Microsoft DP-700 Implementing Data Engineering Solutions Using Microsoft Fabric Exam Practice Test

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.

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.

To meet the specified requirements for User1, the solution must ensure:

Read access to the table data in Lakehouse1: User1 needs permission to access the data within Lakehouse1. By sharing Lakehouse1 with User1 and selecting the Read all SQL endpoint data option, User1 will be able to query the data via SQL endpoints.

Prevent Apache Spark usage: By sharing the lakehouse directly and selecting the SQL endpoint data option, you specifically enable SQL-based access to the data, preventing User1 from using Apache Spark to query the data.

Prevent access to other items in Workspace1: Assigning User1 the Viewer role for Workspace1 ensures that User1 can only view the shared items (in this case, Lakehouse1), without accessing other resources such as notebooks, pipelines, or Power BI reports within Workspace1.

This approach provides the appropriate level of access while restricting User1 to only the required resources and preventing access to other workspace assets.

To identify the version of Delta used when Notebook1 was executed, you should use the Admin monitoring workspace. The Admin monitoring workspace allows you to track and monitor detailed information about the execution of notebooks and jobs, including the underlying versions of Delta or other technologies used. It provides insights into execution details, including versions and configurations used during job runs, making it the most appropriate choice for identifying the Delta version used during the execution of Notebook1.

To allow Job1 in Workspace1 to access an Azure SQL database (Source1) with public internet access disabled, you need to create a managed private endpoint. A managed private endpoint is a secure, private connection that enables services like Fabric (or other Azure services) to access resources such as databases, storage accounts, or other services within a virtual network (VNet) without requiring public internet access. This approach maintains the security and integrity of your data while enabling access to the Azure SQL database.

VACUUM is used to clean up storage by removing files no longer in use by a Delta table. It removes old and unreferenced files from Delta tables. For example, to remove files older than 7 days:

VACUUM delta.`/path_to_table` RETAIN 7 HOURS;

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Join between Products and ProductSubCategories:

Use an INNER JOIN.

The goal is to include only products that are assigned to a subcategory. An INNER JOIN ensures that only matching records (i.e., products with a valid subcategory) are included.

Join between ProductSubCategories and ProductCategories:

Use an INNER JOIN.

Similar to the above logic, we want to include only subcategories assigned to a valid product category. An INNER JOIN ensures this condition is met.

WHERE Clause

Condition: IsActive = 1

Only active products (where IsActive equals 1) should be included in the gold layer. This filters out inactive products.

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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.

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.

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).

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.

Data Analysts' Access Requirements must only have read access to the Delta tables in the gold layer and not have access to the bronze and silver layers.

The gold layer data is typically queried via SQL Endpoints. Granting the Read all SQL Endpoint data permission allows data analysts to query the data using familiar SQL-based tools while restricting access to the underlying files.