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Microsoft 070-773 valid exam - in .pdf Free Demo

  • Exam Code: 070-773
  • Exam Name: Analyzing Big Data with Microsoft R
  • Last Updated: Aug 02, 2026
  • Q & A: 40 Questions and Answers
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  • PDF Price: $49.98    

Microsoft 070-773 valid exam - Testing Engine PC Screenshot

  • Exam Code: 070-773
  • Exam Name: Analyzing Big Data with Microsoft R
  • Last Updated: Aug 02, 2026
  • Q & A: 40 Questions and Answers
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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.

Overview about MICROSOFT 70-773 Exam

  • Length of Examination: 150 minutes
  • Passing Score: 70-80%
  • Registration Fee: 165 USD
  • Number of Questions: 40-60
  • Format: Multiple choice, multiple answer

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

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Microsoft 070-773 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Deploy and Operationalize R Solutions15%- Run R scripts in Hadoop/Spark environments
- Manage scalable analytics workflows
- Integrate R with SQL Server R Services
Topic 2: Manage Data Sets20%- Read and write data using Microsoft R Server
- Transform and clean data
- Handle large datasets beyond memory
Topic 3: Build Statistical and Machine Learning Models25%- Process text data with RML packages
- Train linear and generalized linear models
- Use partitioning and parallelized models
Topic 4: Evaluate and Optimize Models20%- Optimize R scripts for scale and speed
- Assess model performance
- Tune model parameters
Topic 5: Explore and Visualize Data20%- Summarize and aggregate data
- Create visualizations using R and RevoScaleR
- Perform exploratory data analysis

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