable, and use get metadata to check for the sales February CSV file.

Use the get metadata activity to build dynamic file names with date-based endings, implement an until loop to check file existence, and control flow with set variable to determine success.

Use the if condition activity to evaluate if a file exists, routing actions based on get metadata output. Leverage wait logic and set variable steps to manage file arrival.

Configure a data pipeline that validates a received file in a lake house, copies it to a new location, deletes the source, and invokes the next pipeline stage.

Execute a fabric data pipeline by configuring an invoke pipeline activity, selecting the correct workspace and lab two, and validating successful completion.

Enable dynamic pipelines by introducing variables to control an until loop, decrement a max counter, update values by subtracting one, and correctly place failure handling outside the until activity.

Explore end-to-end project testing by configuring an until loop to stop when a file exists or a counter reaches zero, with parameter-driven counters and conditional logic.

Learn to monitor fabric pipelines using the monitor tab, view run history, and filter by status, item type, time, user, and workspace, then rerun or edit pipelines.

Explore fabric data engineering questions on cleaning and validating data as it lands in the lakehouse, using dataflow gen two for deduplication and schema validation with auto mapping.

Create a new pipeline from the pre-built template, customize transformation logic to add deduplication for CSV ingestion to Lakehouse, and enable schema validation and a notebook activity for sentiment classification.

Data engineers learn to explicitly map dataflow Gen2 outputs to lakehouse tables in Microsoft Fabric, ensuring transformed data is saved to the correct lakehouse and not run without data.

Enable a retry policy to automatically recover from transient connectivity issues in the Microsoft Fabric data pipeline by using three retries with a five-minute delay between attempts.

Explore incremental refresh in Microsoft Fabric Dataflow Gen2 for Lakehouse pipelines, focusing on using a date-time column to load only new or modified records and reduce compute costs.

Learn to configure event-based triggers in fabric pipelines to auto-trigger on Azure Data Lake Storage Gen2 uploads, enabling real-time processing and automatic loading into a lakehouse.

Automate fabric data pipeline and lakehouse deployment across dev to prod with Azure DevOps pipelines, fabric rest api, and parameterized templates, securing access via Azure Key Vault and service principals.

Use Fabric's built-in data lineage view to trace dependencies before deleting a column and assess the impact on downstream dashboards and semantic models.

Learn when to enable real time dashboards in fabric by adjusting admin settings, and compare real time dashboards with Power BI for streaming telemetry and regional filters.

Explore different view types in spark: simple view, temporary view, global temp view, and materialized view; learn when each persists, shares across sessions, or pre-compute results to boost performance.

Explore exam-style questions on fabric data engineering, covering parallelism for large ingestions from Azure Data Lake Storage Gen2, fault tolerance for bad records, and encryption in transit with object-level security.

Explore scenario-based questions for Fabric data engineering, covering ingestion optimization with copy parallelism, automated retries, secure authentication with managed identity, broadcast joins, object-level security, and parallelism limitations.

Learn fabric security essentials for data access, using row-level security for region filtering and object-level security to hide tables, plus Azure DevOps deployment and git pull requests for changes.

Explore version-controlled fabric assets with git integration, feature branching, and pull requests, and implement fabric deployment pipelines with stage-based approvals and automated promotions via rest API and Azure DevOps.

Use role play to practice DP 700 questions, exploring career benefits and needed tools, with chat or voice input and short audio uploads for feedback.