DP-700 Practice Test 2 — 25 Questions

Question 1

Refer to the exhibit. You are reviewing the JSON definition for a Spark Job Definition in Microsoft Fabric. The job is failing after exhausting all retries during periods of high capacity utilization. To ensure the job has a better chance of succeeding without increasing the maxRetryCount, which change should you implement in the job configuration?

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

Accepted Answer

Add a 'retryIntervalInSeconds' property to the retryPolicy.. When a Spark job fails due to capacity constraints or resource contention, the failure often happens quickly. By adjusting the retry policy to include an interval or delay, the job can wait for transient capacity spikes to subside before attempting the next run. This increases the likelihood of success in a busy Fabric environment where resources are shared.

Answer

Increase the 'numExecutors' value to 10.

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Change the 'runtimeVersion' to 1.3.

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Change the 'driverSize' to 'Large'.

Question 2

You are setting up a Microsoft Fabric environment for a development team. You want to use Git integration to allow developers to work in their own branches. Which THREE Fabric items are currently supported for Git integration?

Accepted Answer

Lakehouse. Not all Fabric items are currently supported by Git integration. Knowing which items can be synchronized is vital for planning your development lifecycle and source control strategy. As Fabric evolves, more items are added, but the core items like Lakehouses, Notebooks, and Semantic Models form the basis of most synchronization workflows.

Answer

Data Pipeline

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KQL Queryset

Question 3

You are optimizing a Fabric Warehouse for complex analytical queries involving large fact tables. Which TWO actions should you perform to improve query performance?

Accepted Answer

Implement clustered columnstore indexes on the fact table.. Optimizing warehouse performance requires a combination of effective indexing strategies and data distribution management. By choosing the right clustering columns, you reduce data scanning requirements, while partition pruning ensures the engine only reads relevant segments. These techniques are fundamental for scaling analytical workloads in Microsoft Fabric, as they directly influence the amount of data processed during query execution and help maintain consistent performance under high concurrency.

Answer

Convert all tables to heap structures.

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Increase the number of rows per file to 1 billion.

Answer

Disable statistics collection for all columns.

Question 4

You are managing a Microsoft Fabric environment and need to implement 'Endorsement' for several high-quality datasets to help users find reliable data. Which TWO levels of endorsement can you apply to Fabric items?

Accepted Answer

Promoted. Endorsement helps users identify trustworthy and high-quality items in Fabric. It is a key part of data governance. There are two specific levels of endorsement that signify different degrees of validation, helping to distinguish between casual development items and certified, authoritative business assets used for critical reporting.

Answer

Verified

Answer

Approved

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Validated

Question 5

Which approach is most efficient for loading a large volume of historical data into a Fabric Lakehouse from an external source?

Accepted Answer

Use the Data Factory Copy activity.. Efficient data ingestion is critical for minimizing pipeline duration and cloud compute costs. Using the Copy activity in Data Factory, which is optimized for high-throughput, parallel data transfer, is the industry standard for this task. Understanding how to use the correct integration tools prevents performance bottlenecks and ensures that large-scale data migration projects remain within their performance and budget windows.

Answer

Use a Python script in a notebook to read and write rows one-by-one.

Answer

Download files locally and then upload them to the Fabric portal.

Answer

Create a Power BI report and import the data.

Question 6

An organization must comply with strict data residency requirements. They have a Microsoft Fabric tenant in the 'East US' region but need to create a workspace where data is stored and processed in 'North Europe'. How can the administrator achieve this?

Accepted Answer

Create a capacity in North Europe and assign the workspace to it.. Fabric allows for 'Multi-Geo' capabilities where specific capacities can be created in regions different from the home region of the tenant. By assigning a workspace to a capacity located in 'North Europe,' the data for that workspace will reside in that specific geographic location. This is a critical feature for global organizations needing to satisfy regional data sovereignty laws.

Answer

Change the tenant's primary region in the Admin portal settings.

Answer

Use a OneLake shortcut to move the data to North Europe.

Answer

Enable the 'Global Data Sync' feature in the workspace settings.

Question 7

You are designing a data pipeline in Microsoft Fabric using Data Factory. You need to ingest data from an on-premises SQL Server database into a Lakehouse. Which integration component must be deployed to facilitate secure connectivity between the on-premises network and Microsoft Fabric?

Accepted Answer

Self-hosted Integration Runtime. To connect Fabric to on-premises resources, the Self-hosted Integration Runtime (SHIR) acts as the bridge. It handles data transfer across the firewall without opening inbound ports on your local network. This is a critical architectural requirement for hybrid data integration scenarios, ensuring that Fabric can reach behind the perimeter to pull data securely while maintaining strict compliance with corporate security standards for data movement.

Answer

Azure Data Gateway

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Managed Virtual Network

Answer

Azure ExpressRoute

Question 8

Refer to the exhibit. You are reviewing a JSON snippet from a Fabric Data Factory pipeline. What is the result of executing this activity if the target table already exists?

Accepted Answer

The existing data in the table will be replaced by the source data.. The JSON configuration specifies a Copy Activity that pulls data from an Amazon S3 source and writes it to a Lakehouse table. The tableAction property is set to Overwrite, which instructs the engine to drop the existing data in the target table and replace it with the new incoming dataset. This is a common pattern for full-load ingestion.

Answer

The activity will append new rows to the existing table data.

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The pipeline will fail because the table already exists.

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A new table with a timestamp suffix will be created.

Question 9

You are managing a Microsoft Fabric Lakehouse. You need to ensure that data in the Bronze layer is accessible to the Silver layer while enforcing strict row-level security for downstream users. What is the most efficient way to implement this architecture while maintaining performance?

Accepted Answer

Create a Fabric semantic model over the Silver layer and implement row-level security within the model settings.. Implementing Medallion architecture using shortcuts and Fabric security roles provides a decoupled, performant approach. By utilizing OneLake shortcuts, you avoid unnecessary data movement between layers, reducing latency. Applying row-level security through Fabric's semantic model permissions ensures that end-users only access data relevant to their authorization level without requiring redundant copies of the underlying Parquet files, thus optimizing compute and storage utilization across the analytics lifecycle.

Answer

Export Bronze data to a separate Azure SQL database to apply row-level security via T-SQL stored procedures.

Answer

Use Microsoft Purview to enforce row-level security directly on the Parquet files stored in OneLake.

Answer

Create separate physical copies of the Silver layer tables for every user group requiring restricted access.

Question 10

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.

Answer

Z-Order indexing.

Answer

Bloom Filters.

Answer

GZIP Compression.

Question 11

You need to ingest data from an API into a Fabric Lakehouse. The API requires a token-based authentication refreshed every hour. What is the most efficient way to handle this?

Accepted Answer

Use a Web activity to fetch the token, then pass it to the Copy activity.. Using a Web activity in a Fabric Pipeline to call the authentication endpoint, followed by storing the token in a secure Azure Key Vault or variable, is the standard pattern. This allows the subsequent Copy activity to dynamically retrieve and use the token. This pattern ensures secure credential management while maintaining the automation required for hourly refreshes in a production data environment.

Answer

Hardcode the token in the Copy activity connection string.

Answer

Create a new connection every hour manually.

Answer

Configure the API to never expire its tokens.

Question 12

You are tasked with optimizing a Fabric Lakehouse that contains billions of rows. You notice that queries against the SQL analytics endpoint are performing poorly. Which action will most effectively improve performance?

Accepted Answer

Use Z-ordering on columns frequently included in the WHERE clause.. Implementing Z-ordering on columns frequently used in query filters, combined with efficient partitioning, is the best way to optimize query performance in a Lakehouse. Delta Lake's Z-order feature clusters related data together, allowing the SQL analytics engine to skip unnecessary files during query execution. This drastically reduces I/O and improves response times for users querying massive datasets via the SQL endpoint, ensuring a performant and scalable analytics solution.

Answer

Increase the number of partitions to the maximum value allowed.

Answer

Convert all tables to CSV format for faster read operations.

Answer

Enable auto-vacuum on the lakehouse to remove all older file versions.

Question 13

You are configuring a Fabric Pipeline. Which TWO activities can be used to perform conditional logic within your data ingestion workflow?

Accepted Answer

If-Condition activity. Conditional logic is essential for building resilient data pipelines. The If-Condition activity allows for simple branching based on Boolean expressions, while the Switch activity enables multi-way branching based on discrete values. These activities allow engineers to build sophisticated workflows that react to data quality results, source availability, or specific environmental flags, ensuring that the pipeline behaves appropriately under various conditions without manual oversight.

Answer

Lookup activity

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Stored Procedure activity

Answer

Wait activity

Question 14

When ingesting data from an external source, what is the best practice for handling sensitive data?

Accepted Answer

Store secrets in an Azure Key Vault and reference them in the connection.. Sensitive data should be handled using Azure Key Vault to store secrets and connections. By referencing these keys in your Fabric pipeline, you avoid hardcoding credentials. Furthermore, you should use data masking or encryption at rest within the Lakehouse to ensure that sensitive fields remain protected even from unauthorized users who may have access to the underlying storage, fulfilling compliance and security requirements for enterprise data management.

Answer

Hardcode credentials in the pipeline JSON code.

Answer

Pass credentials as plain text parameters to the pipeline.

Answer

Use the same service principal for all external sources.

Question 15

You are performing a large-scale data migration into a Fabric Lakehouse using a Spark Notebook. You notice that the job is failing with an 'Out of Memory' error. What is the most effective way to address this while utilizing PySpark?

Accepted Answer

Use df.repartition() to redistribute data. Out of Memory (OOM) errors in Spark often result from unbalanced partitions or attempting to load too much data into a single executor. By increasing the number of partitions (repartitioning) or adjusting the cluster configuration, you redistribute the workload across the Spark executors. This is a fundamental skill for data engineers to master, as it allows for processing massive datasets efficiently without requiring constant hardware upgrades or job failures.

Answer

Increase the cluster node count significantly

Answer

Convert the data to JSON format before loading

Answer

Disable the Spark broadcast join optimization

Question 16

You are building a data pipeline in Microsoft Fabric using a Copy Activity to ingest millions of small JSON files from an Azure Data Lake Storage Gen2 container into a Lakehouse table. The performance is significantly slower than expected due to high request latencies. Which optimization technique should you implement?

Accepted Answer

Enable staging in the Copy Activity settings to temporarily buffer data before writing it to the destination.. Enabling staging allows the Copy Activity to stage data in an intermediary storage account before writing to the Lakehouse, which efficiently compresses and batches requests. This drastically reduces the number of individual PUT operations against the storage endpoint, directly mitigating the throttling and latency issues commonly associated with processing massive volumes of small files in distributed analytics environments.

Answer

Increase the Data Integration Units (DIUs) to the maximum allowable limit of 256 for the pipeline activity.

Answer

Convert all source JSON files into single-line newline-delimited JSON format prior to executing the copy pipeline.

Answer

Switch the destination from a Delta Lake table in the Lakehouse to a raw staged folder within the Files section.

Question 17

You are designing a data transformation layer using Spark in Microsoft Fabric. Which THREE of the following are primary benefits of using Delta Lake format over traditional Parquet files? (Choose three)

Accepted Answer

ACID transaction support. Delta Lake adds a transaction log (the Delta Log) to the Parquet file format. This enables ACID transactions, preventing data corruption and partial writes. It also supports schema evolution, allowing the data structure to change over time without breaking downstream applications. Finally, 'Time Travel' allows users to query previous versions of data, which is invaluable for auditing, debugging, and reproducing analytical results. These features are critical for modern, reliable data platforms.

Answer

Smaller storage footprint than Parquet

Answer

Faster cold-start execution for Spark clusters

Question 18

You are developing a Notebook in Microsoft Fabric to process high-volume sensor data. You need to optimize performance using Delta Lake features. Which TWO actions should you perform? (Choose two)

Accepted Answer

Enable Z-Ordering on frequently filtered columns. Optimizing Delta Lake tables involves managing file sizes and data distribution. Z-Ordering optimizes multi-column queries by co-locating related data, significantly reducing read amplification. Compaction (using OPTIMIZE) merges small files into larger, more efficient files, which directly improves scan performance. Implementing these features is essential for maintaining query speed as data grows in the Lakehouse, ensuring that the Spark engine spends less time scanning unnecessary file blocks during execution.

Answer

Manually delete old Parquet files from the file system

Answer

Convert the table to CSV format

Answer

Increase the number of partitions to equal total file count

Question 19

A data engineer needs to provide access to data stored in an external Azure Data Lake Storage (ADLS) Gen2 account without moving the data into the Fabric Lakehouse. Which feature of Microsoft Fabric should be used?

Accepted Answer

Shortcuts. Shortcuts in Microsoft Fabric allow users to virtualize data by creating a pointer to an external storage location. This enables the data to appear as if it is stored locally in the Lakehouse, allowing Spark and SQL engines to query it directly. This minimizes data duplication, reduces storage costs, and simplifies data management across different environments.

Answer

Data Factory Mirroring

Answer

Pipeline Copy Activity

Answer

Eventstreams

Question 20

You are monitoring a Fabric pipeline. You need to identify which activity failed within the pipeline execution. Which tool should you use?

Accepted Answer

Fabric Pipeline Monitoring view. The 'Pipeline Monitoring' view in the Fabric workspace provides a centralized dashboard to view the status, start time, end time, and duration of all pipeline activities. Clicking on a specific pipeline run allows you to drill down into individual activities to inspect error logs, which is essential for diagnosing failures. This visibility is crucial for maintaining operational excellence and ensuring that data pipelines meet their SLAs in a production environment.

Answer

Power BI Desktop

Answer

Azure Resource Manager (ARM) templates

Answer

OneLake file explorer