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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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Analyzing Big Data with Microsoft R 70-773 Exam been designed in order to test expertise and technical knowledge in designing and implementing Microsoft services in order to obtain value from the available data set. It examines the candidate's ability to execute Microsoft Big Data Services according to architecture practices. The Analyzing Big Data with Microsoft R 70-773 Exam is high level exam and it requires in-depth knowledge of data analytics concepts, before you go ahead and write this certification you must require some prerequisites Candidates for this exam are data scientists or analysts who process and analyze data sets larger than memory using R.
Candidates should have experience with R, familiarity with data structures, familiarity with basic programming concepts (such as control flow and scope), and familiarity with writing and debugging R functions. Candidates should be familiar with common statistical methods and data analysis best practices. Candidates should also have a high-level understanding of data platforms, such as the Hadoop ecosystem, SQL Server, and core T-SQL capabilities. We think our Analyzing Big Data with Microsoft R 70-773 Exam Practice Test Paper and Dumps will provide you 100% confidence to make you appear for Microsoft 70-773 Exam
This is the list of the contents in our Analyzing Big Data with Microsoft R 70-773 Practice Test:
Reference: https://www.microsoft.com/en-us/learning/exam-70-773.aspx
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| Section | Objectives |
|---|---|
| Topic 1: Build Predictive Models with ScaleR | - Create additional models using MicrosoftML packages - Evaluate models and tuning parameters - Build and use partitioning models - Generate predictions and residuals - Estimate linear models |
| Topic 2: Read and Explore Big Data | - Visualize data - Summarize data - Read data with R Server |
| Topic 3: Use R Server in Different Environments | - Perform in-database analytics using SQL Server - Deploy predictive models to SQL Server and Azure Machine Learning - Use different compute contexts to run R Server effectively - Optimize tasks using local compute contexts - Implement analysis workflows in Hadoop and Spark |
| Topic 4: Process Big Data | - Process data with rxDataStep - Process text using MicrosoftML packages - Perform complex transforms using transform functions - Manage data sets |
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