MCSE: Data Management & Analytics

I have this MCSE — where should I go now?

Choose the continuation of your actual data work. An operational DBA, a warehouse developer and a reporting specialist could all earn this MCSE through different exam combinations. The title alone does not establish expertise in all three areas.

Your strongest background	Closest modern pathway	What to add next
SQL Server backups, availability and administration	SQL Server / Azure SQL DBA	Managed-service boundaries, Entra authentication, migration and recovery
T-SQL, schema design, stored procedures and tuning	SQL database development	Database projects, GitHub delivery, current SQL features; AI integration where relevant
SSIS, dimensional warehousing or cloud ingestion	Fabric data engineering	OneLake, Delta tables, pipelines, Spark and operational monitoring
Analysis Services models and enterprise BI	Fabric analytics engineering	Current semantic models, DAX, storage modes, workspace security and deployment
Power BI, Excel and business reporting	Power BI data analysis	Power Query, model design, service refresh and governed distribution
R, predictive modelling or Cosmos DB	Specialist data work	Current ML tooling or partition-aware application design, according to your route

These are MCSEDirectory editorial recommendations, not Microsoft equivalencies or automatic upgrades. Current credential requirements were checked 8 September 2026.

What was this certification?

Microsoft Certified Solutions Expert: Data Management & Analytics was the data qualification in Microsoft’s streamlined MCSE programme. It joined a qualifying Microsoft Certified Solutions Associate (MCSA) foundation to an additional data specialization. The September 2016 route covered SQL Server 2012/2014 and the three SQL 2016 MCSAs; the June 2018 roadmap also included machine learning, BI reporting and Azure data engineering. 2016 roadmap, pages 6 and 10; 2018 roadmap, pages 2 and 6.

Introduction, earlier qualifications and retirement

The programme’s September 2016 elective pool establishes its introduction era. Its relevant predecessors were MCSE: Data Platform and MCSE: Business Intelligence, both included in Microsoft’s launch transition list. The new programme offered a broader choice of specialization and replaced recurring recertification exams with a non-expiring award. Passing another elective in a later year could add a new dated certification entry; it was not required to keep the award. 2016 programme, pages 3–4.

Microsoft stopped offering the remaining MCSE exams on 31 January 2021, extending the original 30 June 2020 deadline in its 26 March 2020 update. The role-based certification model followed, but it did not automatically turn this MCSE into a modern Azure or Fabric credential. Microsoft retirement notice.

An earned MCSE does not expire under Microsoft’s policy. A retired non-expiring credential stays in the active transcript section for two years after retirement, before becoming historical/inactive. Preserve its full title and award year; distinguish that achievement from your current platform experience. Credential policy; retirement FAQ.

Historical certification requirements

The award rule

The standard rule was one qualifying MCSA AND one additional qualifying elective. It did not require every MCSA, every elective, or a single universal set of MCSE exams. The 2018 chart allowed exams on a path to be completed in any order; “prerequisite” describes the award dependency rather than a mandatory booking order. 2016 programme, page 3; 2018 chart, page 6.

Prerequisite choices and their standard exams

The first four routes appear in both roadmaps. The last three are documented in the 1 June 2018 snapshot. Each row is an alternative MCSA, with all exams in that row required for its standard route. Earlier MCSA upgrade arrangements are separate from this table.

Qualifying MCSA	Standard exam numbers and titles
SQL Server 2012/2014	70-461 — Querying Microsoft SQL Server 2012/2014; 70-462 — Administering Microsoft SQL Server 2012/2014 Databases; 70-463 — Implementing a Data Warehouse with Microsoft SQL Server 2012/2014
SQL 2016 Database Development	70-761 — Querying Data with Transact-SQL; 70-762 — Developing SQL Databases
SQL 2016 Database Administration	70-764 — Administering a SQL Database Infrastructure; 70-765 — Provisioning SQL Databases
SQL 2016 BI Development	70-767 — Implementing a SQL Data Warehouse; 70-768 — Developing SQL Data Models
Machine Learning	70-773 — Analyzing Big Data with Microsoft R; 70-774 — Perform Cloud Data Science with Azure Machine Learning
BI Reporting	70-778 — Analyzing and Visualizing Data with Microsoft Power BI; 70-779 — Analyzing and Visualizing Data with Microsoft Excel
Data Engineering with Azure	70-775 — Perform Data Engineering on Microsoft Azure HDInsight; 70-776 — Engineering Data with Microsoft Cloud Services

Source: Microsoft June 2018 roadmap, page 6. Titles follow that period’s wording, with the full 70- prefix. 70-767 later appeared as Implementing a Data Warehouse using SQL in Microsoft’s final retirement notice; 70-464 likewise appeared there with “2012/2014” in its title. These are title variations for the same exam numbers, not extra exams. Final titles.

Electives: dated pools, not one timeless list

The following table records the September 2016 and June 2018 Microsoft pools. Choose one additional qualifying exam; a foundation exam’s appearance here should not be interpreted as automatically supplying a second award from the same work. For example, the SQL 2016 Database Development MCSA already used 70-762; adding 70-767 was a distinct specialization.

Exam	Title in the historical roadmaps	September 2016	June 2018
70-464	Developing Microsoft SQL Server Databases	Listed	Listed
70-465	Designing Database Solutions for SQL Server	Listed	Listed
70-466	Implementing Data Models and Reports with Microsoft SQL Server	Listed	Listed
70-467	Designing Business Intelligence Solutions with Microsoft SQL Server	Listed	Listed
70-473	Designing and Implementing Cloud Data Platform Solutions	Listed	Listed
70-475	Designing and Implementing Big Data Analytics Solutions	Listed	Listed
70-762	Developing SQL Databases	Not listed	Listed
70-767	Implementing a SQL Data Warehouse	Not listed	Listed
70-768	Developing SQL Data Models	Not listed	Listed
70-773	Analyzing Big Data with Microsoft R	Not listed	Listed
70-774	Perform Cloud Data Science with Azure Machine Learning	Not listed	Listed
70-775	Perform Data Engineering on Microsoft Azure HDInsight	Not listed	Listed
70-777	Implementing Azure Cosmos DB Solutions	Not listed	Planned 2018, not confirmed available by this snapshot

Sources: 2016 roadmap, page 10; 2018 roadmap, page 6. The final notice names 70-465 as Designing Database Solutions for Microsoft SQL Server and 70-777 as Implementing Microsoft Azure Cosmos DB Solutions. Microsoft final exam list.

By May 2020, Microsoft Community Support still explicitly confirmed MCSA: SQL Server 2012/2014 + 70-762 OR 70-464 as MCSE completion routes. A November 2020 community answer preserved an eight-elective listing: 70-464, 70-465, 70-466, 70-467, 70-762, 70-767, 70-768 and 70-777. That later list is useful archival corroboration, not an authoritative programme notice; it should not extend the 2018 eligibility rules or establish when individual exams entered or left the pool. Named Microsoft Support response; community record and captured requirements.

Transition awards and upgrades

At launch, Microsoft said holders of active MCSE: Data Platform or MCSE: Business Intelligence would receive the corresponding 2016 qualification through transcript reevaluation, without additional exams. This was an official historical transition award, distinct from the normal MCSA-plus-elective route. It should not be generalized to every expired predecessor record. 2016 transition slide, page 4.

The SQL 2016 Database Administration page separately documents contemporary evidence of an MCSA upgrade. There is no single additional “Data Management & Analytics upgrade exam” established by the roadmaps. Neither historical arrangement grants credit toward today’s credentials.

Technologies and skills covered

The prerequisite and elective together explain the technical meaning of a holder’s record:

SQL administration foundation: database access, backups, availability, monitoring and provisioning, including Azure deployment. This foundation is documented in the 70-764/765 requirements and objectives.
Database development: 70-762 covered schema and database objects, programmable objects, concurrency and performance. Work with constraints, stored procedures, execution plans and locking belongs here. It does not establish BI model design. Microsoft Press 70-762.
Warehousing: 70-767 addressed warehouse design, ETL and data quality, with SSIS and Master Data Services context. This route emphasizes reliable movement and preparation of data. Microsoft Press 70-767.
BI models: 70-768 included multidimensional and tabular semantic models, querying and Analysis Services configuration. This differs from administering the underlying SQL instance and from producing a report alone. Microsoft Press BI references.
Earlier design electives: 70-465 and 70-467 distinguished database solution design from BI solution design; 70-466 focused on models and reports. Cloud electives 70-473/475 represented other platform and analytics choices. Dated elective definitions.
Expanded 2018 routes: R and Azure Machine Learning, Power BI and Excel, and HDInsight/cloud engineering were separate foundations. The roadmap did not require a SQL DBA to complete all of them. Cosmos DB was another elective direction, initially marked planned. 2018 roadmap.

What remains relevant today

Relational modelling, T-SQL reasoning, transactional correctness and recovery planning retain value in SQL work. For warehouse developers, the durable knowledge includes data grain, business keys, slowly changing dimensions, reconciliation and rerunnable loading. For BI specialists it includes consistent measures, filter context, dimensional relationships and controlling who can see which data.

MCSEDirectory’s assessment: the valuable part is the reasoning behind your implementation. A correctly reconciled warehouse load or a proven recovery sequence transfers more directly than familiarity with a retired exam’s interface. Attribute each skill to the route and work you actually completed, not to the entire elective catalogue.

What has changed

SQL hosting divides responsibility differently. Azure SQL Database, Managed Instance and SQL Server on Azure VMs have different compatibility and operational boundaries. SQL knowledge transfers, but instance/OS administration and platform-managed backups are not interchangeable responsibilities. Microsoft deployment comparison.
Analytics adds a service and storage layer. Fabric engineering involves OneLake, pipelines, notebooks and Spark; an SSIS package does not become a Fabric pipeline merely by changing its connection string. DP-700 objectives.
Model deployment and access require new decisions. Fabric analytics includes semantic models, lakehouses/warehouses, lifecycle management and service-level permissions. Preserve dimensional reasoning while learning storage modes and access boundaries. DP-600 objectives.
SQL development now has a wider certification scope. DP-800 adds GitHub CI/CD and AI-enabled database capabilities to SQL development. That is a skill expansion, not proof that every SQL role requires AI. Current developer role.
Predictive modelling tools have changed. Azure Machine Learning’s v2 tooling uses jobs, components and managed endpoints. An old cloud data-science exam does not demonstrate current deployment practice. Microsoft v2 guidance.

How these skills map to Microsoft today

These are editorial skills correspondences, not certification equivalencies.

Historical experience	Modern application	Main gap
70-764/765 administration and provisioning	SQL Server, Azure SQL Database / Managed Instance	Cloud identity, service choice and tested service-specific recovery
70-464/762 database programming	Current T-SQL and SQL database projects	Automated schema delivery, current security and newer engine capabilities
70-463/767 warehouse and ETL work	Fabric Warehouse/Lakehouse and Data Factory pipelines	OneLake, distributed processing, orchestration and failure recovery
70-466/768 Analysis Services work	Power BI semantic models and Fabric analytics	DAX where formerly MDX-focused; storage mode, refresh and workspace governance
70-778/779 reporting	Power BI analyst and BI developer work	Current Power Query, modelling, access and distribution practices
70-775 HDInsight engineering	Fabric Spark and data engineering where suitable	Fabric execution, storage, security and operational controls
70-777 Cosmos DB work	Current Cosmos DB application/data engineering	Reassess partitioning, throughput and current SDK behaviour for the workload

The detailed pathways below explain the recommended learning sequence and link to current Microsoft scope.

Where do I go from here?

Current certification choices

Checked 8 September 2026: Microsoft Learn offers the following credentials. Their pages list one associated exam and no prerequisite certification; the expected practical knowledge is described below. None has an announced retirement in the current credential retirement list. These are separate choices, not a sequence of five exams. Retirement register.

Credential	Current exam	Preparation most relevant to a legacy holder
Microsoft Certified: Azure Database Administrator Associate	DP-300 — Administering Microsoft Azure SQL Solutions	SQL Server and Azure SQL operation, security, performance and availability
Microsoft Certified: SQL AI Developer Associate	DP-800 — Developing AI-Enabled Database Solutions	T-SQL/database development, GitHub CI/CD and AI concepts
Microsoft Certified: Fabric Data Engineer Associate	DP-700 — Implementing Data Engineering Solutions Using Microsoft Fabric	Loading, orchestration, SQL, PySpark and KQL
Microsoft Certified: Fabric Analytics Engineer Associate	DP-600 — Implementing Analytics Solutions Using Microsoft Fabric	Analytical assets, semantic modelling, SQL, DAX and KQL
Microsoft Certified: Power BI Data Analyst Associate	PL-300 — Microsoft Power BI Data Analyst	Power Query, DAX and delivering usable business analysis

If you remained a database administrator

Closest roles: SQL Server DBA, Azure SQL DBA or database platform engineer. DP-300 is the closest credential fit for the administration foundation. Keep your incident diagnosis, backup-chain and permissions knowledge; add cloud resource permissions, Entra database authentication and the operational differences between a VM, Managed Instance and SQL Database. Current DBA scope; DP-300 objectives.

Suggested learning sequence: inventory a representative database’s SQL Agent jobs, logins and application dependencies → compare two plausible Azure SQL targets → rehearse migration → configure restricted connectivity and authentication → test point-in-time recovery → baseline and investigate query performance.

Practical project: migrate a sample order database, record recovery time and data loss against stated objectives, and demonstrate how the application reconnects after recovery. Document which backup, patching and availability tasks remain yours. This tests the service boundary that distinguishes cloud DBA work from an old instance-maintenance routine. See the SQL Server to Azure SQL pathway.

If database development was your specialization

Closest roles: SQL database developer or database performance specialist. The 70-464/762 branch is a better foundation here than assuming all MCSE holders should become administrators. Keep schema design, transactions, stored procedures and execution-plan analysis. Add database projects in Git, reviewed schema changes, automated deployment and current platform security. DP-800 is relevant when you also want its AI-enabled SQL scope; DP-300 fits work that is primarily operational. Developer requirements; DP-800 objectives.

Suggested learning sequence: modernize one database project → add constraints and concurrency tests → measure one query-plan improvement → automate deployment to a disposable test database → restrict application access. Then, if AI integration belongs in your role, study embeddings, vector search and secure model access.

Practical project: implement an order-entry database with a reproducible deadlock test and a measured fix. Add a reviewed migration that preserves existing rows. For the AI branch, evaluate product similarity search against a small labelled query set and enforce the same data-access restrictions used by conventional queries. This is our suggested exercise, not a claim that AI features were covered by the MCSE.

If you built warehouses or ETL pipelines

Closest roles: data engineer or analytics platform engineer. SSIS control flow, incremental loading, data quality and warehouse design give you useful preparation for Fabric Data Engineer Associate / DP-700. The gap is executing and operating those patterns in Fabric, including OneLake storage, Spark transformations, service permissions and pipeline monitoring. Engineering role; current objectives.

Suggested learning sequence: learn Warehouse versus Lakehouse storage → load SQL source data through a Fabric pipeline → implement a watermark-based incremental load → use SQL or PySpark transformations → add quality checks and alerts → version and deploy the solution.

Practical project: load sales facts and a customer dimension; include a late-arriving customer, a corrected source record and a duplicate batch. Stop a run halfway through, rerun it and prove totals and row counts remain correct. Explain the recovery behaviour and measure pipeline duration. That makes the transition from package development to operating a data service concrete.

If you built Analysis Services models

Closest roles: analytics engineer or semantic-model developer. For a 70-466/768 specialist, Fabric Analytics Engineer Associate / DP-600 is the strongest editorial correspondence. Retain dimensional modelling and measure definitions. A tabular/DAX background transfers more directly than an exclusively multidimensional/MDX background; neither removes the need to learn Fabric permissions, storage modes and deployment. Analytics role; DP-600 objectives.

Suggested learning sequence: select one existing business model → rebuild its star schema over a Fabric analytical store → translate representative calculations into DAX → compare supported storage modes → configure row-level access → test deployment and refresh.

Practical project: reproduce monthly revenue, prior-year comparisons and distinct customer counts. Test results at detail and total levels, including a user restricted to one region. Measure query behaviour and document access to the underlying data separately from access through the model. A matching chart alone does not establish a correct migration.

If reporting was your main work

Closest roles: Power BI data analyst or BI developer. Power BI Data Analyst Associate / PL-300 fits 70-778/779 holders whose work remained close to business questions and report delivery. Power Query and DAX continue to matter. Add current model design, refresh, workspace access and distribution; SQL instance administration is not a prerequisite career detour. Analyst role; PL-300 objectives.

Suggested learning sequence: profile a real reporting dataset → clean it in Power Query → define grain and relationships → write explicit DAX measures → create accessible report navigation → configure refresh and access → validate with the report’s intended users.

Practical project: replace a monthly spreadsheet pack with a Power BI report that reconciles to the source ledger, handles missing periods and restricts regional data correctly. Document who maintains the data connection and what happens when refresh fails. Choose DP-600 later if responsibility expands into shared enterprise models and Fabric analytical assets; it is not mandatory progression for every report author.

If you followed a machine-learning or Cosmos DB route

For 70-773/774 holders, the useful continuation is applied data science or ML engineering when you retained modelling experience. Preserve evaluation, feature preparation and statistical reasoning; add current Python tooling and Azure Machine Learning v2 jobs, reusable components, environments and endpoints. Current tooling. Azure Data Scientist Associate retired on 1 June 2026; do not start a new plan around earning that retired credential. Microsoft retirement register.

Suggested project: reproduce a tabular prediction experiment with separate training/validation data, record its environment and metrics, then deploy and test an endpoint using the current tooling. Explain leakage controls and how a changed input distribution would be detected. If your interest is supplying reliable data to models rather than developing them, the DP-700 pathway above is the more relevant credential direction.

For 70-777 holders, current Cosmos DB work can lead toward application data engineering. Study partition-key choice and request-unit consumption against real access patterns; relational indexing intuition alone is insufficient. Microsoft partitioning guidance.

Suggested project: model a multi-tenant order workload, compare two partition-key choices, measure cross-partition query costs and test uneven tenant traffic. Then update application data access and operational diagnostics. The SQL and Fabric credentials above cover different products; select one only if your job is expanding into its scope, not as a nominal Cosmos DB replacement.

MCSA: SQL 2016 Database Administration supplies the detailed administration prerequisite, objectives and earlier upgrade evidence.
MCSE: Data Platform and MCSE: Business Intelligence were related predecessor qualifications named in Microsoft’s 2016 transition; they are not additional requirements for every holder. Transition rules.
The SQL development, warehousing and model references above explain 70-762, 70-767 and 70-768; the dated requirement tables identify the other routes without creating empty exam pages.
Browse career pathways for related guidance and retirement versus expiry for interpreting your record.

Sources and methodology

Microsoft’s original programme PDFs establish the main historical requirements. Microsoft Press explains exam scope. Current credential names, exams, prerequisites and announced retirements were checked against Microsoft Learn on 8 September 2026. Pathway selection, learning sequences and project proposals are MCSEDirectory editorial analysis. See our research standards and corrections policy.

Historical scope and remaining evidence gaps

The 2016 and 2018 PDFs were retrieved directly and their graphical route tables inspected. The June 2018 chart is a dated snapshot, not a claim that every listed exam or prerequisite remained available until January 2021. It explicitly marks Cosmos DB exam 70-777 as planned. Its exact availability date and every intervening pool change have not been established here.

The 2020 Support response is attributed to Rosalie_G, Microsoft Community Support, and was available as indexed text. The November 2020 eight-exam list comes from Leon Laude’s community answer, not a Microsoft certification-programme author. It is labelled corroboration accordingly. Microsoft’s later roadmap endpoint RE2NfU6 returned 404 during this review and was not used as evidence for a reconstructed final rule.

The exact first-award day, complete retrospective credit rules, and all prerequisite upgrade/retirement interactions remain unconfirmed. The 2016 automatic award was conditional on an active predecessor; no broader entitlement is inferred. Exam titles vary between programme slides and the final retirement notice, so dated wording is preserved rather than silently treating each spelling as a separate qualification.

At a glance