Analyzing Data with Microsoft Power BI (DA-100 Deutsch Version): DA-100 Exam
"Analyzing Data with Microsoft Power BI (DA-100 Deutsch Version)", also known as DA-100 exam, is a Microsoft Certification. With the complete collection of questions and answers, ExamcollectionPass has assembled to take you through 150 Q&As to your DA-100 Exam preparation. In the DA-100 exam resources, you will cover every field and category in Microsoft Certified: Data Analyst Associate Certification helping to ready you for your successful Microsoft Certification.
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2. Data Model (25-30%):
The main domains of this section are:
- Model performance optimization: This module will measure your competence in enhancing cardinality levels through changing data types and summarization. It will also evaluate your skills in eliminating unnecessary columns and rows, creating and managing aggregations, and identifying poorly performing visuals, measures, and relationships.
- Data model design: The learners need to have competence in defining tables, role-playing dimensions, quick measures, and cross-filter direction & relationship cardinality. The topic also requires their skills in designing a data model to fulfill the performance prerequisites, creating common data tables, and solving many-to-many relationships.
- Creation of measures with DAX: The applicants should be able to utilize DAX in building multifaceted measures, use CALCULATE to maneuver filters, and utilize the fundamental statistical functions for enhancing data. This domain also requires their skills in creating semi-additive measures and implementing Time Intelligence with DAX.
- Data model development: This objective requires one’s ability to create hierarchies, calculated tables, and calculated columns. It also covers your skills in establishing Q&A features, implementing row-level security functions, and applying security filtering and cross-filter direction.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/da-100
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How can you prepare for the Microsoft DA-100 exam to succeed?
The candidates can explore various resources to prepare for the Microsoft DA-100 exam. The Microsoft Learning Platform offers many study tools that the learners can use to achieve success in their test. The first step in the preparation process is to go over the detailed exam topics. After that, you can choose your resources carefully. Here are the official recommended materials for the DA-100 test:
- Books
The recommended book resource for this test is Exam Ref: DA-100 Analyzing Data with Microsoft Power BI by Daniil Maslyuk. This is a professional-level resource to help the learners optimize their performance in the test and improve their skills. The book can be found on the Microsoft Press Store.
- Instructor-Led Training
Microsoft recommends the Analyzing Data with Power BI training course for the candidates taking the DA-100 test. This training focuses on different best practices and methods associated with the technical and business requirements for visualizing, analyzing, and modeling data with Power BI. The individuals will learn how to process and access data from different data sources, including non-relational and relational data.
- Free Online Training
The applicants who prefer self-paced study can explore the learning paths for the Microsoft DA-100 exam. There are six learning paths for this certification test, including Getting Started with Microsoft Data Analytics, Prepare Data for Analysis, Model Data in Power BI, and Visualize Data in Power BI, among others. Each path contains different modules that address particular topics of the exam.
- Practice Test
Microsoft also offers the official practice test for Microsoft DA-100. The exam questions cover all the objectives of the actual test. This means that you will be well prepared for any questions that may be administered in the exam.
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Microsoft DA-100 Deutsch Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Deploy and maintain deliverables | 10% | - Manage datasets
|
| Model the data | 30% | - Develop a data model
|
| Prepare the data | 20% | - Transform and load the data
|
| Analyze the data | 15% | - Perform advanced analysis
|
| Visualize the data | 25% | - Create reports
|
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