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  • Versión 1.0.2, 2 noviembre 2026

    nivel: principiante

    Un curso de preparación para el examen de Microsoft AI-901 (Microsoft Azure AI Fundamentals), escrito para ingenieros de software de las Big 4 y de otras firmas de servicios profesionales que ya escriben SQL, C# y Python y ahora necesitan un dominio práctico de la IA en Azure — y el certificado que lo demuestra. AI-901 sustituyó a AI-900 en 2026 y cambió de carácter: más de la mitad del examen trata de construir con Microsoft Foundry — implementar modelos, escribir prompts, crear agentes y llamar a capacidades de texto, voz, visión y Content Understanding desde aplicaciones ligeras en Python. El curso sigue el temario oficial de habilidades (revisión de abril de 2026) dominio por dominio, con los mismos pesos: conceptos y capacidades de IA (40–45 %) e implementación de soluciones de IA con Foundry (55–60 %). Cada tema se enseña de la misma manera: el problema que te lleva a recurrir a la capacidad, la capacidad en sí y lo que cuesta — en tokens, cuotas, latencia, residencia de datos y riesgo. Los laboratorios prácticos recorren paso a paso el portal de Foundry y el SDK de Python; si tu suscripción corporativa bloquea Foundry, cada laboratorio tiene una vía de «leer el código» para que puedas practicar igualmente cada objetivo del examen. El caso conductor es Aldervane Advisory LLP, una firma ficticia de servicios profesionales que crea un agente interno de Service Desk, extrae datos de facturas y partes de horas y transcribe reuniones con clientes — donde la confidencialidad de los datos de los clientes nunca es opcional. La práctica usa los formatos de pregunta del examen real: opción única y múltiple, emparejamiento, series de afirmaciones Sí/No y completar código. Sketchnotes visuales resumen cada concepto en una página llena de color. Las unidades opcionales cubren las mismas aplicaciones en C#, cuotas y costes, evaluación y trazabilidad, y gobernanza de los datos de los clientes.

    24 unidades · ~27,8 h sin las unidades opcionales, ~33,8 h con ellas · autor: Andrey Kuralekh · recetas: 1

    Material: inglés · lección en tu idioma · Examen: inglés

    para: developers Big 4ai-901azuremicrosoft-foundryazure-aigenerative-aiai-agentsresponsible-aipythoncertificationprofessional-services

  • Versión 1.1.1, 31 octubre 2026

    nivel: intermedio

    A hands-on course on using large language models across the test process: prompting for test analysis and design, evaluating what the model gives back, containing its risks, and rolling it out in an organisation that handles client data. Built around a professional-services engagement, where confidentiality and documented review are not optional. Assumes working knowledge of test design, test levels and defect management.

    Pruebas y QA · 21 unidades · ~18,4 h · autor: Andrey Kuralekh

    Material: inglés · lección en tu idioma · Examen: inglés

    para: QA engineersgenaillmsoftware-testingprompt-engineeringtest-automationprofessional-services

  • Versión 1.1.1, 31 octubre 2026

    nivel: intermedio

    A working course in T-SQL for QA engineers in professional services firms who already know the basics — SELECT, WHERE, a simple JOIN — and now need SQL as a daily testing instrument rather than as a topic. Everything is set in ENGAGE, the fictional engagement and billing platform of Aldervane Advisory LLP: clients, engagements, staff, timesheets, approvals, invoices, an audit log and a migration staging table. One schema, carried through every unit, so the examples are the ones you actually meet — a report total that disagrees with the raw rows, a record the UI claims it saved, a migration that quietly dropped a hundred time entries, a duplicate that only appears under a business key. Each construct is introduced the same way: the problem that makes you reach for it, the construct itself, and the price it charges. LEFT JOIN buys you the missing rows and charges you row multiplication. NOLOCK buys you speed on a shared environment and charges you dirty reads. A green test that never asked the right question costs more than a red one. Nineteen core units cover reading an unfamiliar schema, staying safe on a shared test environment, NULL and three-valued logic, data types and collation, dates and time zones, joins and orphan records, set operations as reconciliation, aggregation, subqueries, CTEs, window functions, test data setup and idempotent teardown, verifying what the application wrote, reading someone else's views and triggers, and turning a SQL check into a deterministic assertion. Five optional units add a local sandbox script, execution plans, client data and AI assistants, an end-to-end migration reconciliation checklist, and normalisation as an explanation of why the schema looks the way it does. Units are open in any order: come for the whole course, or come for the one thing that is biting you today. Scope note: the course teaches you to write queries and to read them. Practice covers both; the certification exam checks reading, recognition and knowledge of constructs, since a written query cannot be machine-verified here.

    Pruebas y QA · 24 unidades · ~19,4 h sin las unidades opcionales, ~24,1 h con ellas · autor: Andrey Kuralekh

    Material: inglés · lección en tu idioma · Examen: inglés

    para: QA engineerssqlt-sqlqatestingsql-serverdata-verificationtest-dataprofessional-services

  • Versión 1.0.1, 31 octubre 2026

    nivel: avanzado

    A course for senior developers at Big 4 and other professional-services firms who already use GitHub Copilot — or are about to be told to — and want to use it as an engineer rather than as a faster autocomplete. Copilot in 2026 is an agent platform: agent mode and a Plan agent in Visual Studio 2026 and VS Code, specialised agents for debugging, profiling, testing and .NET modernisation, custom agents and skills your team keeps in the repository, MCP servers, cloud agents that return pull requests, and Copilot CLI in the terminal. The course teaches the skills that separate a senior from a passenger: engineering the context, splitting a task into a plan the agent can execute, reviewing an AI diff as critically as a colleague's, encoding team rules once so they work in every IDE, and knowing exactly what leaves the client's perimeter — and what it costs in AI Credits. Every capability is taught the same way: the pain that makes you reach for it, the capability itself, and its cost — including the uncomfortable ones, such as content exclusion not applying in agent mode, or a green test suite that an agent wrote to match its own bug. The mandatory track follows the official GH-300 (GitHub Copilot) exam outline, so the course doubles as certification preparation; optional units go deeper into Copilot CLI automation, hooks, model and cost engineering, and measuring real productivity. Labs use one sample repository — Aldervane Advisory LLP's ENGAGE platform — that opens in Visual Studio 2026, VS Code and the CLI alike; it is a public GitHub template (github.com/AndreyKuralekh/engage), so each learner starts from their own copy. Every lab also has a read-and-answer path for learners without a Copilot Business or Enterprise seat.

    25 unidades · ~29,5 h sin las unidades opcionales, ~34,5 h con ellas · autor: Andrey Kuralekh

    Material: inglés · lección en tu idioma · Examen: inglés

    para: Senior developers Big 4github-copilotgh-300visual-studio-2026vs-codecopilot-cliai-agentsmcpprompt-engineeringcode-reviewdotnetgovernancecertificationprofessional-services

  • Versión 1.0.2, 31 octubre 2026

    nivel: intermedio

    A preparation course for Microsoft exam PL-300 (Microsoft Power BI Data Analyst), written for senior developers in Big 4 and other professional-services firms who already think in SQL and code — and keep getting surprised by DAX. The course follows the official skills outline (April 2026 revision) with its four domains and weights: prepare the data (25–30%), model the data (25–30%), visualize and analyze the data (25–30%), and manage and secure Power BI (15–20%). It covers what the 2026 updates added: Direct Lake, calculation groups, visual calculations, DAX query view and Copilot in Power BI. The spine of the course is the shift from SQL thinking to DAX thinking: filter context, row context, CALCULATE and context transition, taught with small models you can compute in your head before you check them in Power BI Desktop. Every topic follows the same order: the pain, the solution, and what it costs — memory, refresh time, query latency, licences or security risk. Labs run in Power BI Desktop (free) on a synthetic Aldervane Advisory LLP dataset — timesheets, invoices with three role-playing dates, month-end WIP balances and a nested JSON export — published on GitHub. Service features (workspaces, apps, RLS, refresh) are shown step by step, with a free route to a sandbox tenant and a read-the-screenshots path for learners whose corporate tenant blocks publishing. Practice uses the formats of the real exam: single and multiple choice, matching, Yes/No statement sets, "what does this measure return in this cell" and DAX completion. Isotype-style pictures summarise one idea each. Optional units cover PBIP/TMDL and Git, a SQL-to-DAX phrasebook, client-confidentiality patterns and the bridge to Microsoft Fabric.

    28 unidades · ~51 h sin las unidades opcionales, ~59 h con ellas · autor: Andrey Kuralekh

    Material: inglés · lección en tu idioma · Examen: inglés

    para: developers Big 4pl-300power-bidaxpower-querydata-modelingmicrosoft-fabricrow-level-securitycertificationprofessional-services