Real Microsoft 70-773 practice exam questions for easy pass!
Updated: Sep 09, 2026
No. of Questions: 40 Questions & Answers with Testing Engine
Download Limit: Unlimited
Our Microsoft 70-773 study material is researched and written by the experts who acquaint with the knowledge in the actual test. The accurate and verified answers can help you prepare well for the actual test. Besides, you can try 70-773 free demo questions to assess the validity of it.
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Reference: https://www.microsoft.com/en-us/learning/exam-70-773.aspx
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Analyzing Big Data with Microsoft R |
| Exam Number: | 70-773 |
| Real Exam Qty: | 40-60 |
| Exam Duration: | 120 minutes |
| Certificate Validity Period: | Certification retired (Exam retired June 30, 2019) |
| Passing Score: | 700/1000 |
| Exam Price: | USD 165 |
| Related Certifications: | MCSA: Machine Learning |
| Available Languages: | English |
| Exam Format: | Multiple Response, Scenario-based Questions, Case Study, Multiple Choice |
| Sample Questions: | Microsoft 70-773 Sample Questions |
| Exam Way: | Pearson VUE testing center or online proctored delivery (before retirement). |
| Pre Condition: | No mandatory prerequisite exam. This exam was one of the required exams for the MCSA: Machine Learning certification path. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/posts/mapping-microsoft-70-xxx-exams-to-new-role-based-certifications |
It is one of the toughest exam and it requires you a lot of hands-on experience. It is advisable to have prior knowledge of Big Data technologies on Microsoft. The more experience you have, the more it is beneficial for you, Major challenging things which you can find in exam is about different scenario based questions, you must have strong understanding of Big Data.
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| Section | Objectives |
|---|---|
| Topic 1: Read and Explore Big Data | - Read data with R Server - Visualize data - Summarize data |
| Topic 2: Use R Server in Different Environments | - Use different compute contexts to run R Server effectively - Perform in-database analytics using SQL Server - Deploy predictive models to SQL Server and Azure Machine Learning - Implement analysis workflows in Hadoop and Spark - Optimize tasks using local compute contexts |
| Topic 3: Process Big Data | - Process data with rxDataStep - Perform complex transforms using transform functions - Process text using MicrosoftML packages - Manage data sets |
| Topic 4: Build Predictive Models with ScaleR | - Generate predictions and residuals - Create additional models using MicrosoftML packages - Evaluate models and tuning parameters - Build and use partitioning models - Estimate linear models |
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