DP-700 Practice Test 5 — 10 Questions

Question 1

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?

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

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.

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

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

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

Question 2

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.

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Smaller storage footprint than Parquet

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Faster cold-start execution for Spark clusters

Question 3

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.

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Manually delete old Parquet files from the file system

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Convert the table to CSV format

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Increase the number of partitions to equal total file count

Question 4

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.

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Data Factory Mirroring

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Pipeline Copy Activity

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Eventstreams

Question 5

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.

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Power BI Desktop

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Azure Resource Manager (ARM) templates

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OneLake file explorer

Question 6

You are building a pipeline and need to ensure that a downstream activity only runs if a previous activity succeeds. Which pipeline feature should you use?

Accepted Answer

An 'On Success' connector. The 'On Success' constraint on the pipeline connector links two activities in a dependency chain. This ensures that the second activity starts only if the first one finishes without errors. This is fundamental for building reliable, production-grade workflows where the order of operations and the integrity of the data processing chain are paramount for successful data delivery to the end users.

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A ForEach activity

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A Wait activity

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A Filter activity

Question 7

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 8

Your team is using a Fabric Lakehouse to store Parquet files. You notice that the storage costs are increasing faster than expected, and there are many old versions of the data being retained. Which maintenance task should you schedule to optimize OneLake storage costs without affecting the current production data?

Accepted Answer

Run the VACUUM command on the Delta tables.. Delta tables in Fabric maintain a history of changes to support features like 'Time Travel'. However, this history consumes storage space over time as old, unreferenced Parquet files are kept. The VACUUM command is the standard maintenance operation to permanently delete these old files, balancing historical data needs with storage efficiency.

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Run the OPTIMIZE command on all tables.

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Change the workspace license to 'Pro'.

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Delete the '_delta_log' folder in OneLake.

Question 9

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 10

You are designing a data pipeline in Microsoft Fabric to ingest incremental data from an on-premises SQL Server. You need to ensure that only records modified since the last pipeline run are ingested. Which approach should you implement to maintain optimal performance and minimal data redundancy?

Accepted Answer

Configure the Copy activity to use a dynamic query with a watermark column from a control table.. Implementing a watermark column strategy is the standard practice for incremental loading in Fabric data pipelines. By storing the last processed timestamp or incremental ID in a control table, the pipeline can dynamically filter incoming data. This approach significantly reduces compute costs and ingestion time compared to full table loads, ensuring that only delta changes are processed while maintaining data consistency across the lakehouse destination.

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Execute a full truncate and load operation using a Lookup activity before the Copy activity.

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Use a Dataflow Gen2 to perform a full join between the source and the existing lakehouse table.

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Enable Change Data Capture (CDC) on the source SQL Server and ingest all logs into a CSV file.