Microsoft DA-100 exam is a great opportunity for data professionals to validate their skills and advance their careers. Analyzing Data with Microsoft Power BI certification helps professionals stand out in the job market, demonstrating to employers that they have the technical expertise and knowledge to analyze business data and create insightful reports. Additionally, the certification can lead to better job opportunities, higher salaries, and more rewarding career paths.
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Exam Details
Microsoft DA-100 is an associate-level certification exam, which means that irrespective of your current hands-on experience in the subject area, you can still work towards passing this test and obtaining the certificate. All you need is a thorough understanding of the exam topics and diligent preparation.
The DA-100 exam contains 40-60 questions, covering different types, such as case studies, multiple choice, drag and drop, active screen, and build list, among others. This test can be scheduled using Pearson VUE, the official administrator of the Microsoft certification exams. When you are ready to register for this test, you have to pay the fee of $165. This applies to a single sitting for the exam. If you do not get the required passing score that is 720 points, you must pay a new fee to retake the test.
Microsoft DA-100: Analyzing Data with Microsoft Power BI certification is highly valued in the data analysis industry. Analyzing Data with Microsoft Power BI certification is recognized globally and is considered a valuable asset for professionals working in data analysis and visualization. Analyzing Data with Microsoft Power BI certification demonstrates the candidate's ability to work with Microsoft Power BI and validates their proficiency in using the tool to analyze data and create reports.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/da-100
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Microsoft DA-100 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Prepare the data | 20% | - Get data from different data sources
- 1. Change data source settings
- 2. Identify and connect to a data source
- 3. Select a storage mode
- 4. Choose an appropriate Power BI data source
- 5. Select a shared dataset or create a local dataset
- Transform and load the data
- 1. Design a star schema
- 2. Define relationship cardinality and cross-filter direction
- 3. Create and transform columns
- 4. Select appropriate column data types
- 5. Create and transform tables
- Profile the data
- 1. Examine data structures
- 2. Interpret data distribution and variance
- 3. Identify data anomalies
- Clean the data
- 1. Evaluate data, including data statistics and column properties
- 2. Resolve inconsistencies, unexpected or null values, and data quality issues
- 3. Resolve data import errors
|
| Analyze the data | 15% | - Enhance reports for usability and storytelling
- 1. Configure sync slicers
- 2. Create custom tooltips
- 3. Group and layer visuals
- 4. Configure bookmarks
- 5. Edit and configure interactions between visuals
- 6. Configure navigation for a report
- 7. Apply sorting
- Perform advanced analysis
- 1. Use the Analyze feature
- 2. Use advanced analytical custom visuals
- 3. Perform What-if analysis
- 4. Identify outliers
|
| Visualize the data | 25% | - Create dashboards
- 1. Set mobile view
- 2. Configure data alerts
- 3. Manage tiles on a dashboard
- 4. Use the Q&A feature
- Create reports
- 1. Configure conditional formatting
- 2. Choose an appropriate visualization type
- 3. Import a custom visual
- 4. Create and manage bookmarks
- 5. Apply slicing and filtering
- 6. Format and configure visualizations
- 7. Add visualization items to reports
- Enforce accessibility
- 1. Use accessibility features
- 2. Design accessible reports
- 3. Configure tab order
|
| Model the data | 30% | - Develop a data model
- 1. Create calculated tables
- 2. Create calculated columns
- 3. Implement row-level security roles
- 4. Create hierarchies
- 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
- Create measures by using DAX
- 1. Use DAX logical functions
- 2. Create calculated measures
- 3. Implement Time Intelligence using DAX
- 4. Use DAX aggregation functions
- 5. Create semi-additive measures
- Design a data model
- 1. Define a relationship's cross-filter direction and security filtering
- 2. Define quick measures
- 3. Design and implement role-playing dimensions
- 4. Configure table and column properties
- 5. Define the tables
|
| Deploy and maintain deliverables | 10% | - Create and manage workspaces
- 1. Create and configure a workspace
- 2. Assign workspace roles
- 3. Configure and update a workspace app
- Manage datasets
- 1. Configure a dataset scheduled refresh
- 2. Configure row-level security group membership
- 3. Configure cross-workspace sharing
- 4. Provide access to datasets
|