DP-700 Practice Test 13 — 15 Questions

Question 1

An organization is using the 'Fabric Capacity Metrics' app to monitor their F64 capacity. They see a high 'Background rejection' rate. What does this indicator typically mean for the analytics solution?

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

Accepted Answer

Scheduled jobs and refreshes are being throttled due to capacity exhaustion.. Background rejection occurs when the capacity has exhausted its available units and the 'smoothing' window for background tasks (like scheduled refreshes or notebook jobs) is full. Unlike interactive tasks, which may experience latency, background tasks are rejected if they cannot be accommodated within the capacity's future limits. Monitoring this metric is vital for right-sizing the capacity for automated workloads.

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The firewall is blocking requests from unauthorized IP addresses.

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The OneLake storage limit has been exceeded for the tenant.

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User login attempts to the Fabric portal are failing.

Question 2

Your organization is implementing a multi-stage development lifecycle in Microsoft Fabric. You need to ensure that when content moves from the 'Test' workspace to the 'Production' workspace, the data source connection automatically switches from the test SQL database to the production SQL database. Which feature should you configure?

Accepted Answer

Deployment pipeline rules. Deployment pipeline rules allow for the dynamic modification of item properties, such as data source connections or parameter values, during the deployment process between stages. This ensures that the production environment remains isolated from development data without requiring manual intervention after every deployment. It is a critical component for maintaining environment parity and reducing manual configuration errors in enterprise analytics.

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Workspace-level sensitivity labels

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OneLake shortcuts

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Git integration branch policies

Question 3

An administrator needs to organize Fabric items using Domains. They want to ensure that items are automatically assigned to the 'Sales' domain based on their location. Which THREE of the following steps are required to implement and verify this domain-based organization?

Accepted Answer

Create the 'Sales' domain in the Fabric Admin portal.. Domains in Fabric provide a way to group workspaces and items by business units. Automating this process reduces manual effort and ensures consistent governance. Understanding the workflow from creation to assignment and verification is key for administrators managing large-scale Fabric environments with multiple business departments and varying data ownership.

Answer

Define a workspace name-based rule for automatic assignment.

Answer

Enable the 'Contributors can update domain' setting.

Question 4

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

Question 5

You are designing a Fabric pipeline to copy data from an on-premises SQL Server to a OneLake Lakehouse. Which integration runtime configuration ensures the highest throughput for large datasets?

Accepted Answer

Configure a Self-hosted Integration Runtime and enable the Staged Copy feature using an Azure Blob storage account.. Self-hosted integration runtimes are required for on-premises data sources. To maximize throughput for large volumes, you must enable staged copy, which allows the data to be temporarily cached in an Azure Blob storage account before landing in the Lakehouse. This approach optimizes performance by decoupling the read and write operations, reducing latency and avoiding bottlenecks on the on-premises gateway during heavy transformation or ingestion tasks.

Answer

Use the default Azure Integration Runtime without a staging account.

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Deploy a Self-hosted Integration Runtime on a virtual machine in the same region as the Lakehouse.

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Increase the Data Integration Unit (DIU) setting to the maximum value in the Copy Activity.

Question 6

You are processing a large dataset in a Notebook and encounter frequent 'Shuffle' operations that slow down the job. What is the most likely cause?

Accepted Answer

The job is performing wide transformations on large datasets.. Frequent shuffles are almost always caused by operations that redistribute data across the cluster, such as wide transformations like group-by, join, or distinct. When these operations are performed on large datasets, the cost of moving data over the network becomes the primary bottleneck. Reducing the need for these operations or optimizing the join strategy is critical for improving performance in distributed Spark processing environments.

Answer

The cluster has too much memory.

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The data is already partitioned correctly.

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The file format is set to Delta.

Question 7

You are configuring a medallion architecture in Microsoft Fabric using Lakehouse Delta tables. You need to implement an incremental data load pattern from a bronze table to a silver table using a PySpark notebook. Which TWO actions must you perform to ensure idempotency and high performance? Choose exactly two.

Accepted Answer

Use the Delta Lake merge operation based on a unique business surrogate key to handle both inserts and updates idempotently.. Implementing merge operations with watermarking and schema evolution enables incremental updates while maintaining table consistency. Idempotency guarantees that running the pipeline multiple times for the same time window yields identical results without data duplication, which is critical for robust enterprise data engineering pipelines in Microsoft Fabric.

Answer

Overwrite the entire silver table partition dynamically on every execution without checking for existing record identifiers.

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Disable Delta Lake transaction logs during the write phase to accelerate the throughput of incremental insert operations.

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Cache the entire bronze table into the Spark driver memory before executing the join and transformation logic.

Question 8

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.

Answer

On-premises Data Gateway.

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Service Principal Authentication.

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OneLake Shortcut.

Question 9

Your organization uses a Fabric Spark notebook to process large volumes of streaming data into a Delta table. You notice that over time, query performance on the table is degrading significantly. Upon investigation, you find thousands of small files in the underlying OneLake folder. Which optimization strategy should you implement to resolve the performance issue while maintaining data integrity?

Accepted Answer

Execute the OPTIMIZE command on the Delta table.. Small file problems are a common performance bottleneck in distributed systems like Spark when writing streaming data. Frequent small writes create metadata overhead and slow down file scanning during reads. Implementing the OPTIMIZE command with V-Order or enabling automatic compaction during the write process consolidates these small files into larger, more efficient Parquet files, significantly improving query execution speed.

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Run the VACUUM command with a retention period of zero hours.

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Increase the Spark executor count to distribute the load.

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Disable V-Order on the Spark session to reduce write overhead.

Question 10

When monitoring Fabric Data Pipelines, which TWO metrics are most useful for identifying performance issues in data copy activities?

Accepted Answer

Data throughput (MB/s).. In a copy activity, identifying data throughput and the duration of the 'Data Integration Unit' (DIU) utilization is critical. If the throughput is low, it suggests network constraints or inefficient serialization/deserialization. Monitoring the duration allows engineers to understand if the activity is hitting concurrency limits or if source/destination systems are struggling to keep up with the data volume requested by the copy operation.

Answer

Number of active users in the workspace.

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Total number of rows in the destination table.

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The name of the user who triggered the pipeline.

Question 11

You need to automate the ingestion of files from an Azure Blob Storage container into a Fabric Lakehouse. Which THREE components are required to build this automated pipeline? (Choose three)

Accepted Answer

A Trigger to define the execution schedule. An automated pipeline requires a trigger to start the process, a Linked Service to connect to the source storage, and the Copy Activity to perform the actual movement. These three components form the core backbone of any Fabric data pipeline. Mastery of these components is essential for orchestrating repeatable and scalable data workflows that can handle modern data engineering requirements across complex enterprise environments.

Answer

A Managed VNet configuration

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A SQL Server Integration Services (SSIS) project

Question 12

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 13

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?

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 14

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.

Answer

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 15

You need to monitor the health and performance of your Microsoft Fabric environment. Which two components should you primarily monitor to identify bottlenecks in data movement?

Accepted Answer

On-premises Data Gateway performance. Bottlenecks in data movement usually occur at the integration boundary, specifically the Gateway or the Pipeline execution engine. Monitoring the Gateway ensures that local data sources can be reached, while monitoring Pipeline activity provides insight into throughput and failure rates. Combined, these two metrics allow engineers to determine if the issue is with network connectivity, local resource constraints, or the orchestration logic itself, which is critical for maintaining reliable end-to-end data pipelines.

Answer

OneLake file storage size

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User interaction counts on Power BI reports

Answer

Workspace user list