Microsoft DP-700 Exam Prep : Fabric Data Engineer Associate

Discover why Microsoft Fabric unifies analytics, data science, and data engineering within Azure, eliminating overhead and enabling seamless data collaboration among analysts, scientists, and engineers.

Discover how Microsoft Fabric delivers an end-to-end unified data platform powered by one lake, integrating Fabric Data Factory, Fabric Data Warehouse, real-time analytics, Fabric Data Science, and Power BI.

Explore the fabric hierarchy from tenant to fabric capacity and workspaces, and learn how SKUs like F2, F4, and especially F64 define capacity units across regions.

Explain the four fabric workspace roles—admin, member, contributor, and viewer—showing their permissions, how admins manage capacity and users, and the distinction between workspace and capacity levels.

Discover how to create and use a free Microsoft Fabric account, secure a 60-day trial of Fabric capacity, and navigate tenant types and sign-in requirements.

Explore OneLake, a single unified data lake with enhanced features that serves as a one drive for data, storing all formats within fabric capacity.

Discover why one lake consolidates data across departments, replacing multiple storage accounts with a single data lake, and learn to navigate the Fabric one lake catalog and explorer.

Learn to download, install, and sign in to the One Lake File Explorer in fabric, and use it to access data via the catalog or File Explorer.

Fabric lakehouse merges data lake and data warehouse on one lake, stores data as files, and enables table features on top, including external and managed tables, with Delta Lake.

Upload CSV data to the lake house using a Brazilian e-commerce dataset to practice data engineering and data warehousing, including using lake catalog, bronze lake house, and previewing CSV files.

Explore the OneLake file explorer within the lake house, view and upload data in Fabric, and navigate files and tables with auto sync, including login troubleshooting.

Learn to create tables in a lakehouse by loading a csv file without code, view the delta-backed table, preview data, and understand managed versus default storage.

Upload Parquet files into a lakehouse, create a source workspace and lakehouse, load data into a delta-formatted table, and understand Parquet's snappy encoding and preview limitations.

Learn how internal shortcuts use a single data lake as the source of truth, creating shortcuts across workspaces and avoiding data duplication by referencing Azure Data Lake Storage Gen2.

Explore external shortcuts in fabric that let you connect a workspace to data residing in data lakes outside fabric. Access data stored in S3 or GCS without rebuilding your solution.

Create internal shortcuts to files across different lake houses in fabric, organize into a source data folder with drag-and-drop, and rename shortcuts for clarity.

Create internal shortcuts in tables by using a table as source from the Lake House, and understand target versus shortcut locations; enable file shortcuts with Delta format and preview data.

Create external shortcuts in fabric to access data in Azure Data Lake Storage Gen2, configure containers and hierarchical namespace, and manage access with Storage Blob Data Contributor roles.

Learn how caching in shortcuts reduces egress costs by caching data from a data lake inside Fabric via external shortcuts, with automated cache refresh and a 28-day retention policy.

Create a lakehouse with schema by adding a new schema and using schema shortcuts to manage delta files.

Explore lakehouse sql endpoints in read-only mode to run sql queries and build Power BI reports without changing data.

Master data ingestion in fabric, covering ETL and ELT approaches, and explore batch options: fabric data factory, data flows Gen2, and spark notebooks, with real-time analytics for streaming.

Compare fabric data factory with Azure Data Factory, cover ETL and ELT concepts, data movement and orchestration, low-code workflows, and how notebooks and dataflows enable indirect ETL within ELT.

Explore fabric data factory overview, create data pipelines in the fabric portal with a blank canvas and copy activity, and understand the json behind and the role of parameters.

Learn how the copy activity moves data from a source to a destination in fabric data factory, performing extraction and loading as an ELT process with connections and data sets.

Ingest data from Azure Data Lake Storage Gen2 by configuring a copy activity, transferring CSV files to Bronze Lakehouse and saving them as Parquet, with fault tolerance for bad rows.

Master for each activity, array, and parameter handling in Fabric Data Factory to drive copy activities on CSV files from source to bronze lake house.

Fetch metadata with the get metadata activity to inspect files or folders in Fabric Data Factory and access the output, including child items.

Apply the filter activity in data factory to extract CSV items from an array by a name-based condition, using outputs and the contents function to feed subsequent steps.

connect the filter csv output to a for each loop and configure it. add an if condition to compare item.name with the target file and delete when equal.

Use the delete activity in data factory to remove files from a container folder, with file names from the loop in a for-each, and introduce set variable in next chapter.

Create a pipeline integer variable V files and set it with the length of the array using dynamic content and length function to enable email notifications in Fabric Data Factory.

Configure email and teams failure alerts in Fabric Factory, sign in with corporate accounts, and orchestrate pipelines using the invoke activity and a parent pipeline.

Configure and run a parent invoke pipeline in fabric data pipelines, sign in, create connection, pass parameters, chain a metadata-driven child pipeline, monitor execution, and validate results in data lake.

Learn how triggers in Fabric Data Factory automate pipelines with schedule and events triggers, configuring start/end dates, intervals, and time zones to run jobs without manual clicks.

Monitor Fabric Data Factory pipelines by viewing run history in the home or monitor tab, then filter by data pipeline and status to inspect failures.

Explore data flows in fabric as a low-code ETL tool that transforms and loads data from multiple sources into destinations like SQL databases and lake houses, complementing data factory.

Learn type casting in dataflow gen2 within fabric dp-700, using no-code transformations in a Power Query editor to transform parquet data and load into the bronze lake house.

Replace spaces with hyphens in the customer city column using the replace values transformation in dataflowgen2, showing how to edit or undo in data flows without writing code.

Master string transformations in fabric data engineering, including split columns by delimiter, rename, apply case changes, and use trim to remove extra spaces for numerical transformations.

Explore applying statistical functions to numeric data, including count, count distinct values, square root, sine, cosine, even/odd checks, and logarithms, using transform and diagram view for data flow.

Explore the diagram view in dataflow gen2 to visualize the data frame's transformations, from source and navigation steps to grouping, sorting, and joins across multiple sources.

Learn to join two data sources in fabric data flows using merge queries as new, perform a left outer join on customer ID, and adjust columns without code.

Add a lakehouse destination for your data flow, selecting bronze or silver lakehouse and mapping to enrich table. Publish the flow and run it via manual or automatic refresh.

Learn how to refresh a data flow using the refresh button, schedule daily or weekly refreshes, and consider integrating the data flow with fabric data factory to orchestrate within pipelines.

Learn how to integrate DataflowGen2 with Data Factory by building and scheduling pipelines, embedding a data flow into a parent pipeline, and orchestrating end-to-end data transformations.

Explore data engineering with Spark, the leading framework for distributed computing and massive parallel processing, and discover PySpark as the Python API on top of Spark.

Explore fabric node sizes via t-shirt sizing from small to double extra large, noting small (4 v cores, 64 gb) and medium (8 v cores, 64 gb) per machine.

Compare starter pools and custom pools in fabric: starter pools offer a default, always-on cluster (1-10 machines, medium) for development; custom pools specify 3 to 100 machines with XL nodes.

Explore fabric notebooks within the data engineering workload, from creating a lake house and notebooks to running PySpark and Spark SQL cells with magic commands, environments, and code snippets.

Learn PySpark fundamentals, reading data with a spark session and spark read in parquet format from abfs paths, creating and displaying dataframes, and applying simple transformations like select and withColumnRenamed.

Demonstrate typecasting in PySpark by using withColumn, column, and cast to convert the customer zipcode to integer, then display the dataframe and print its schema to verify.

Transform date columns by converting the shipping limit date to timestamp, handling spaces and formats, and casting price and freight value to float for cleaner data.

Learn to read data into a Spark dataframe, use regexp_replace to replace values, apply multiple withColumn transformations, and cast columns to integers, while enabling infer schema for CSV data.

Learn PySpark intermediate level functions to transform strings, handle nulls, cast columns to integers, apply filters, and build conditional flags with when and otherwise.

Import and display the orders data frame, remove any nulls with drop any, convert order purchase timestamp to timestamp format, and use Spark SQL for querying.

Convert a dataframe to a temporary view and run Spark SQL in PySpark. Use either SQL code cells or the Spark dot SQL API.

Create and customize charts in fabric notebooks to visualize big data, using time series from order timestamps, counts by customer ID, and pie charts for delivery status and product categories.

Explore PySpark data writing in Fabric, mastering external vs managed tables, delta format, and append, overwrite, error, and ignore modes across lakehouse storage.

Learn notebook utils in fabric, focusing on file system operations (ls, cp, fast cp, mv, rm), mkdir, put, and notebook run/exit features for exam readiness.

Explore Delta Lake tables in Fabric, mastering time travel, behind the scenes optimization techniques, and streaming with Spark Structured Streaming on Delta Lake tables for exam readiness.

Create a bronze delta table in Bronze Lake House using Spark SQL, perform inserts, updates, and deletes, and travel back to a prior version with data versioning and restore.

Learn optimization strategies in Delta Lake tables, including the optimize command for larger files, the z order for data skipping with column statistics, and V order for compression.

Learn how the optimize write command coalesces partitions before writing and how it differs from the post-write optimize command, and vacuum removes files to save space and GDPR data deletion.

Explore spark structured streaming with Delta Lake tables, using a delta table as the source and streaming to a destination with idempotency (exactly-once) and checkpointed writes.

Create isolated fabric environments to tailor compute, spark and delta settings for select notebooks without altering workspace defaults, enabling multiple environments with custom libraries and configurations.

Create and configure a Fabric environment with starter and custom pools, manage autoscale and runtime version, and organize libraries, resources, and compute for notebooks.

Monitor Spark notebook runs with the monitor tab and Spark Web UI, view details and recent runs, then schedule notebooks and integrate them into data pipelines.

Explore spark job definition in Fabric, comparing notebook-based submissions and Python file submissions, and learn how to attach lake houses, reference files, and arguments for scheduled data transformations.

Import notebooks from your PC into Fabric by using the workspace import button, selecting from this computer, and uploading from GitHub or other repositories.

Discover how data warehousing works in fabric, including lake centric data warehouses, delta format data, and the star and snowflake schemas, plus slowly changing dimensions types 1-3 for reporting.

learn how to create a fabric data warehouse, use mirroring to sync external sources such as snowflake and databricks, and access read-only sql endpoints for querying and reporting.

Load data into a data warehouse using ETL flows or copy into command for ADLs Gen2, or use create table as select to load from OneLake.

Learn to load data from an Azure data lake into a fabric data warehouse using the copy into command, and see how ctas offers a faster alternative with skeleton creation.

Master ctas to copy lake house data into a data warehouse, create a gold schema, and build curated tables like enriched customers and orders via cross data warehouse queries.

Build a gold view by joining orders, order items, and products to fetch delivered orders, apply a low-mid-high weight flag, and publish a reusable view for category analytics.

Use window functions in T-SQL to create a gold layer business view for payments. Partition by payment type and apply dense_rank on payment value to generate top buckets.

Explore T-SQL functions in fabric data warehouse, learn how to create dynamic SQL with parameters that return tables, and compare functions with views using a customer zip code example.

Learn how T-SQL stored procedures differ from functions, enabling DML operations, with a practical create-and-run example and guidance on monitoring via information schema and query insights.

Monitor current data warehouse activity with dynamic management views (DMVs) like system.dm_exec_connections, dm_exec_sessions, and dm_exec_requests to track connections, sessions, and long-running queries.

Explore query monitoring with fabric's dedicated insights schema, featuring three views: request history, long running queries, and frequently run queries to track recent, prolonged, and common SQL activity.

Explore low/no-code data modeling in fabric data warehouse with the visual query editor, using a blank canvas to build joins, merges, and transforms, then save results as views.

Learn to query a data warehouse from notebooks using T-SQL, perform DML and DDL, and apply lake centric security with row and column controls.

Master security in data warehousing with dynamic data masking, row and object level controls, and hands-on SSMS setup for Fabric data warehouse.

Learn fabric data warehouse access control by applying roles and rules to grant read all data, monitor queries, audit activity, and share permissions, while enforcing least privilege for new users.

Explore dynamic data masking in Fabric data warehouse to protect sensitive data by masking emails and identifiers in the front-end view. Owners see full data; viewers receive masked values.

Implement column level security in fabric warehouse by creating roles such as data analyst, assigning members, and denying access to columns like unique_id, then test access to ensure hidden data.

Explore row level security to ensure users only view their own data by using email IDs, security predicates, and security policies in fabric.

Build a semantic model in fabric from the gold layer, enabling data analysts to create reports. Define relationships and cardinalities (1 to 1, 1 to many) across tables.

Learn how the direct link semantic model in fabric combines import mode and direct query, enabling querying on one leg data while loading only the columns used by DAX queries.

Explore fabric access control to secure your fabric workspace and its objects, like warehouses and lake houses, from insiders and outsiders. Apply the least privileged principle across multiple access layers.

Master data lineage within fabric to track end-to-end data flow, assess impact, and visualize lineage across sources, lakehouses, and cross-workspace environments.

Provide badges that certify items and build trust for developers. Promote, certify, and designate master data to mark reliability.

Explore fabric monitoring through the monitor catalog to filter and view pipelines, notebooks, and lake houses by status, last 24 hours, and workspace, with customizable columns such as duration.

Learn how fabric admin creates and assign domains to workspaces, navigate the admin portal and tenant settings, and distinguish domain governance from workspace admin tasks.

Learn how fabric gateways enable secure connections to on-prem and cloud data, manage connections in the fabric portal, and monitor gateway governance and maintenance for reliable access.

Utilize the fabric capacity metric app to monitor capacity, performance, and resource usage, analyze throttling with graphs, and learn when upgrades or temporary resources may be needed.