DP-700 Sample Questions & Answers

You are tasked with implementing security for a new Fabric Lakehouse. The requirements are as follows:

1. Data engineers must have full control over the Lakehouse, including the ability to read and write to tables and underlying files.
2. Business analysts must be able to query all data using the SQL endpoint but must be prevented from accessing the underlying files in OneLake.
3. A specific group of junior analysts must only see data for the 'North America' region from the Sales table.

Which combination of security mechanisms should you use? (Select TWO)

You are troubleshooting a slow-running PySpark notebook in a Fabric workspace. The notebook reads a large Delta table, performs a series of transformations, and then joins it with a small dimension table. You observe from the Spark UI that one particular stage is taking an exceptionally long time and has a significant amount of data shuffle.

The problematic code snippet is:
large_df.join(small_df, 'user_id', 'inner')

Which optimization technique should you apply to mitigate this performance bottleneck?

A manufacturing company uses Microsoft Fabric to monitor its production line. An Eventstream ingests sensor data, which is then written to a KQL database for real-time dashboarding. A critical requirement is to detect anomalies where the average temperature of a specific sensor (sensorId = 'SN01-T5') exceeds 150 degrees Celsius over a 5-minute period. You need to write a KQL query to create an alert based on this condition.

Which KQL query correctly implements this logic?

True or False: When you use the Mirroring feature in Microsoft Fabric to replicate an Azure SQL Database, the initial setup performs a full snapshot of the source data, and subsequent changes are then replicated in near real-time using change data capture (CDC), without requiring the configuration of a separate data pipeline.

Case Study: Global E-Commerce Platform

Company Background
GlobalCart is a multinational e-commerce company that operates in North America, Europe, and Asia. They are migrating their analytics platform to Microsoft Fabric to create a unified view of their sales, customer, and inventory data. Their primary goal is to empower regional business units with self-service analytics while maintaining centralized governance and data engineering standards.

Existing Environment
Data is currently stored in a mix of sources: an on-premises SQL Server 2019 database for inventory, an Azure SQL Database for sales transactions, and Parquet files in an ADLS Gen2 account for historical clickstream data. The company has a single Fabric F64 capacity located in 'Central US'. The data engineering team is proficient in SQL and PySpark.

Requirements

1. Architecture: Implement a medallion architecture using a central 'Corporate' workspace for Bronze and Silver layer Lakehouses. Each region (NA, EU, AS) must have its own separate workspace containing a Gold layer Lakehouse with data filtered specifically for that region.
2. Data Ingestion: Data from the on-premises SQL Server must be ingested nightly with minimal impact on the source system. Sales data from Azure SQL should be replicated in near real-time.
3. Transformation: Transformations from Bronze to Silver will be complex and require custom Python libraries. Transformations from Silver to the regional Gold layers will involve filtering and simple aggregations.
4. Security: Data analysts in each region must only be able to access the Gold layer Lakehouse in their respective regional workspace. They should not have any access to the 'Corporate' workspace or other regions' workspaces.

Problem Statement
You need to design the data flow and transformation strategy to populate the regional Gold layer Lakehouses from the centralized Silver layer Lakehouse. The solution must be efficient and scalable. Which approach best meets the requirements?

A data engineer has configured a Fabric deployment pipeline with Development, Test, and Production stages. After deploying changes from Test to Production, they notice that the connection details for a Lakehouse shortcut, which should point to a different storage account in Production, have not been updated. They need to ensure that connection details are automatically updated during deployment. What must be configured in the deployment pipeline?

You are designing a solution to ingest data from an Amazon S3 bucket into a Fabric Lakehouse. The data consists of historical logs stored in Parquet format, totaling over 5 TB. The key requirements are to minimize data duplication and avoid cross-cloud data transfer costs for exploratory analysis, while still allowing data to be transformed and loaded into managed Delta tables for production reporting.

Which Fabric feature should you use as the primary mechanism to access the S3 data?

A data pipeline that loads data into a Fabric Warehouse fails intermittently. The pipeline monitoring view shows that the 'Copy Data' activity is failing with the error message: Execution Timeout Expired. The timeout period elapsed prior to completion of the operation or the server is not responding. The source is an on-premises SQL Server accessed via a data gateway.

What is the most likely cause of this issue and the best first step to resolve it?

You are implementing a medallion architecture in a Fabric Lakehouse. You need to perform the VACUUM operation on your Delta tables to remove old, unreferenced files and manage storage costs. According to Microsoft's recommended best practices, how should you typically execute the VACUUM command?

You are using Spark Structured Streaming in a Fabric notebook to process a continuous stream of JSON messages. The goal is to write the output to a Delta table while handling late-arriving data. You need to ensure that events arriving up to 1 hour late are included in the correct windowed aggregation. Which combination of Structured Streaming features should you use? (Select TWO)