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DP-800 Microsoft SQL AI Developer Associate Course 2026
Rating: 4.3 out of 5(20 ratings)
99 students

DP-800 Microsoft SQL AI Developer Associate Course 2026

Pass the Microsoft SQL AI Developer Associate (DP-800) with 3 hours of beginner friendly videos.
Created byCloud Guru Amit
Last updated 7/2026
English
English [Auto],

What you'll learn

  • Design and manage tables, indexes, columnstore indexes, and sequences.
  • Apply PRIMARY KEY, FOREIGN KEY, UNIQUE, CHECK, and DEFAULT constraints.
  • Implement in-memory, temporal, external, ledger, and graph tables.
  • Use JSON functions like OPENJSON, JSON_VALUE, JSON_ARRAY, and JSON_OBJECT.
  • Apply regular expressions for search, replace, and pattern matching.
  • Perform fuzzy string matching with EDIT_DISTANCE and JARO_WINKLER.
  • Build queries using Common Table Expressions (CTEs).
  • Analyze data with advanced window functions.
  • Secure databases with encryption, dynamic data masking, and RLS.
  • Analyze query execution plans and DMVs.
  • Monitor applications with Application Insights and Log Analytics.
  • Compare ANN vs ENN search methods for vector data.
  • Use semantic vector search functions like VECTOR_SEARCH and VECTOR_DISTANCE.
  • Apply Retrieval-Augmented Generation (RAG) in SQL solutions.
  • Extract and process LLM responses for database solutions.

Course content

4 sections • 128 lectures • 6h 3m total length

  • Tables1:10

    Explore how tables organize data in rows and columns, like a classroom register, enabling structured storage and quick retrieval for queries and reports.

  • Tables Hands-On9:50

    Learn to set up an Azure SQL database and configure networking. Create a dbo.sales table with int primary key, nvarchar product name, date, and decimal(10,2); insert data and run queries.

  • Indexes1:04
  • Column Store Indexes1:24

    Explore column store indexes and how storing data column-wise speeds large analytical queries by scanning only the relevant columns across millions of records, such as sales totals.

  • Indexes Hands-On6:59
  • Sequences1:09
  • Sequences Hands-On5:57

    Learn to use SQL sequences by creating an order sequence that starts at 1000, increments by one, and can reset to 2000 for new records.

  • PRIMARY KEY1:05

    Learn how a primary key provides a unique, non-null identifier for each table row, using a barcode analogy to guarantee uniqueness and accuracy.

  • FOREIGN KEY1:13
  • UNIQUE1:10
  • CHECK1:26

    Apply check constraints to enforce rules on column values, such as salaries greater than zero or ages between 5 and 100, ensuring only valid data enters the database.

  • DEFAULT1:05

    Explore how the default constraint assigns a predefined value to a column when no value is provided, like country defaulting to India or order status pending, ensuring autofill and consistency.

  • Database Constraint Hands-On20:30

    Explore database constraints in Azure SQL: implement and verify primary key, foreign key, unique, check, and default constraints through hands-on tests with customers and orders tables.

  • In-memory1:11
  • Temporal Table1:04
  • Temporal Table Hands-On5:13
  • External Table1:14

    Explore external tables that query data stored outside, such as in Azure Data Lake or Blob Storage, without copying it, using SQL.

  • Ledger Table1:07
  • Ledger Table Hands-On3:45

    Create and enable a ledger table with system versioning to ensure tamper-evident history and cryptographic proofs. Insert and update ledger orders, then verify ledger transactions and history.

  • Graph Table1:14

    Explore graph tables that store data as nodes and edges to map relationships, such as customers and products linked to show who bought what, and analyze connections.

  • Graph Table Hands-On2:55
  • JSON Columns1:12
  • JSON Indexes1:17

    Discover how JSON indexes point directly to keys or paths inside JSON columns to speed up queries and filter items by rating without scanning the whole document.

  • JSON Column and Index Hands-On3:58

    Create a table with JSON columns in Azure SQL, insert dummy JSON data, extract values including nested RAM details, and build JSON indexes to optimize queries.

  • OPEN_JSON1:37

    Explore the open JSON function to convert JSON text into rows and columns, turning messy JSON into a structured table with order ID, product, and quantities for SQL queries.

  • JSON_VALUE1:13

    Extract a single property from a JSON document using the JSON_VALUE function. It acts as a precise picker, returning specific values like a customer's name from name, age, and address.

  • JSON_ARRAY1:08

    Learn how JSON arrays group multiple values into a single array, using maths, science, and history as an example to illustrate structured data in JSON format.

  • JSON_CONTAINS1:13

    Learn how the JSON contains function checks if a value exists in a JSON document, using an example like product ID 101 in an order to filter and validate data.

  • JSON_ARRAYAGG1:12
  • JSON_OBJECT1:07

    Explore the JSON object function by creating a JSON object from key-value pairs, turning raw data into a labeled, attribute-based JSON document like name, age, class.

  • JSON Function Hands-On7:16

    Explore GsonObject, GsonArray, GsonArrayAggregate, GsonValue, GsonContains, and Open Json functions in Azure SQL, from creating the orders table to parsing Json into relational data.

  • REGEXP_LIKE1:12

    Begin a module on regular expressions and the reg exp like function to test whether a string matches a defined pattern, such as a book title format, a smarter check.

  • REGEXP_REPLACE1:24

    Master regular expression replace by learning how it finds text matching a pattern and substitutes it with new text, such as removing non-digit characters from phone numbers.

  • REGEXP_SUBSTR1:16

    Learn how the regular expression substring (regexp_substr) extracts the exact substring that matches a regex pattern, like a spotlight pulling out the targeted text from a long string.

  • REGEXP_COUNT1:07
  • REGEXP_MATCHES1:06
  • REGEXP_INSTR1:08
  • REGEXP_SPLIT_TO_TABLE1:16

    Explore how regular expression split to table turns a long string into multiple rows based on a regex pattern, such as breaking a sentence into separate words for analysis.

  • Regular Expression Hands-On20:44

    Explore regular expressions in Apache Spark on Azure Synapse Analytics, applying regex like replace, instr, count, match, and split to table to transform and mask data.

  • EDIT_DISTANCE1:25

    Learn how edit distance powers fuzzy string matching by counting edits needed to turn one word into another, such as color versus colour, to measure string similarity.

  • JARO_WINKLER _DISTANCE1:17

    Explore gyro-winkler distance, a string similarity metric scoring how alike two strings are on a 0 to 1 scale, with higher scores for matching beginnings like jon, jonathan, and john.

  • EDIT_DISTANCE_SIMILARITY1:22

    Explore edit distance similarity and how edits translate to a similarity score between 0 and 1, illustrated by comparing close spellings and an 83% similarity example.

  • Fuzzy string matching Hands-On6:34

    Learn fuzzy string matching with edit distance, edit distance similarity, and gyro Winkler distance to cleanse data, deduplicate records, and match similar names like John and Jonathan.

  • Common Table Expressions (CTEs)1:21

    Explore T-SQL with common table expressions (CTEs) by defining a temporary result set using the with keyword, then reuse it to simplify queries such as listing employees and identifying managers.

  • Common Table Expressions (CTEs) Hands-On2:46

    Practice creating a common table expression (cte) using the with clause to simplify a complex query, create a SalesData table and validate results against a manual group by calculation.

  • Window Functions1:06

    Learn how window functions compute ranks, totals, and averages across a set of rows without collapsing the data, using row_number and similar analytics while preserving the full dataset.

  • Window Functions Hands-On3:26
  • Graph Queries with MATCH Operator1:02

    Explore graph queries with the match operator to trace friendships and connections among students, using nodes and edges to reveal linked relationships in a social network.

  • Views1:08

    Explore programmability objects through views, saved queries that expose selected data without storing it. Create views to summarize regional states totals, making queries easier, safer, and reusable.

  • Views Hands-On8:51

    Explains how SQL views act as virtual tables to secure data, simplify complex queries, and aggregate information, demonstrated by creating, altering, and dropping views for IT employees.

  • Scalar Functions1:05
  • Scalar Functions Hands-On4:50
  • Table-valued Functions1:13

    Explore table-valued functions, reusable logic that returns a full table of rows and columns. Use tvf to simplify queries and reuse logic, such as listing top selling products.

  • Table-valued Functions Hands-On5:37
  • Stored Procedures1:10

    Master stored procedures, pre-compiled SQL scripts stored in the database that run with a single command, enabling reusable, centralized, efficient, and secure database task execution such as regional sales queries.

  • Stored Procedures Hands-On6:14
  • Triggers1:08

    Explore triggers, SQL objects that fire automatically on insert, update, or delete events to enforce rules and maintain data integrity. Learn to log changes with these automatic alarms.

  • Triggers Hands-On4:17

    Create and test triggers in the Azure portal to automate auditing of insert, update, and delete operations, logging changes into an audit table.

  • GitHub Copilot in Fabric1:12

    Discover how GitHub Copilot in Fabric reads context and auto-suggests the next lines. It speeds up development, reduces errors, and makes coding more intuitive for T-SQL queries.

  • Microsoft Copilot in Fabric1:10
  • Model Context Protocol (MCP)1:14

    Learn how the Model Context Protocol, or MCP, securely connects AI to tools and data, providing relevant text for accurate SQL queries and safer development.

Requirements

  • Basic knowledge of SQL syntax and relational databases.
  • Familiarity with Microsoft SQL Server or Azure SQL Database.
  • No prior AI or advanced database experience required — course starts from fundamentals.

Description

Are you preparing for the Microsoft DP-800: SQL AI Developer Associate certification? This ultimate course is designed to take you from foundational concepts to advanced, exam-ready skills with a perfect blend of theory and hands-on practice.

You’ll begin by mastering database objects such as tables, indexes, column store indexes, and sequences, while learning how to enforce integrity with constraints like PRIMARY KEY, FOREIGN KEY, UNIQUE, CHECK, and DEFAULT. From there, you’ll explore specialized tables including in-memory, temporal, external, ledger, and graph tables.

The course dives deep into JSON support and functions, regular expressions, and fuzzy string matching to help you handle modern data challenges. You’ll sharpen your T-SQL skills with Common Table Expressions (CTEs), window functions, and graph queries using the MATCH operator. Programmability objects such as views, scalar functions, table-valued functions, stored procedures, and triggers are covered in detail.

Security and optimization are a major focus: you’ll implement encryption, dynamic data masking, row-level security, auditing, and passwordless access, while learning to resolve deadlocks, blocking issues, and optimize queries using DMVs, Query Store, and Query Performance Insight. You’ll also gain practical exposure to CI/CD practices with SQL Database Projects, secrets management, schema drift detection, and deployment pipelines.

Finally, the course equips you with cutting-edge AI capabilities: embeddings, vector search, semantic search, Retrieval-Augmented Generation (RAG), and integration with GitHub Copilot, Microsoft Copilot, and Model Context Protocol (MCP). You’ll learn how to design intelligent search solutions, build hybrid search strategies, and connect SQL with Azure services like Data API Builder, Application Insights, and Logic Apps.

Every theory concept is reinforced with hands-on demonstrations — from creating tables and indexes to implementing encryption, auditing, concurrency controls, and AI-powered search. By the end, you’ll not only be exam-ready but also capable of building secure, optimized, and AI-enabled SQL solutions for real-world scenarios.


Enroll now and transform your SQL expertise into AI-driven database mastery!

Who this course is for:

  • Database professionals who want to expand beyond administration into AI‑enabled SQL development.
  • SQL developers aiming to master advanced T‑SQL, JSON, regex, and graph queries.
  • Data engineers seeking hands‑on experience with CI/CD, performance tuning, and Azure integration.
  • Students and beginners with basic SQL knowledge who want structured, step‑by‑step guidance.
  • Cloud practitioners working with Microsoft Azure SQL Database, Fabric, and related services.

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