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How to Prepare for Exam DA-100: Analyzing Data with Microsoft Power BI

Preparation Guide for Exam DA-100: Analyzing Data with Microsoft Power BI

Introduction for Exam DA-100: Analyzing Data with Microsoft Power BI

Analyzing Data with Microsoft Power BI certification demonstrates your expertise in analyzing data with both Power BI. This certification is for data analysts who are responsible for designing and building scalable data models, cleaning, transforming, and analyzing data. The exam includes questions about Data preparation, tools, queries and tables, visualization types and support, dashboards creation. Data Analysts also collaborate with IT and business teams to ensure that data analysis is providing business value.

ValidExam offers detailed exam dumps and MICROSOFT DA-100 practice exams and MICROSOFT DA-100 practice test to prepare candidates, who are going to be data analysts in the future, to prepare and pass this exam in the first go.

Exam DA-100: Analyzing Data with Microsoft Power BI will allow you to learn:

Perform Quick Data Analysis with Power BI

With the help of Power BI, you can collect your company's data, whether it's located in the cloud or locally, and provides quick and easy access to this data - all without having to ask any human being for help.

Generate Accurate Interactive reporting

Your customers can see all data on one screen and the Power BI Mobile apps are automatically updated with changes to your data so you can access updated data and reports, no matter where you are.

Financial overview

Companies of a certain size can benefit from Power BI's analytical reporting features and the ability to access their data across multiple devices.

How Power BI is user friendly

With the help of Power BI innovative tools, you can easily find out what data you want to see and how the results will look like. The tool allows you to switch between different formats in order to give you a more visual representation of your data. Power BI can also connect to a wide variety of databases and information sources, including cloud-based sources such as Azure SQL Database, Oracle, Salesforce.com, Amazon Redshift, Dropbox Business, Google Analytics Premium, and Tableau Online.

Power BI Data Visualization

Power BI makes productive and creative. You no longer need to go through complex processes without leaving the Power BI interface. The benefit of this process is that you can, as a company, focus on the data and actions of your customers rather than creating lengthy reports and documents to satisfy them. It combines data from multiple sources into a single, standardized, format.

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About Data Analyst Associate and Who It Is Intended for

The Microsoft Certified: Data Analyst Associate is earned by passing DA-100 and is a role-based intermediate-level certification that falls under the Microsoft Power Platform certification hierarchy and authenticates the data analyst's expertise in utilizing Microsoft Power BI in maximizing the businesses' data assets value. The earner of this certificate will be competent to design and develop scalable data models along with cleaning and transformation of data to provide business values through data visualizations. Overall, this certification will be beneficial for data analysts in broadening their technological prowess and the values that they provide for businesses.

Microsoft DA-100 Practice Test Questions, Microsoft DA-100 Exam Practice Test Questions

The candidates who want to earn the Microsoft Certified: Data Analyst Associate certification must pass one qualifying exam, which is Microsoft DA-100. This test evaluates the ability of the test takers to accomplish specific technical tasks. These include the processes with data, such as analyzing, preparing, maintaining and deploying, visualizing, and modeling.

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

Microsoft DA-100 Exam Syllabus Topics:

Section	Weight	Objectives
Model the data	30%	- Create measures by using DAX 1. Use DAX aggregation functions 2. Create calculated measures 3. Create semi-additive measures 4. Implement Time Intelligence using DAX 5. Use DAX logical functions - Develop a data model 1. Create calculated tables 2. Create calculated columns 3. Create hierarchies 4. Implement row-level security roles - Design a data model 1. Define a relationship's cross-filter direction and security filtering 2. Configure table and column properties 3. Define quick measures 4. Design and implement role-playing dimensions 5. Define the tables - Optimize model performance 1. Optimize Power Query transformations 2. Improve performance by choosing optimal data types 3. Improve performance by identifying and removing unnecessary rows and columns
Visualize the data	25%	- Create reports 1. Add visualization items to reports 2. Create and manage bookmarks 3. Choose an appropriate visualization type 4. Import a custom visual 5. Format and configure visualizations 6. Apply slicing and filtering 7. Configure conditional formatting - Create dashboards 1. Manage tiles on a dashboard 2. Set mobile view 3. Configure data alerts 4. Use the Q&A feature - Enforce accessibility 1. Design accessible reports 2. Configure tab order 3. Use accessibility features
Analyze the data	15%	- Perform advanced analysis 1. Identify outliers 2. Use the Analyze feature 3. Perform What-if analysis 4. Use advanced analytical custom visuals - Enhance reports for usability and storytelling 1. Apply sorting 2. Edit and configure interactions between visuals 3. Configure navigation for a report 4. Configure sync slicers 5. Configure bookmarks 6. Group and layer visuals 7. Create custom tooltips
Deploy and maintain deliverables	10%	- Create and manage workspaces 1. Configure and update a workspace app 2. Assign workspace roles 3. Create and configure a workspace - Manage datasets 1. Configure cross-workspace sharing 2. Provide access to datasets 3. Configure a dataset scheduled refresh 4. Configure row-level security group membership
Prepare the data	20%	- Transform and load the data 1. Define relationship cardinality and cross-filter direction 2. Create and transform tables 3. Design a star schema 4. Create and transform columns 5. Select appropriate column data types - Profile the data 1. Identify data anomalies 2. Examine data structures 3. Interpret data distribution and variance - Clean the data 1. Resolve inconsistencies, unexpected or null values, and data quality issues 2. Resolve data import errors 3. Evaluate data, including data statistics and column properties - Get data from different data sources 1. Select a storage mode 2. Select a shared dataset or create a local dataset 3. Identify and connect to a data source 4. Choose an appropriate Power BI data source 5. Change data source settings