DP-700 Practice Test 2025 (Fabric Data Engineer Associate)

Understand shortcut caching in fabric data workspaces, enabling cross-workspace data access via external sources like AWS and S3. Enforce a 24-hour retention, serving reads from cache when eligible.

Ingest events from Azure Event Hub to a Microsoft Fabric Lakehouse, using kql and sql query sets to filter before storage, with Event Stream transformations filtering bikes greater than ten.

Construct a kql query joining customers and sales on customer id, extend date column, and project customer name, date, and sale amount; then summarize with make_list for payment modes used.

Learn to optimize KQL queries by using the project operator for calculated columns, apply filters immediately after table references, and start joins with the smaller table.

Demonstrate how the update policy moves records from table A to table B in a KQL table, highlighting dynamic data and later data-type changes.

Learn two kql queries on a visits table: per-user running totals with row_cumulative_sum, and session start plus nth visit using row_window_session and row_number, with ten-minute gaps and a 30-minute limit.

Identify KQL queries to display visits with ordered amount over 150 sorted by visit time in ascending order; option B adds explicit ascending sort, option C uses T-sql default ascending.

Learn safe data type conversion in fabric warehouse by casting the unit price from varchar to integer. Use try_convert to handle non-numeric values and keep the query running without errors.

Explore ranking functions in kql and window functions on serialized data, including row_rank_min, row_rank_dense, and row_rank_mean, and how tied rows affect rank values.

Discover how private endpoints enable access to a disabled public azure sql server from fabric notebooks and spark jobs, and load data into a lakehouse.

Explore loading data into a delta table with PySpark in a fabric lakehouse, creating the table if needed and applying an upsert on product_id to update or insert rows.

Design a KQL table in Microsoft Fabric Event House to store telemetry from task system. Use gid for job id, dynamic for matrix data type, and duration as time span.

Analyze PySpark code in a Fabric notebook. Code A builds corrected emails withColumn and concat using split and when, while Code B flags suspicious rows with when and bitwise or.

Explain how to configure hopping windows in fabric event stream to count cars in the last minute updated every 30 seconds, using group by and aggregate transformations.

Detect and track changes in an scd type 2 dim customer table in a fabric warehouse by hashing current and staging records, expiring rows, and inserting new versions for history.

Denormalize category and subcategory attributes into the dim product table to simplify queries and boost performance in a star schema, given Fabric's lack of key constraints.

Write a KQL query that partitions by ticker and orders by trade date ascending to return each ticker's previous closing price and price change; option B is correct.

Design a fabric data pipeline that loads only the Azure SQL tables modified in the last 24 hours into an empty lakehouse, using a control.table_metadata in the fabric warehouse.

Process a lakehouse dataset in Microsoft Fabric by ingesting the lakehouse file into a fabric warehouse with dataflow gen two, and compute running totals with windowing functions in notebooks.

Demonstrate incremental data loading in a lakehouse using a fabric notebook and merge on product ID to update last reviewed dates and mark aged records expired.

Demonstrates using rollup to build hierarchical totals by year then by food item, and using grouping sets to produce year and food item totals without a grand total.

Enable change data capture (CDC) on the database and table to capture all row-level changes from the orders table and stream them via Fabric Event Stream to an event house.

Copy POS tables from Azure SQL to the bronze lake house. Use for each for parallel copies and a lookup to get tables.

Create a lake shortcut to read Amazon S3 data in real time from the bronze layer of the lake house, and enable shortcut caching to minimize egress costs.

Incrementally load new sales order data from Azure SQL to the lakehouse bronze layer using Data Flow Gen two. Use max createddate filtering and append to preserve history.

Analyze why incremental refresh for loading JSON data into the lakehouse fails because the query does not fold, and learn that enabling it offers no performance benefit.

Route invalid product records to a quarantine table using a left anti-join. Create the product dimension by inner joining to enrich with category data and save to the gold layer.

Label the semantic model as master data and the warehouse as certified to increase visibility across the organization. Avoid labeling Power BI dashboards as promoted or certified.

Identify the roles that can delete a domain in Microsoft Fabric, including domain admin, fabric admin, and global admin, across the admin portal and workspace settings.

Explain how the default domain and security group membership determine which Fabric workspaces get assigned to domains. Assess domain contributor roles and admin permissions for creating and managing workspace assignments.

Learn how to manage fabric workspace access in the Microsoft 365 Admin Center by configuring domains, domain admins, and domain contributors, including security group defaults.

Explore how a deployment pipeline moves the dev workspace's notebook and copy folder to the test workspace via a clean deploy, creating a replica where files are paired.

Discover how to overwrite test items with dev items in a Fabric deployment pipeline by pairing corresponding items across dev and test workspaces and performing a clean deploy.

connect to a semantic model from a Jupyter Notebook in Microsoft Fabric using the semantic link to dynamically execute Power BI measures. list measures and evaluate with the SymPy library.

Implement row-level security in a fabric warehouse by creating a table-valued function as the security predicate and binding it with a security policy on the orders table.

Move the customer data to a separate lakehouse via a data pipeline, then grant the user workspace contributor access to append data to the products data table.

Explore how to enable high concurrency sessions in Microsoft Fabric notebooks and determine which dev notebooks can share a single spark session based on lakehouse connections and user.

Explore how a Fabric notebook runs two Apache Spark jobs, view historical runs in the Monitor Hub timeline, and manage Delta Lake versions in Spark Settings.

Copy on premises sql server data to a fabric lakehouse using an on premises gateway, via data flow gen2 or data pipelines, with correct mappings.

Apply column-level security by granting only the metadata read first and then selectively granting select on the four allowed columns, enforcing least privilege and preventing access to the billing amount.

Examine data movement scenarios between on-premises SQL Server, Fabric Lakehouse, Fabric Warehouse, and Azure Blob Storage, and determine when to enable staging in copy activity.

Evaluate access permissions for Lakehouse data in Fabric using PySpark and SQL, with two users, and show that the proposed solution fails to meet the least-privilege goal.

Assess lakehouse access using PySpark and SQL by applying read all and subscribe permissions, then verify client access via notebook reads and SQL endpoints.

Analyze access permissions for lakehouse data using PySpark and SQL, emphasizing least-privilege design and evaluating read permissions for Spark and SQL endpoint data.

Grant delete permission on the trip table (object level) and directly share the warehouse with read all data to give Client1 granular access while following least privileges.

Apply dynamic data masking to the fabric warehouse employees table by altering columns: partial masking for email (first two and last four with @) and random masking for salary.

Enable the global git integration switch and select GitHub as the provider in Fabric workspace settings. Configure a GitHub personal access token with contents read/write to allow commits from Fabric.

Enable one lake availability to mirror a database into delta lake for fabric notebooks. Adaptive batching delays data visibility; lower latency with alter merge table kql to view data.

Show how a master notebook uses a dag to orchestrate five child notebooks with dependencies and parallelism, logging start and end times in a lakehouse to verify the execution order.

Grant workspace contributor access for data engineers to read and write lakehouse data, and give Azure DevOps basic organization access plus project contributors to enable Git commits and source control.

Learn how to optimize delta tables in a Microsoft Fabric lakehouse by vacuuming unreferenced parquet files, and using optimize to consolidate files and reorder to improve query performance.

Read data from structured, semi-structured and unstructured sources with T-SQL and KQL, write with KQL, in a Microsoft Fabric event house, and manage KQL databases to optimize performance and cost.

Demonstrates how to add a unique constraint in Microsoft Fabric Data Warehouse, explaining not enforced vs enforced, nonclustered requirements, and the column store architecture's impact on constraints.

Identify which user logins, Windows or SQL Server authenticated, are actively running queries by querying the execute sessions dynamic management view in Microsoft Fabric workspace.

Use the monitor hub to debug a failed fabric pipeline by inspecting the input JSON of the copy activity to reveal the source SQL query and the misspelled select.

Identify frequently run queries in the last 24 hours using the Query Insights schema. Review the last run command to copy the query text for performance analysis.

Diagnose schema mismatch errors between an event stream and a lakehouse destination in a Microsoft Fabric workspace, using runtime logs to obtain error counts and timestamps.

Monitor the semantic model's refresh history in your fabric workspace to quickly verify daily scheduled refreshes, view the status of the last and next refresh, duration, and any errors.

Explore how V order during data load adds overhead in fabric warehouses, and how disabling V order speeds ingestion while preserving identical compute and loads.

Replace MD5 hash keys with integer surrogate keys for the fact and product dimension, and disable row-level security on the fact table to maintain direct lake mode and boost performance.