Microsoft DP-700 Practice Test - Questions Answers, Page 2

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.

In a star schema, the DimProduct table serves as a dimension table that contains descriptive attributes about products. It will provide context for the FactSales table, which contains transactional data. The following columns should be included in the DimProduct table:

ProductName: The ProductName is an important descriptive attribute of the product, which is needed for analysis and reporting in a dimensional model.

ProductColor: ProductColor is another descriptive attribute of the product. In a star schema, it makes sense to include attributes like color in the dimension table to help categorize products in the analysis.

ProductID: ProductID is the primary key for the DimProduct table, which will be used to join the FactSales table to the product dimension. It's essential for uniquely identifying each product in the model.

To ensure that Notebook2 can attach to the same Apache Spark session as Notebook1, you need to enable high concurrency for notebooks. High concurrency allows multiple notebooks to share a Spark session, enabling them to run within the same Spark context and thus share resources like cached data, session state, and compute capabilities. This is particularly useful when you need notebooks to run in sequence or together while leveraging shared resources.

To meet the requirements of ensuring that the data engineer can write data to the Customer table without reading data from the Employee table (which contains Personally Identifiable Information, or PII), you can implement the following steps:

Share Lakehouse1 with the data engineer.

By sharing Lakehouse1 with the data engineer, you provide the necessary access to the data within the lakehouse. However, this access should be controlled through roles and permissions, which will allow writing to the Customer table but prevent reading from the Employee table.

Assign the data engineer the Contributor role for Workspace1.

Assigning the Contributor role for Workspace1 grants the data engineer the ability to perform actions such as writing to tables (e.g., the Customer table) within the workspace. This role typically allows users to modify and manage data without necessarily granting them access to view all data (e.g., PII data in the Employee table).

Migrate the Employee table from Lakehouse1 to Lakehouse2.

To prevent the data engineer from accessing the Employee table (which contains PII), you can migrate the Employee table to a separate lakehouse (Lakehouse2) or workspace (Workspace2). This separation of sensitive data ensures that the data engineer's access is restricted to the Customer table in Lakehouse1, while the Employee table can be managed separately and protected under different access controls.

To implement Row-Level Security (RLS) in a Fabric warehouse, you need to use a function that defines the security logic for filtering the rows of data based on the user's identity or role. This function can be used in conjunction with a security policy to control access to specific rows in a table.

In the case of sales representatives, the function would define the filtering criteria (e.g., based on a column such as SalesRepID or SalesRepName), ensuring that each representative can only see their respective data.

A deployment pipeline in Fabric is designed to automate the process of deploying assets (such as reports, datasets, eventhouses, and other objects) between environments like Dev, Test, and Prod. Since you need to deploy an eventhouse as part of the deployment process, a deployment pipeline is the appropriate tool to move this asset through the different stages of your environment.

A deployment pipeline in Fabric allows you to deploy assets like warehouses, datasets, and reports between different workspaces (such as from Workspace1 to Workspace2). One of the key features of a deployment pipeline is the ability to check for invalid references before deployment. This can help identify issues with assets, such as broken links or dependencies, ensuring the deployment is successful without introducing errors. This is the most efficient way to verify references and manage the deployment with minimal development effort.