Microsoft Analyzing Big Data with Microsoft R : 070-773

  • Exam Code: 070-773
  • Exam Name: Analyzing Big Data with Microsoft R
  • Updated: Aug 03, 2026
  • Q & A: 40 Questions and Answers

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About Microsoft Analyzing Big Data with Microsoft R : 070-773 Exam

Analyzing Big Data with Microsoft R 70-773 Dumps will include below mentioned topics with Exam focused percentage

  • Use R Server in different environments: 20-25%
  • Read and explore big data: 20-25%
  • Build predictive models with Scale R: 20-25%
  • Process big data: 20-25%
  • Implement Domain Name System (DNS): 15-20%

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Analyzing Big Data with Microsoft R 70-773 Dumps Provided Study Notes

TorrentVCE expert team recommend you to prepare some notes on these topics along with it don't forget to practice Analyzing Big Data with Microsoft R 70-773 Dumps which been written by our expert team, Both these will help you a lot to clear this exam with good marks.

  • Data Processing with RxDataStep
  • Spark
  • DataSets
  • Hadoop
  • Linear Model, Partitioning Models
  • Read Data with R Server
  • Work Flow Analysis
  • Transform Functions

Reference: https://www.microsoft.com/en-us/learning/exam-70-773.aspx

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MICROSOFT 70-773 Exam certification will not only help you learn some new skills, it can position you for a higher paying job or help you transform your current role into a Big Data and Analytics professional. Get prepared to take this exam and become one of the very first Microsoft Big Data Certified professionals in the world using our relevance quality of MICROSOFT 70-773 Exam study material and so we bring best-in-industry MICROSOFT 70-773 Exam online course and practice exams for you to help in your exam preparation.

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MICROSOFT 70-773 Exam Reference, Microsoft Documents

Microsoft 070-773 Exam Syllabus Topics:

SectionObjectives
Topic 1: Integrate R with Microsoft data platforms- Use R in Azure Machine Learning environments
- Use R with SQL Server
Topic 2: Perform data analysis with R- Descriptive statistics and exploratory data analysis
- Apply statistical methods in R
- Work with large datasets
Topic 3: Visualize and communicate results- Data visualization with R packages (ggplot2, etc.)
- Reporting and dashboards
Topic 4: Build and evaluate predictive models- Regression and classification models
- Feature engineering in R
- Model training and validation
Topic 5: Prepare and clean data for analysis- Import and transform data using R
- Clean and preprocess datasets
- Handle missing and inconsistent data

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