Microsoft DP-500 Practice Test - Questions Answers, Page 5

Announcing Power BI Premium Per User general availability and autoscale preview for Gen2.

Power BI Premium per user features and capabilities

* Pixel perfect paginated reports are available for operational reporting capabilities based on SSRS technology. Users can create highly formatted reports in various formats such as PDF and PPT, which are embeddable in applications and are designed to be printed or shared.

* Automated machine learning (AutoML) in Power BI enables business users to build ML models to predict outcomes without having to write any code.

* Etc.

Note:

Power BI empowers every business user and business analyst to get amazing insights with AI infused experiences. With Power BI Premium, we enable business analysts to not only analyze and visualize their data, but to also build an end-to-end data platform through drag and drop experiences.

Everything from ingesting and transforming data at scale, to building automated machine learning models, and analyzing massive volumes of data is now possible for our millions of business analysts.

Reference: https://powerbi.microsoft.com/nl-be/blog/announcing-power-bi-premium-per-usergeneral-availability-and-autoscale-preview-for-gen2/

Optimize datasets for write operations by enabling large models

When using the XMLA endpoint for dataset management with write operations, it's recommended you enable the dataset for large models. This reduces the overhead of write operations, which can make them considerably faster. For datasets over 1 GB in size (after compression), the difference can be significant.

Tabular Editor supports Azure Analysis Services and Power BI Premium Datasets through XMLA read/write.

Note: Tabular Editor - An open-source tool for creating, maintaining, and managing tabular models using an intuitive, lightweight editor. A hierarchical view shows all objects in your tabular model.

Objects are organized by display folders with support for multi-select property editing and DAX syntax highlighting. XMLA read-only is required for query operations. Read-write is required for metadata operations.

Reference: https://docs.microsoft.com/en-us/power-bi/enterprise/service-premium-connect-tools

https://tabulareditor.github.io/

Reference: https://docs.microsoft.com/en-us/power-bi/enterprise/service-premium-connect-tools

https://powerbi.microsoft.com/en-us/blog/power-bi-february-service-update

Authentication for enumerating serverless SQL database resources

There are three places you'll need to set authentication to allow Microsoft Purview to enumerate your serverless SQL database resources:

The Azure Synapse workspace

The associated storage

The Azure Synapse serverless databases

The steps below will set permissions for all three.

Azure Synapse workspace

In the Azure portal, go to the Azure Synapse workspace resource.

On the left pane, select Access Control (IAM).

Select the Add button.

Set the Reader role and enter your Microsoft Purview account name, which represents its managed service identity (MSI).

Select Save to finish assigning the role

Azure Synapse Analytics serverless SQL pool catalog Purview Azure Purview managed service identity

Storage account

In the Azure portal, go to the Resource group or Subscription that the storage account associated with the Azure Synapse workspace is in.

On the left pane, select Access Control (IAM).

Select the Add button.

Set the Storage blob data reader role and enter your Microsoft Purview account name (which represents its MSI) in the Select box.

Select Save to finish assigning the role.

Azure Synapse serverless database

Go to your Azure Synapse workspace and open the Synapse Studio.

Select the Data tab on the left menu.

Select the ellipsis (...) next to one of your databases, and then start a new SQL script.

Add the Microsoft Purview account MSI (represented by the account name) on the serverless SQL databases. You do so by running the following command in your SQL script:

SQL

CREATE LOGIN [PurviewAccountName] FROM EXTERNAL PROVIDER;

Apply permissions to scan the contents of the workspace

You can set up authentication for an Azure Synapse source in either of two ways. Select your scenario below for steps to apply permissions.

Understanding folding with Query Diagnostics

One of the most common reasons to use Query Diagnostics is to have a better understanding of what operations were 'pushed down' by Power Query to be performed by the back-end data source, which is also known as 'folding'. If we want to see what folded, we can look at what is the 'most specific' query, or queries, that get sent to the back-end data source. We can look at this for both ODATA and

SQL.

Reference: https://docs.microsoft.com/en-us/power-query/querydiagnosticsfolding

Prepare files for querying

If possible, you can prepare files for better performance:

* Convert large CSV and JSON files to Parquet. Parquet is a columnar format. Because it's compressed, its file sizes are smaller than CSV or JSON files that contain the same data. Serverless

SQL pool skips the columns and rows that aren't needed in a query if you're reading Parquet files.

Serverless SQL pool needs less time and fewer storage requests to read it.

Reference: https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/best-practices-serverlesssql-pool

https://stackoverflow.com/questions/65320949/parquet-vs-delta-format-in-azure-data-lake-gen-2store

Denormalize to improve query performance.

Note: Normalization prevents data duplications, preserves disk space, and improves the performance of the disk I/O operations. The downside of the normalization is that the queries based on these normalized tables require more table joins.

Schema denormalization (i.e. consolidation of some dimension tables) for such databases can significantly reduce costs of the analytical queries and improve the performance. Reference: https://www.mssqltips.com/sqlservertip/7114/denormalization-dimensions-synapsemapping-data-flow/