Microsoft DA-100

Requirements

The Microsoft DA-100: Analyzing Data with Microsoft Power BI test does not have any official prerequisite. However, the applicants must understand the content of the exam topics. They also need to have fundamental knowledge of data processing and data repositories in Cloud and on-premises.

Ideal Audience and Required Skills

DA-100 is intended for data analysts who use MS Power BI to enhance the value of the data their organization holds. These professionals are responsible for polishing and transforming data, creating expandable data models, and performing data visualizations that can be interpreted and analyzed to withdraw critical information. Data analysts also work alongside other stakeholders of the business to provide relevant data according to their company’s requirements. Thus, a strong prior understanding of both cloud & on-premises data processing and data repositories is also required by those specialists who are going to take test DA-100.

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/da-100

Understanding functional and technical aspects of Exam DA-100: Analyzing Data with Microsoft Power BI Deploy and maintain deliverables

The following will be discussed in MICROSOFT DA-100 exam dumps:

• Create and manage workspaces
• Configure incremental refresh settings
• Configure row-level security group membership
• Identify downstream dataset dependencies
• Promote or certify Power BI content
• Use deployment pipelines
• Providing access to datasets
• Create and configure a workspace
• Apply sensitivity labels to workspace content
• Recommend a development lifecycle strategy
• Configure and update a workspace app
• Publish, import, or update assets in a workspace
• Configure a dataset scheduled refresh
• Manage datasets
• Assign workspace roles

Understanding functional and technical aspects of Exam DA-100: Analyzing Data with Microsoft Power BI Model the data

The following will be discussed in MICROSOFT DA-100 exam dumps:

• Define role-playing dimensions
• Create calculated tables
• Optimize model performance
• Create calculated columns
• Create hierarchies
• Develop a data model
• Remove unnecessary rows and columns
• Use basic statistical functions to enhance data
• Resolve many-to-many relationships
• Define quick measures
• Identify poorly performing measures, relationships, and visuals
• Create a common date table
• Use CALCULATE to manipulate filters
• Design the data model to meet performance requirements
• Improve cardinality levels through summarization
• Create measures by using DAX
• Use DAX to build complex measures
• Implement row-level security roles
• Define the tables
• Define a relationship's cardinality and cross-filter direction
• Design a data model
• Define the appropriate level of data granularity
• Replace numeric columns with measures
• Flatten out a parent-child hierarchy
• Implement Time Intelligence using DAX
• Apply cross-filter direction and security filtering
• Create semi-additive measures
• Configure table and column properties
• Set up the Q&A feature
• Improve cardinality levels by changing data types
• Create and manage aggregations

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