Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric DP-700 Exam Dumps: Updated Questions & Answers (September 2026)

The requirement specifies that Litware plans to make the book reviews available in the lakehouse without making a copy of the data. In this case, creating a shortcut in Fabric is the most appropriate solution. A shortcut is a reference to the external data, and it allows Litware to access the book reviews stored in Amazon S3 without duplicating the data into the lakehouse.

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SELECT last_run_start_time, last_run_command: These fields will help identify the execution details of the long-running queries.

FROM queryinsights.long_running_queries: The correct solution is to check the long-running queries using the queryinsights.long_running_queries view, which provides insights into queries that take longer than expected to execute.

WHERE last_run_total_elapsed_time_ms > 7200000: This condition filters queries that took more than 2 hours to complete (7200000 milliseconds), which is relevant to the issue described.

AND number_of_failed_runs > 1: This condition is key for identifying queries that have failed more than once, helping to isolate the problematic queries that cause failures and need attention.

In this case, the key issue causing performance degradation likely stems from the use of MD5 hash surrogate keys. MD5 hashes are 128-bit values, which can be inefficient for large datasets like the 500 million rows in your fact table. Using a more efficient data type for surrogate keys (such as integer or bigint) would reduce the storage and processing overhead, leading to better query performance. This approach will improve performance while minimizing operational costs because it reduces the complexity of querying and indexing, as smaller data types are generally faster and more efficient to process.

The sales data issue can be resolved by configuring incremental refresh for the dataflow. Incremental refresh allows for only the new or changed data to be processed, minimizing the amount of data transferred and improving performance.

The solution specifies that data older than one month never changes, so setting the refresh period to 1 Month is appropriate. This ensures that only the most recent month of data will be refreshed, reducing unnecessary data transfers.

The correct sequence is Create a notebook → Attach the notebook to Lakehouse1 → Create a data pipeline .

First, create a Fabric notebook because notebooks provide the interactive development environment required for authoring, executing, debugging, and testing PySpark code. Microsoft describes Fabric notebooks as a primary code item for developing Apache Spark workloads and specifically supports their use for data ingestion, preparation, and transformation.

Second, attach the notebook to Lakehouse1 . Attaching a lakehouse establishes the notebook ' s lakehouse context, allowing PySpark code to access the lakehouse ' s Files and Tables through Spark and OneLake. This enables developers to interactively validate the transformation against the required Fabric data.

Third, create a data pipeline and use a Notebook activity to operationalize the notebook. Fabric Data Factory explicitly supports running Fabric notebooks as pipeline activities for automated data transformation workflows. Pipelines can subsequently be run manually, scheduled, or incorporated into larger orchestration processes.

Converting the notebook to an Apache Spark job definition is unnecessary; Spark job definitions are separate executable Fabric items. Publishing the notebook is also not required for pipeline execution.

Study Guide reference: Ingest and transform data → Transform data with Apache Spark → Fabric notebooks → Orchestrate processes and schedules with Data Factory pipelines.