70-773 Analyzing Big Data with Microsoft R Exam Guide
Microsoft identified 70-773 as “Analyzing Big Data with Microsoft R,” an exam associated in its role-based certification mapping with Microsoft Certified: Azure Data Scientist and exam DP-100. That makes the first candidate decision administrative, not technical: verify whether 70-773 is still available before investing in preparation. This guide explains what the exam was intended to validate, who its subject matter suits, how to build a focused study plan from the limited official evidence available, and when a current Azure data-science path is the more practical choice.
What does 70-773 validate?
70-773 was positioned around analyzing big data with Microsoft R. The official Microsoft mapping does not provide a current skills outline, domain weighting, question count, passing score, or delivery specification in the supplied research, so candidates should treat the exam title as the reliable scope boundary rather than assume that a modern certification blueprint applies.
The title points to a data-science task: using R to examine large or complex datasets and produce useful analytical results. It does not, by itself, establish the exact products, commands, statistical methods, or scenario types tested. Those details may have belonged to the historical exam outline, but they are not verified in the supplied official snapshot.
The strongest official context comes from Microsoft’s mapping article, published on July 9, 2019, during its transition from 70-xxx exams to role-based certifications. Microsoft placed 70-773 beside Azure Data Scientist and DP-100. That is a useful relationship for career planning, but it should not be read as proof that the two exams have identical content.
What the title does not tell you
The title does not establish whether a particular R package, Azure service, data size, algorithm, or deployment workflow appears on the assessment. Avoid study plans built from unsourced lists of “guaranteed” topics. Use any surviving official exam page or Microsoft-provided historical material only after confirming that it refers specifically to 70-773.
Who should consider this exam topic?
The subject is most relevant to a candidate who wants to analyze large datasets with R and already understands the analytical problem being solved. It is a poor fit for someone seeking a current, automatically available Microsoft credential without first checking the exam’s status and replacement route.
A practical audience includes data analysts moving toward data science, R practitioners working with substantial datasets, and professionals comparing a historical Microsoft R exam with the Azure Data Scientist pathway. The official mapping supports the connection to Azure Data Scientist, while the supplied sources do not verify prerequisites or required professional experience for 70-773.
Candidates should separate three goals before studying. One goal may be learning the historical technology represented by the title. Another may be obtaining a Microsoft credential. A third may be building current Azure data-science skills. The same study investment can serve the first and third goals, but the credential decision depends on current availability and Microsoft’s present certification information.
If your primary objective is employability in a current Azure data-science role, begin with the current Microsoft certification catalog rather than assuming that preparation for 70-773 will satisfy a newer exam. If your employer specifically names 70-773 or you are maintaining historical knowledge, document the exam’s status and source material before scheduling anything.
What is the official replacement relationship?
Microsoft’s role-based mapping associates 70-773, Analyzing Big Data with Microsoft R, with Microsoft Certified: Azure Data Scientist, earned through DP-100. This is the clearest official planning signal for candidates who are deciding whether to pursue the old exam topic or redirect their preparation to the newer role-based route.
The mapping article explains that Microsoft was introducing role-based certifications to align credentials more closely with organizational roles and cloud-related skills. It lists 70-773 and 70-774 together with Azure Data Scientist and DP-100. The relationship is therefore a transition-era alignment, not a promise that passing 70-773 grants a current certification or that DP-100 reproduces every historical R objective.
Use the relationship as a research starting point. Open the current DP-100 certification and exam information from Microsoft Learn, review its present skills outline, and compare that outline with your target job or project. Do not schedule DP-100 solely because it appears in the historical mapping; confirm that it remains the right current exam for your intended credential.
The French-language version of Microsoft’s mapping article presents the same 70-773 relationship. It can help confirm the table entry if the English page is difficult to access, but it does not add a current blueprint, delivery method, score, or availability detail for 70-773.
How to choose between historical study and a current path
Choose historical 70-773 preparation when the technology itself is your learning objective and you have reliable, exam-specific material. Choose the current role-based route when the credential is the priority. If both matter, study the current path first and use historical R topics as supplementary context rather than treating the old exam as the sole measure of present capability.
Is 70-773 currently available?
The supplied official research does not provide a retirement date or a current scheduling page for 70-773. Therefore, this guide cannot responsibly label the exam active or retired. Check Microsoft’s current certification and retirement information before buying preparation material, booking an appointment, or setting a deadline.
Microsoft states that when an exam is retired, candidates can no longer take it or earn the associated certification or credential through that exam after the retirement date. The same guidance recommends taking an exam before its retirement date if a candidate is already preparing for one scheduled to retire. That policy makes status verification an essential first step for 70-773.
The retirement page supplied with the research lists exams scheduled to retire in the next 12 months and notes that the information is subject to change. The snapshot does not list 70-773 in that table, but absence from a supplied excerpt is not evidence that the historical exam is available. Use the live Microsoft page and the relevant certification detail page for the final decision.
Do not rely on a third-party listing, an old booking link, or a practice-test seller’s claim that an exam is “still valid.” A credible status check should lead to an official Microsoft page that identifies the exam and gives a current route to scheduling or clearly explains its retirement.
A sensible status-check sequence
Start with Microsoft Learn and search the exact code and title. Then review the retirement guidance and the historical mapping. If no official scheduling route appears, stop before paying for a course or attempting to register. Finally, identify the current role-based replacement and confirm its own objectives, eligibility, and scheduling details on Microsoft Learn.
Which skills should you study first?
Because the supplied research contains no 70-773 skills-measured list, begin with the capabilities implied by the title and validate each one against exam-specific Microsoft material. The most defensible sequence is data handling, exploratory analysis, statistical reasoning, model-oriented thinking, and communication of results, with R practice connecting the stages.
Study data preparation before advanced analysis. Work on importing representative datasets, inspecting structure, identifying missing or malformed values, selecting useful fields, and transforming data into an analysis-ready form. These are practical recommendations, not verified 70-773 objectives; their purpose is to create a foundation that supports almost any serious big-data analysis workflow.
Next practise exploratory analysis. Ask what the data contains, how variables behave, whether categories are imbalanced, and which patterns may be artifacts of collection or preprocessing. Use R interactively and record the reasoning behind each transformation. A candidate who can explain why a step is necessary is better prepared than one who only remembers a command sequence.
Then review statistical interpretation. Focus on distinguishing association from causation, recognizing sampling and measurement problems, and explaining uncertainty without overstating a result. Do not infer that a particular statistical technique is tested unless an official 70-773 outline confirms it. The study objective is analytical judgment, not an invented checklist.
Finish each study block by communicating a finding. Write a short conclusion that names the evidence, its limitations, and the action it may support. Big-data analysis is valuable only when another person can understand what was found and what should happen next.
Build a traceable topic matrix
Create a table with four columns: topic, source, hands-on task, and confidence. Put only verified 70-773 topics in the source column. Place broader R or data-science practice in a separate section labelled supplementary. This prevents a common mistake: turning general experience with R into an unsupported claim about the historical exam blueprint.
How should you practise R without exam dumps?
Use small, repeatable analytical projects that force you to make decisions about data quality, transformation, analysis, and interpretation. Practice questions can help with wording and retrieval, but leaked content and memorized dumps are not a reliable substitute for understanding and should not be treated as proof of exam coverage or a passing guarantee.
Choose datasets that require more than plotting a clean table. Include inconsistent types, missing records, duplicate rows, unusual values, and fields that need careful interpretation. Keep the raw data unchanged, create a documented preparation stage, and save the reasoning for every material decision.
After preparing the data, perform an exploratory pass before selecting an analytical method. State the question in plain language, identify the relevant variables, and explain why the chosen method fits the question. Then test whether the result is sensitive to a reasonable change in assumptions or preprocessing.
Practise working from an unfamiliar prompt. Give yourself a business or operational question, identify what evidence is available, and decide what cannot be concluded. This develops transfer rather than recall. If your work depends on a package or service, record its purpose and limitations, but do not assume that the package is part of 70-773 without official confirmation.
Review your own code as if another analyst will maintain it. Look for hidden assumptions, repeated transformations, ambiguous names, and steps that cannot be reproduced. The review is as important as the first solution because it exposes gaps that passive reading often hides.
A useful project record
For each exercise, retain the question, data description, preparation decisions, R code, output, interpretation, and unresolved limitations. At revision time, explain the project without opening the code, then reproduce the result from a clean starting point. That combination tests both conceptual recall and practical control.
What preparation mistakes should you avoid?
The most serious mistake is preparing for an exam that may no longer be schedulable. After that, candidates commonly confuse a historical mapping with a current blueprint, study commands without analytical context, and use practice material as if it were an official specification.
Do not begin by purchasing a large question bank. First establish the exam’s official status and locate exam-specific objectives. If those objectives are unavailable, label your plan as skills preparation rather than claim that it mirrors the assessment. This protects both your time and your expectations.
Do not treat the DP-100 relationship as a shortcut. Microsoft’s table identifies DP-100 as the exam associated with Azure Data Scientist, but it does not state that every 70-773 topic appears there or that every DP-100 topic appeared in 70-773. Read the current DP-100 requirements independently.
Do not confuse scale with quality. A dataset being large does not make an analysis sound. Check data provenance, variable meaning, missingness, leakage, bias, and whether the conclusion answers the original question. A technically fluent solution can still be analytically wrong.
Do not memorize isolated R syntax while ignoring outputs. Practise reading warnings, checking object structure, validating transformations, and explaining why an output is trustworthy. If you cannot diagnose a result that looks surprising, more command memorization will not solve the underlying weakness.
Finally, do not publish unsupported exam claims. Exact duration, languages, price, question count, score requirements, prerequisites, and delivery arrangements are all details that must come from a current official source. None is established for 70-773 in the supplied research.
A practical study roadmap
Use a staged roadmap that starts with the credential decision, then builds analytical capability and ends with evidence-based readiness. The roadmap below is a preparation recommendation, not an official Microsoft schedule, because the supplied sources do not define a 70-773 course sequence or study duration.
Stage one is administrative verification. Record the exact exam title, inspect Microsoft’s current pages, and determine whether an official scheduling path exists. If it does not, redirect the credential goal to the current role-based route. Keep a copy of the relevant Microsoft mapping for context, but do not use it as proof of availability.
Stage two is baseline assessment. Without using dumps, complete a short project from raw data to written conclusion. Note where you struggle: R syntax, data preparation, statistical reasoning, scale-related performance, or communicating results. Turn those observations into study tasks rather than spending equal time on every topic.
Stage three is foundation work. Revisit R objects, data structures, importing, transformation, filtering, joining, aggregation, missing-data handling, and visual inspection as needed for your project. Use deliberate exercises that change one condition at a time, so you can see which operation changes the result.
Stage four is analytical reasoning. For each project question, define the target, identify possible sources of bias, select a defensible method, and test the interpretation. Compare an initial result with a sensible alternative and explain the difference. This is more valuable than collecting unrelated demonstrations.
Stage five is timed decision practice. Only use timing information if a current official 70-773 page supplies it. Otherwise, practise completing a defined analytical task within a self-chosen limit, clearly marking that the limit is your own training constraint. Review wrong decisions, not just unfinished work.
Stage six is final verification. Recheck the official exam status, objectives, registration route, and any current policies immediately before scheduling. If the target has changed, update the plan instead of forcing historical preparation onto a newer assessment.
When to switch to DP-100 preparation
Switch when the Microsoft mapping is the reason you selected 70-773 but the current credential is your real objective. Begin with the live DP-100 study guide and skills outline, identify differences from your R-focused practice, and rebuild the topic matrix. The historical exam title can guide background study, but it should not replace current objectives.
How do you decide whether you are ready?
Readiness should mean that you can perform and explain the relevant work, not that you recognize a set of recalled answers. For 70-773, use a conservative standard: you should be able to complete an unfamiliar R-based analysis, defend your preparation choices, and explain limitations while separately confirming that the exam can actually be taken.
Use several checks. Can you translate a vague question into an analyzable task? Can you inspect a dataset before transforming it? Can you identify when a result is affected by missing or poor-quality data? Can you reproduce your output and explain the important parts of the code? Can you communicate a conclusion without claiming more than the evidence supports?
If you repeatedly fail one check, return to the relevant project stage. A syntax problem calls for focused R drills; a misleading conclusion calls for statistical and domain reasoning; an irreproducible result calls for better workflow discipline. Do not respond to every weakness by taking more mock questions.
Before committing to an appointment, confirm that the official source identifies the exam code, current availability, and applicable policies. The supplied research does not establish those details for 70-773. If the official route points to a replacement, assess readiness against that replacement’s current requirements instead.
Candidates who already hold a Microsoft credential should also check their Certification Dashboard and transcript rather than assuming that an old exam record is missing. Microsoft explains that certificates, badges, and transcripts are accessed through the Certification Dashboard, where certificates can be viewed and transcripts can be downloaded or shared.
What should you do after passing or changing paths?
After a successful credential outcome, verify the result in your Microsoft Certification Dashboard and use the transcript or badge when you need evidence. If you move to the current Azure Data Scientist route, update your study records and professional materials to name the credential you actually earned rather than presenting the historical exam as a current certification.
Microsoft’s certificate guidance says the Certification Dashboard provides access to certificates, badges, and transcripts. The transcript can be downloaded as a PDF or shared using a transcript ID and access code. These are practical ways to provide verifiable evidence, subject to the credential appearing in your account.
If you decide not to pursue 70-773, keep the R projects. Rewrite each project description around the business question, data preparation, analytical method, result, and limitation. Then map the demonstrated skills to the current certification or role you are targeting. This turns an administrative change into a useful portfolio improvement.
If an exam has retired and you already earned the associated certification, Microsoft states that the certification remains on your transcript. If you have not earned it, the retirement policy says you cannot take the retired exam or earn the associated credential through it after retirement. That distinction is why checking your own record and the current official status matters.
Your next actions for 70-773
Do not schedule from a historical title alone. Confirm the exam’s status on Microsoft Learn, compare your objective with the Azure Data Scientist and DP-100 mapping, and then choose either a verified 70-773 route or a current replacement. Only after that decision should you invest in exam-specific material.
If the exam is available through an official route, obtain the applicable current objectives and build your topic matrix around them. Practise R through documented, unfamiliar datasets; review analytical reasoning; and use practice assessments only as readiness checks, never as a substitute for official objectives or hands-on understanding.
If the exam is unavailable, stop searching for unofficial workarounds. Use Microsoft’s current role-based certification information to select the path that matches your career goal, desired skillset, and schedule. Preserve your historical R study as supporting knowledge, but measure your readiness against the current exam you intend to take.
Finally, keep your evidence organized: official status link, target credential, current objectives, project notes, weak areas, and registration confirmation. This small record prevents the most expensive preparation error—spending time on an exam that no longer matches the credential you need.
Conclusion
70-773 is best approached as a historical Microsoft R and big-data analysis topic with a documented connection to Azure Data Scientist and DP-100. The supplied official research does not verify a current blueprint, scheduling route, delivery format, score, or retirement date for the exam itself. Verify those facts directly with Microsoft before preparing or booking. If the historical exam is unavailable, redirect the same analytical discipline toward the current role-based certification and retain your R projects as evidence of practical skill.
