DP-700 Ingest and Transform Data Practice Test 4 — 15 Questions

Question 1

You have a Lakehouse with several Delta tables. You need to provide a SQL-based interface for data analysts to query this data using their existing SQL tools, but you want to ensure they do not accidentally modify the underlying files. What should you use?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

The SQL Analytics Endpoint.. The SQL Analytics Endpoint is a read-only gateway to the Delta tables in a Lakehouse. It automatically surfaces Delta tables as SQL tables, allowing users to use T-SQL for analysis while the underlying storage remains immutable through this specific interface, ensuring data integrity for analytical workloads.

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A Fabric Warehouse.

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A Spark Notebook with SQL cells.

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A Shortcut to a SQL Server database.

Question 2

When writing data to a Lakehouse table using a Spark Notebook in Microsoft Fabric, which optimization is enabled by default to ensure maximum compatibility and performance across all Fabric engines?

Accepted Answer

V-Order.. V-Order is a proprietary optimization in Microsoft Fabric that enhances the Parquet file format used by Delta Lake. It is enabled by default for all write operations in Fabric to ensure that data is stored in a way that is highly optimized for the platform's diverse compute engines.

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Z-Order indexing.

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Bloom Filters.

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GZIP Compression.

Question 3

You are configuring a Dataflow Gen2 to perform incremental updates for a large fact table. Which TWO steps are essential to ensure the dataflow only processes new or changed records during each run?

Accepted Answer

Create a parameter to store the last successful refresh date.. Incremental loading in Dataflows Gen2 requires a mechanism to identify new data and a logic to handle how that data is integrated into the destination. This typically involves using parameters to filter source data based on a 'last modified' date and configuring the destination to append new records.

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Enable 'High Concurrency' mode for the Dataflow.

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Use the 'Remove Duplicates' transformation on the entire table.

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Configure a 'Tumbling Window' trigger in the Lakehouse.

Question 4

Refer to the exhibit. You have applied these Spark configurations to your Fabric environment. What is the primary benefit of this specific combination of settings when transforming data into Delta tables?

Accepted Answer

It ensures high-performance reads by optimizing file layout and format.. This configuration enables V-Order, optimized writes, and auto-compaction. Together, these settings ensure that data is not only stored in the optimized V-Order format but also that the files produced are of an ideal size, reducing the 'small file problem' and improving read performance for all downstream engines.

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It enables real-time data streaming from Kafka sources.

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It allows the SQL Analytics Endpoint to perform write operations.

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It automatically encrypts the data using customer-managed keys.

Question 5

You need to ingest data from an Azure SQL Database that is protected by a firewall and does not allow public network access. Which Fabric feature should you use to establish a secure connection without opening the firewall to all Azure services?

Accepted Answer

Managed Private Endpoint.. Managed Private Endpoints in Microsoft Fabric allow for secure, private connectivity to data sources that are behind firewalls or restricted to private networks. They ensure that data traffic remains within the Microsoft network backbone and provide a specific, secure path for ingestion activities like Dataflows and Pipelines.

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On-premises Data Gateway.

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Service Principal Authentication.

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OneLake Shortcut.

Question 6

You need to ingest data from an on-premises SQL Server database into a Fabric Lakehouse. What must be installed and configured on a local server to facilitate this connection?

Accepted Answer

On-premises Data Gateway.. The On-premises Data Gateway acts as a bridge, providing quick and secure data transfer between on-premises data (data that isn't in the cloud) and Microsoft cloud services like Fabric. It is a requirement for accessing local data sources that are not publicly accessible over the internet.

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Azure Data Factory Integration Runtime.

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SQL Server Integration Services (SSIS).

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Microsoft Entra Connect.

Question 7

You are configuring a Copy Activity in Fabric Data Factory to ingest data from an external REST API. Which TWO configuration settings are required to ensure data is handled correctly? (Choose two)

Accepted Answer

Pagination rules. When working with REST APIs, you must define the base URL and the pagination rules to capture the full dataset. Many APIs limit the number of records per request; therefore, setting up pagination ensures the pipeline automatically requests subsequent pages until all data is retrieved. Correct configuration of these properties is vital for robust, scalable data ingestion that avoids incomplete data loads in your lakehouse.

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An on-premises data gateway

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A self-hosted integration runtime

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A JSON schema definition

Question 8

You are using Dataflow Gen2 to ingest data from multiple sources. You need to perform a complex transformation that involves merging data from a SQL database and a flat file based on a common key. Which step should you take to achieve this efficiently?

Accepted Answer

Use the Merge transformation. In Dataflow Gen2, the 'Merge' transformation allows users to join tables from different sources within the Power Query interface. By selecting the common key, you can combine datasets into a single model for downstream processing. This approach is highly efficient as it leverages the underlying mashup engine to perform the heavy lifting, ensuring the transformation is processed in a unified, maintainable data pipeline.

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Write a custom Python script

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Create a temporary SQL view

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Export both to CSV first

Question 9

Which file format should you choose for a Lakehouse to ensure the best support for ACID transactions and high-performance analytical queries?

Accepted Answer

Delta. Delta Lake is the open-source storage layer that brings reliability and performance to the data lake. It is the native format for Fabric Lakehouses, providing ACID transactions, schema enforcement, and time travel capabilities. Using Delta format allows Fabric to optimize file access, leverage metadata, and significantly improve query performance for BI and data science workloads, making it the industry standard for modern data lake architectures.

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CSV

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JSON

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Avro

Question 10

When creating a Dataflow Gen2, what is the primary advantage of using a staging-enabled destination?

Accepted Answer

It enables query folding and performance optimization. Staging-enabled destinations in Dataflow Gen2 allow the mashup engine to perform intermediate data operations in the Fabric lakehouse. This improves performance by enabling query folding and reducing the data transfer load on the source system. It effectively offloads complex transformations to the cloud storage, ensuring that the final data load is optimized and consistently formatted for downstream usage in the Fabric ecosystem.

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It increases the number of concurrent users

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It reduces the cost of storage

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It automatically encrypts the data

Question 11

Refer to the exhibit. You are running a Fabric Notebook. What is the most likely cause of the error shown?

Accepted Answer

The Spark session has timed out. The 'SESSION_NOT_FOUND' error occurs when the Spark session assigned to the notebook has timed out or been terminated while the job was waiting to execute. This is common if there is a long wait time for resource allocation or if the cluster was idle for too long. Checking the cluster availability and workspace capacity settings is essential to ensure resources are available for the Spark job.

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The Python code has a syntax error

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The notebook is missing a library

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The Lakehouse path is incorrect

Question 12

Which Fabric feature allows you to orchestrate complex data ingestion and transformation workflows using a drag-and-drop interface?

Accepted Answer

Data Pipeline. Data Factory pipelines in Fabric provide a powerful, low-code interface for orchestrating end-to-end data integration. By dragging and dropping activities such as Copy, Notebook, and Dataflow, engineers can build robust, repeatable workflows. This feature is fundamental for simplifying the management of complex data pipelines, allowing teams to focus on logic and flow rather than writing extensive custom code for orchestration and scheduling.

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KQL Queryset

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Power BI Report

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Mirroring

Question 13

You are monitoring a Data Factory pipeline. What is the best way to be notified when a pipeline fails during an execution?

Accepted Answer

Use the alerts in the monitoring hub. Fabric pipeline monitoring can be enhanced using alerts configured within the monitoring hub or by utilizing Microsoft Fabric's integration with Azure Monitor/Log Analytics. By setting up alert rules based on pipeline status, you ensure proactive notification, allowing for rapid response and troubleshooting. This is a critical operational practice for maintaining high-reliability data systems, as it reduces downtime and ensures data quality standards are consistently met.

Answer

Manually refresh the monitor tab

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Check the SQL database logs

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Run a daily Notebook to check status

Question 14

You are optimizing a Spark job in Fabric. Which TWO of the following techniques will help reduce the execution time? (Choose two)

Accepted Answer

Cache frequently used DataFrames. Reducing execution time in Spark requires efficient resource utilization and minimizing unnecessary data processing. Caching DataFrames that are reused multiple times prevents redundant computations, while enabling adaptive query execution allows Spark to dynamically optimize the physical plan based on runtime statistics. Together, these strategies significantly lower latency and resource consumption, leading to a much faster and more cost-effective data transformation process within the Fabric Notebook environment.

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Always set the number of partitions to 1

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Convert all data to JSON

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Increase the idle timeout

Question 15

Which Fabric tool would you use to perform a visual, low-code data transformation that directly results in a clean table in your Lakehouse?

Accepted Answer

Dataflow Gen2. Dataflow Gen2 provides a highly intuitive, low-code interface for complex transformations. It uses the Power Query engine to allow users to connect, clean, and transform data visually. Once the transformations are complete, the data can be loaded directly into a Fabric Lakehouse, making it the perfect tool for non-programmers to create clean, production-ready tables without needing to manage code-based pipelines or notebooks.

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Notebook

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Data Pipeline

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SQL Endpoint