DP-700 Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Free Practice Exam Questions (2026 Updated)

The input JSON contains the configuration details and parameters passed to the Copy data activity during execution, including the dynamically generated SQL query.

Viewing the input JSON for the failed pipeline run provides direct insight into what query was executed at the time of failure.

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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.

An eventstream is the best solution for ingesting data from Azure Event Hub into Fabric, while applying filtering logic such as retaining only the events that have a Neighbourhood value of " Chelsea. " Eventstreams in Microsoft Fabric are designed for handling real-time data streams and can apply transformation logic directly on incoming events. In this case, the eventstream can filter events based on the Neighbourhood field before storing the retained events in a Fabric lakehouse.

Eventstreams are well-suited for stream processing, such as this case where you need to filter out only specific data (events with a Neighbourhood of " Chelsea " ) before storing it in the lakehouse.

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 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.

When you enable OneLake availability for an eventhouse, both new and existing data in the eventhouse will be copied to OneLake. This feature ensures that data, whether newly ingested or already present, becomes available for access through OneLake, making it easier for users to interact with and explore the data directly from OneLake file explorer.

To ensure that the usage of the data in the Amazon S3 bucket meets the technical requirements, we must address two key points:

Minimize egress costs associated with cross-cloud data access: Using a shortcut ensures that Fabric does not replicate the data from the S3 bucket into the lakehouse but rather provides direct access to the data in its original location. This minimizes cross-cloud data transfer and avoids additional egress costs.

Prevent saving a copy of the raw data in the lakehouses: Disabling caching ensures that the raw data is not copied or persisted in the Fabric workspace. The data is accessed on-demand directly from the Amazon S3 bucket.

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Bronze Layer: A pipeline Copy activity

The bronze layer is used to store raw, unprocessed data. The requirements specify that no transformations should be applied before landing the data in this layer. Using a pipeline Copy activity ensures minimal development effort, built-in connectors, and the ability to ingest the data directly into the Delta format in the bronze layer.

Silver Layer: A notebook

The silver layer involves extensive data cleansing (deduplication, handling missing values, and standardizing capitalization). A notebook provides the flexibility to implement complex transformations and is well-suited for this task.

The technical requirements specify that:

Medallion layers must be fully populated sequentially (bronze → silver → gold). Each layer must be populated before the next.

If any step fails, the process must notify the data engineers.

Data imports should run simultaneously when possible.

Why Use a Data Pipeline That Calls Other Data Pipelines?

A data pipeline provides a modular and reusable approach to orchestrating the sequential population of medallion layers.

By calling other pipelines, each pipeline can focus on populating a specific layer (bronze, silver, or gold), simplifying development and maintenance.

A parent pipeline can handle:

- Sequential execution of child pipelines.

- Error handling to send email notifications upon failures.

- Parallel execution of tasks where possible (e.g., simultaneous imports into the bronze layer).

MAR1 has seven entities, each accessible via a different API endpoint. A ForEach activity is required to iterate over these endpoints to fetch data from each one. It enables dynamic execution of API calls for each entity.

The Copy data activity is the primary mechanism to extract data from REST APIs and load it into the bronze layer in Delta format. It supports native connectors for REST APIs and Delta, minimizing development effort.