70-773 Exam Guide: Analyzing Big Data with Microsoft R
Exam 70-773, “Analyzing Big Data with Microsoft R,” was part of Microsoft’s earlier 70-xxx exam program and focused on analyzing large-scale data with Microsoft R. Microsoft’s role-based certification mapping associated it with the Azure Data Scientist certification and exam DP-100. This guide helps candidates make the important decision first: verify whether they are pursuing a historical credential or should prepare for the mapped role-based route instead, then organize study around practical data-science work rather than relying on memorized answers.
What did 70-773 validate?
70-773 validated knowledge represented by its official title: analyzing big data with Microsoft R. It was intended for candidates working with data-analysis and data-science tasks in the Microsoft ecosystem, particularly those whose experience included R-based analysis of substantial datasets. The available official material does not provide a current detailed skills outline or percentage blueprint for this historical exam.
The exam’s name is useful, but it should not be treated as a complete syllabus. It identifies the central technology and activity without specifying every command, service, scenario, or performance level assessed. Build your preparation around the underlying work: preparing data, selecting analytical methods, using R appropriately, interpreting results, and explaining conclusions.
Who should consider the subject matter?
The strongest fit is a learner who already works with structured or large datasets and wants to understand how Microsoft R was used for analysis. Data analysts, aspiring data scientists, developers supporting analytical workloads, and technically oriented professionals with R exposure are plausible audiences. The official mapping does not state a formal prerequisite, so do not assume one from job titles alone.
What the title does not prove
The title does not establish the exam’s question count, duration, languages, delivery method, passing score, or current availability. None of those details are supplied in the verified research for 70-773. Treat third-party pages that present such values as current facts with caution and confirm any scheduling information through Microsoft’s official certification resources.
Check the credential path before studying
The first preparation task is a status check, not a practice test. Microsoft’s mapping article identifies 70-773 as “Analyzing Big Data with Microsoft R” and maps it to Microsoft Certified: Azure Data Scientist, earned through DP-100. Because the mapping was published on July 9, 2019, use it as historical transition guidance and confirm the present path before investing in exam-specific materials.
How Microsoft mapped 70-773
Microsoft published its 70-xxx mapping as part of the move toward role-based certifications. In that table, 70-773 is paired with Azure Data Scientist and DP-100. The same table also maps 70-774, “Perform Cloud Data Science with Azure Machine Learning,” to the same certification, which shows that the replacement path is broader than a direct title change.
A practical go-or-switch decision
If Microsoft Learn or the authorized scheduling system still presents 70-773 as available to you, check the associated terms and dates before booking. If it does not, stop collecting 70-773-specific material and use the mapped DP-100 route as your planning reference. Do not assume that passing an old exam automatically grants a current certification; confirm the rule with Microsoft.
Why retirement status matters
Microsoft explains that exams are retired when they no longer reflect relevant skills or technologies. After an exam retires, candidates cannot take it or earn the associated certification or credential through that exam. The retirement page also says that an already-earned certification remains on the candidate’s Microsoft Learn transcript. These are official policy points, not evidence that 70-773 has a particular retirement date.
What to study when no blueprint is available
Without a verified 70-773 objective list, use a capability map instead of inventing domain weights. Divide your work into data preparation, R analysis, large-scale processing concepts, model or method selection, result interpretation, and communication. Mark each topic as reading-only, guided practice, or independent practice, then spend most of your time on the last category.
Data preparation and quality
Practice inspecting data types, missing values, inconsistent categories, duplicate records, outliers, and unsuitable fields. For every cleaning decision, write down why it is appropriate and what information it may remove. Large datasets magnify small quality problems, so an analysis that runs successfully can still produce an unreliable conclusion.
R analysis habits
Work in R rather than only reading syntax references. Reproduce a small analysis from import through summary and visualization, then alter the data and explain what changed. Keep scripts readable, separate preparation from analysis, and record assumptions. The goal is to demonstrate controlled reasoning, not to memorize isolated function names.
Scale and resource decisions
When a dataset is too large for a simple local workflow, identify what must move, what can be summarized, and what should be processed in stages. Compare the cost of sampling with the risk of losing rare cases. Also consider reproducibility, data movement, and operational constraints. These are practical recommendations for studying the subject, not verified 70-773 blueprint items.
Interpreting and communicating findings
A technically correct calculation is not enough. Practice stating the population represented, the evidence used, the uncertainty or limitation, and the decision the result supports. Create short written explanations for charts and model outputs. This habit helps prevent a common failure mode: selecting a plausible result without understanding its business or analytical meaning.
Build a hands-on practice environment
Use a small, reproducible dataset first and increase its size only after the workflow is correct. Keep the raw data unchanged, create a preparation script, save intermediate outputs deliberately, and document the analytical question. This setup lets you diagnose whether a problem comes from data quality, code, resource limits, or interpretation.
A repeatable exercise pattern
For each exercise, follow six steps: define the question, inspect the data, prepare a documented working set, run the analysis, validate the result, and explain the limitation. Repeat the same pattern with a different dataset or business question. Consistency matters because it exposes gaps that a one-off tutorial can hide.
Use controlled variations
Change one factor at a time: missing-value treatment, feature selection, sample size, aggregation level, or analytical method. Compare the outputs and record the reason for the difference. This is more valuable than copying a finished notebook because it develops the judgment needed to recognize when a familiar technique is inappropriate.
Keep an evidence log
Maintain a table with the task, technique, input assumptions, output, validation check, and remaining uncertainty. Add links to the Microsoft documentation you used. The log becomes a targeted revision list and helps you distinguish something you can perform independently from something you merely recognize in a glossary.
A study sequence that avoids wasted effort
Study in dependency order: understand the analytical question, prepare trustworthy data, perform the R workflow, address scale, and then interpret and communicate the result. Starting with advanced terminology or random question banks creates false confidence because later decisions depend on earlier data and method choices.
Phase one: establish the baseline
Begin by writing what you can do without notes. Include importing data, examining structure, transforming fields, summarizing variables, visualizing relationships, and explaining an analytical conclusion. Do not grade yourself by familiarity with words. Grade yourself by whether you can complete a clean workflow and justify each major choice.
Phase two: close specific gaps
Turn each weak area into a short practical assignment. If transformation is weak, prepare several messy columns. If interpretation is weak, explain charts without discussing code first. If scale is weak, compare a full dataset with a deliberately reduced working set and document what may be lost.
Phase three: integrate the workflow
Complete end-to-end case exercises under a self-imposed time limit, but do not mistake speed for an official exam duration. Review the result for reproducibility, leakage, unsupported assumptions, and unexplained outputs. A strong final exercise should leave behind a script, a concise explanation, and a list of limitations.
Phase four: make the scheduling decision
Only schedule after confirming that the exam is available, the registration account is correct, and the official page describes the current experience you will receive. If the legacy exam cannot be booked, redirect effort to the mapped role-based certification rather than trying to find a substitute from an unofficial catalogue.
Common preparation mistakes
Most avoidable mistakes come from preparing for an assumed exam rather than the verified one. Candidates often confuse the historical 70-773 title with the current Azure Data Scientist route, study only syntax, or treat unverified third-party claims as an official blueprint. A disciplined status check and skills-based practice prevent these errors.
Mistake: treating the mapping as a live exam page
The mapping article explains how older exams aligned with newer certifications; it is not, by itself, evidence of current scheduling availability, delivery details, or a current DP-100 objective list. Use it to identify the relationship, then follow the current Microsoft Learn certification and exam pages for present requirements.
Mistake: memorizing code without diagnosis
Recognizing a function name does not show that you can identify a malformed field, select a defensible transformation, or interpret an unexpected result. For each technique, practice a failure case and explain how you detected it. That turns passive recognition into transferable troubleshooting skill.
Mistake: ignoring scale assumptions
A method that works on a small demonstration file may become impractical or misleading at larger scale. Record memory, processing, sampling, and data-transfer assumptions during practice. Do not claim that a particular platform or architecture is required for 70-773 unless an official objective confirms it.
Mistake: using dumps as a substitute for preparation
Exam dumps and leaked-question claims are not a reliable or appropriate way to establish competence. They can be outdated, unauthorized, and detached from the skills the credential is meant to represent. Use official study information, documentation, hands-on exercises, and legitimate practice assessments where Microsoft provides them.
How to verify scheduling and delivery information
Do not publish or rely on a fixed price, duration, language list, question count, or delivery format for 70-773 from the evidence available here. The official research supplies those details for a different AI Transformation Leader assessment, not for 70-773. Check Microsoft Learn and the authorized scheduling provider immediately before registering because historical exam information can persist after a program transition.
A registration checklist
Confirm the exact exam number and title, verify that the exam can be scheduled, review the current exam policy, and ensure the Microsoft account used for registration is the one connected to your certification profile. Save the official confirmation and check the cancellation or rescheduling terms shown at booking.
What to do if the exam is unavailable
Do not infer that a third-party appointment page makes the exam current. Return to Microsoft’s official retirement and certification resources. If the legacy route is closed, compare your goal with the mapped Azure Data Scientist and DP-100 path, including its current skills outline and requirements, before choosing a new study plan.
What happens to proof of an earned credential
If you already earned the relevant credential, Microsoft provides a Certification Dashboard for certificates, badges, and transcripts. The transcript can be downloaded or shared using a transcript ID and access code. This is separate from deciding whether a candidate can still sit 70-773, so preserve your records even if the legacy exam is no longer offered.
Certificate, badge, and transcript uses
Microsoft describes certificates as proof of accomplishments, badges as a way to share skills on professional profiles, and the transcript as a record that can be emailed, downloaded as a PDF, or shared through a generated URL. Check the dashboard rather than assuming an old credential has disappeared because its exam is no longer scheduled.
A useful record-keeping step
Download or securely record the credential information associated with your own Microsoft account, and confirm that your name and profile details are correct. Do not create a new account merely to register for a related path without first checking how Microsoft will associate the achievement with your transcript.
A practical final-week plan
In the final week, stop expanding the syllabus and test the workflow you intend to use. Recheck the official status, review your evidence log, complete a few representative analyses, and explain the results without notes. The purpose is to expose unresolved gaps and administrative problems while there is still time to correct them.
Several study sessions before the appointment
Review weak tasks in priority order, not in the order you encountered them. Rebuild one analysis from raw input, inspect every transformation, and validate the final result. Then complete a second exercise with a different question so that your preparation is not tied to one dataset or tutorial.
The last review session
Use brief notes containing concepts, decision rules, and personal error patterns. Avoid attempting to memorize an entire reference library. Confirm the appointment information and the identity or account details required by the official provider. If the exam is not verifiably available, postpone the booking decision and investigate the mapped route.
After an unsuccessful attempt
Treat a failed attempt as a signal to diagnose skills, not as a reason to search for recalled questions. Review the official retake policy that applies to the exam you actually took, identify the weakest capability areas, and create new practice exercises around those gaps before considering another appointment.
Your next actions
Start with three actions: verify whether 70-773 can still be scheduled, read Microsoft’s mapping of 70-773 to Azure Data Scientist and DP-100, and inventory your ability to perform an R-based analysis from raw data to justified conclusion. Those checks will tell you whether to pursue the legacy exam, switch paths, or strengthen fundamentals first.
If you are researching the old exam
Record the official title and mapping, then avoid treating catalogue pages as current requirements. Use Microsoft’s retirement policy to understand the consequences of an unavailable exam, and consult the current certification pages for any replacement route. Keep a note of what is verified, what is historical, and what still needs confirmation.
If you are moving to the mapped route
Use DP-100’s current Microsoft Learn study guide as the authority for objectives, technologies, assessment format, and scheduling. Reuse your R fundamentals where they remain relevant, but do not assume that preparation for 70-773 covers every requirement of a newer role-based exam. Build a fresh gap analysis from the current outline.
Conclusion
70-773 is best approached as a historical Microsoft R and big-data analysis exam whose official mapping points toward the Azure Data Scientist pathway through DP-100. The responsible preparation choice is therefore two-stage: verify availability and current rules through Microsoft, then study through reproducible analytical work rather than unofficial question collections. If the legacy route is closed, use the current mapped certification requirements as the basis for a new plan and preserve any previously earned credential through the Microsoft Certification Dashboard.
