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Microsoft 70-773 Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Analyzing Big Data with Microsoft R |
| Exam Number: | 70-773 |
| Available Languages: | English |
| Passing Score: | 700 out of 1000 |
| Certificate Validity Period: | Retired, no longer active |
| Real Exam Qty: | 40–60 |
| Exam Duration: | 150–180 |
| Related Certifications: | MCSA: Machine Learning Microsoft Certified Professional |
| Exam Price: | 165 USD |
| Exam Format: | Multiple answer, Case studies, Drag and drop, Multiple choice |
| Recommended Training: | Microsoft Official Course 20773: Analyzing Big Data with Microsoft R |
| Exam Registration: | Microsoft Exam Registration |
| Sample Questions: | ![]() |
| Exam Way: | Online proctored or at authorized Pearson VUE test centers |
| Pre Condition: | Basic knowledge of R programming, statistics, data structures, and familiarity with SQL Server, Hadoop, or big data platforms |
| Official Syllabus URL: | https://www.microsoft.com/en-us/learning/exam-70-773.aspx |
Microsoft 70-773 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Evaluate and Optimize Models | 20% | - Tune model parameters - Assess model performance - Optimize R scripts for scale and speed |
| Topic 2: Manage Data Sets | 20% | - Handle large datasets beyond memory - Transform and clean data - Read and write data using Microsoft R Server |
| Topic 3: Deploy and Operationalize R Solutions | 15% | - Manage scalable analytics workflows - Integrate R with SQL Server R Services - Run R scripts in Hadoop/Spark environments |
| Topic 4: Build Statistical and Machine Learning Models | 25% | - Use partitioning and parallelized models - Process text data with RML packages - Train linear and generalized linear models |
| Topic 5: Explore and Visualize Data | 20% | - Create visualizations using R and RevoScaleR - Perform exploratory data analysis - Summarize and aggregate data |
70-773 Exam FAQ: What Candidates Ask About Microsoft Analyzing Big Data with Microsoft R
The 70-773 exam, officially known as Microsoft Analyzing Big Data with Microsoft R, is the Microsoft test that leads to the MCSA: Machine Learning certification at the Professional level. Passing it validates the skills employers expect from a certified professional. It is also associated with related credentials such as MCSA: Machine Learning, Microsoft Certified Professional.
The 70-773 exam contains 40–60 questions, and you have 150–180 to complete them. Work out your per-question pace before test day, and flag slow items instead of stalling on them — time pressure, not knowledge, sinks many first attempts. Timed mock exams in the Actual4Exams test engines are the most reliable way to build that rhythm.
The passing score for the 70-773 exam is 700 out of 1000, and the official registration fee is 165 USD. If you miss the mark, a retake means paying the full fee again, so book your seat only when you are ready. A practical benchmark: score consistently above the passing line on timed practice tests before scheduling the real exam.
Basic knowledge of R programming, statistics, data structures, and familiarity with SQL Server, Hadoop, or big data platforms
Entry requirements can change, so confirm the latest conditions on the official exam page: https://www.microsoft.com/en-us/learning/exam-70-773.aspx.
You can book the 70-773 exam through the official registration channels below:
Exam delivery: Online proctored or at authorized Pearson VUE test centers. Seats at popular test centers fill quickly, so schedule early once your preparation is on track.
Microsoft recommends the following training options for Microsoft Analyzing Big Data with Microsoft R candidates:
Pair any course with the 40 practice questions from Actual4Exams to measure how ready you really are before paying the exam fee.
Yes. A free PDF demo of the Microsoft Analyzing Big Data with Microsoft R questions is available, so you can check the question style and answer quality before you pay. Every purchase also includes 365 days of free updates, and if the product expires you can renew the update service at a 50% discount from your member zone.
If you take the corresponding 70-773 exam within 60 days of purchase and do not pass, you can apply for a full refund under the 100% Money Back Guarantee: submit a scan of your enrollment slip and your official Score Report (PDF) within 2 days of the exam date, and the claim is processed within 7 days. Attempts made within 3 days of purchase, downloads without an actual exam attempt, free materials, and expired orders are not eligible, and the candidate name must match the payer name. Prefer new material instead of a refund? You can exchange your purchase for two free products of equal value and keep the update service on your original product. As for delivery, the files are available for instant download and are also emailed to you within one minute of payment — if nothing arrives within 2 hours, contact customer service. There is no limit on how many computers you can install the product on.
The official Microsoft Analyzing Big Data with Microsoft R outline is organized into 5 domains. The first three are:
- Build Statistical and Machine Learning Models — 25% of the exam
- Evaluate and Optimize Models — 20% of the exam
- Deploy and Operationalize R Solutions — 15% of the exam
See the complete exam topics section above for the full outline and the weighting of every domain.
Microsoft Analyzing Big Data with Microsoft R Sample Questions:
HOTSPOT
Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.
Start of repeated scenario
You are developing a Microsoft R Open solution that will leverage the computing power of the database server for some of your datasets.
You are performing feature engineering and data preparation for the datasets.
The following is a sample of the dataset.
End of repeated scenario.
You plan to score some data to create data features to address empty rows.
You have the following R code.
You need to transform the data and overwrite the current dataset.
Which R code segment should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Note: This question is part of a series of questions that use the same or similar answer choices. An answer choice may be correct for more than one question in the series. Each question is independent of the other questions in this series. Information and details provided in a question apply only to that question.
You need to estimate a model where the outcome variable is continuous, is in the range of
[0, inf], and has a substantial mass at an exact value of 0.
Which function should you use?
- A. rxLinMod
- B. summary
- C. rxTweedie
- D. rxTransform
- E. rxDataStep
- F. rxPredict
- G. stepAic
- H. rxLogit
You are running a parallel function that uses the following R code segment. (Line numbers are included for reference only.)
0 1 cp <- 0.01 xval <- 0 maxdepth <- 5
0 2
0 3 (form, data = "segmentationDataBig", maxDepth = maxdepth, cp = cp, xval = xval, blocksPerRead = 250 You need to complete the R code. The solution must support chunking.
Which function should insert at line 02?
- A. rxBTrees
- B. rxExec
- C. rxDTree
- D. rxDForest
You have a dataset that has multiple blocks and only numeric variables.
You are computing in a local compute context.
You plan to lag a variable named x to create a new variable named x_lagged by using a transform function. You will create a new element in the output of the function.
You need to minimize the number of missing values.
Which three actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. Assign a value to the first value of x_lagged in the current block.
- B. Use rxGet to retrieve the first value of x in the next block to be processed.
- C. Use rxGet to retrieve a value stored in processing of the prior block.
- D. Use rxSet to store the last value of x in the current block.
- E. Use rxSet to store the last value of x_lagged in the current block.
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