Course Summary

Students will build on existing analytics experience and will learn to implement and manage a data analytics environment, query and transform data, implement and manage data models, and explore and visualize data. In this course, students will use Microsoft Purview, Azure Synapse Analytics, and Power BI to build analytics solutions.

mplement and manage a data analytics environment (25–30%)
Govern and administer a data analytics environment
Manage Power BI assets by using Microsoft Purview

Identify data sources in Azure by using Microsoft Purview

Recommend settings in the Power BI admin portal

Recommend a monitoring and auditing solution for a data analytics environment, including Power BI REST API and PowerShell cmdlets

Integrate an analytics platform into an existing IT infrastructure
Identify requirements for a solution, including features, performance, and licensing strategy

Configure and manage Power BI capacity

Recommend and configure an on-premises gateway type for Power BI

Recommend and configure a Power BI tenant or workspace to integrate with Azure Data Lake Storage Gen2

Integrate an existing Power BI workspace into Azure Synapse Analytics

Manage the analytics development lifecycle
Commit Azure Synapse Analytics code and artifacts to a source control repository

Recommend a deployment strategy for Power BI assets

Recommend a source control strategy for Power BI assets

Implement and manage deployment pipelines in Power BI

Perform impact analysis of downstream dependencies from dataflows and datasets

Recommend automation solutions for the analytics development lifecycle, including Power BI REST API and PowerShell cmdlets

Deploy and manage datasets by using the XMLA endpoint

Create reusable assets, including Power BI template (.pbit) files, Power BI data source (.pbids) files, and shared datasets

Query and transform data (20–25%)
Query data by using Azure Synapse Analytics
Identify an appropriate Azure Synapse pool when analyzing data

Recommend appropriate file types for querying serverless SQL pools

Query relational data sources in dedicated or serverless SQL pools, including querying partitioned data sources

Use a machine learning PREDICT function in a query

Ingest and transform data by using Power BI
Identify data loading performance bottlenecks in Power Query or data sources

Implement performance improvements in Power Query and data sources

Create and manage scalable Power BI dataflows

Identify and manage privacy settings on data sources

Create queries, functions, and parameters by using the Power Query Advanced Editor

Query advanced data sources, including JSON, Parquet, APIs, and Azure Machine Learning models

Implement and manage data models (25–30%)
Design and build tabular models
Choose when to use DirectQuery for Power BI datasets

Choose when to use external tools, including DAX Studio and Tabular Editor 2

Create calculation groups

Write calculations that use DAX variables and functions, for example handling blanks or errors, creating virtual relationships, and working with iterators

Design and build a large format dataset

Design and build composite models, including aggregations

Design and implement enterprise-scale row-level security and object-level security

Optimize enterprise-scale data models
Identify and implement performance improvements in queries and report visuals

Troubleshoot DAX performance by using DAX Studio

Optimize a data model by using Tabular Editor 2

Analyze data model efficiency by using VertiPaq Analyzer

Optimize query performance by using DAX Studio

Implement incremental refresh (including the use of query folding)

Optimize a data model by using denormalization

Explore and visualize data (20–25%)
Explore data by using Azure Synapse Analytics
Explore data by using native visuals in Spark notebooks

Explore and visualize data by using the Azure Synapse SQL results pane

Visualize data by using Power BI
Create and import a custom report theme

Create R or Python visuals in Power BI

Connect to and query datasets by using the XMLA endpoint

Create and distribute paginated reports in Power BI Report Builder

Before attending this course, it is recommended that students have: A foundational knowledge of core data concepts and how they’re implemented using Azure data services. Experience designing and building scalable data models, cleaning and transforming data, and enabling advanced analytic capabilities that provide meaningful business value using Microsoft Power BI

Implement and manage a data analytics environment (25–30%) Query and transform data (20–25%) Implement and manage data models (25–30%) Explore and visualize data (20–25%)

Following your booking, a confirmation message will be sent to all participants, ensuring you're well-informed of your successful enrollment. Calendar placeholders will also be dispatched to assist you in scheduling your commitments around the course. Rest assured, all course materials and access to necessary labs or platforms will be provided no later than one week before the course begins, allowing you ample time to prepare and engage fully with the learning experience ahead.

Our comprehensive training package includes all the necessary materials and resources to facilitate a full learning experience. Enrollees will be provided with detailed course content, encompassing a wide array of topics to ensure a thorough understanding of the subject matter. Additionally, participants will receive a certificate of completion to recognize their dedication and hard work. It's important to note that while the course fee covers all training materials and experiences, the examination fee for certification is not included but can be purchased separately.

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