Skip to content

Commit ec065c7

Browse files
JamesJBarnettJamesJBarnett
authored
Merge pull request #53618 from MScalopez/DP-800-final
DP-800 - Added 5 new modules for the new DP-800 cert
2 parents e08f621 + 1022404 commit ec065c7

100 files changed

Lines changed: 4230 additions & 0 deletions

File tree

Some content is hidden

Large Commits have some content hidden by default. Use the searchbox below for content that may be hidden.

‎learn-pr/paths/implement-ai-capabilities-database-solutions/index.yml‎

Lines changed: 33 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,33 @@
1+
### YamlMime:LearningPath
2+
uid: learn.wwl.implement-ai-capabilities-database-solutions
3+
metadata:
4+
title: Implement AI capabilities in database solutions
5+
description: Learn to design intelligent search, work with models and embeddings, and build Retrieval Augmented Generation (RAG) patterns in T-SQL.
6+
ms.date: 02/05/2026
7+
author: calopez
8+
ms.author: calopez
9+
ms.topic: learning-path
10+
title: Implement AI capabilities in database solutions
11+
prerequisites: |
12+
Before starting this learning path, you should have experience working with Azure SQL Database or SQL Server, writing Transact-SQL queries, and a general understanding of AI concepts.
13+
summary: |
14+
This learning path explores how to implement AI capabilities directly in Azure SQL Database. You learn to design intelligent search using full-text and vector search, integrate AI models and embeddings, and build Retrieval Augmented Generation (RAG) solutions entirely in T-SQL.
15+
iconUrl: /training/achievements/generic-trophy.svg
16+
levels:
17+
- intermediate
18+
roles:
19+
- developer
20+
- data-engineer
21+
products:
22+
- azure-sql-database
23+
- sql-server
24+
- ai-services
25+
subjects:
26+
- artificial-intelligence
27+
- databases
28+
modules:
29+
- learn.wwl.design-implement-intelligent-search-with-sql
30+
- learn.wwl.design-implement-models-embeddings-with-sql
31+
- learn.wwl.design-implement-rag-with-sql
32+
trophy:
33+
uid: learn.wwl.implement-ai-capabilities-database-solutions.badge

‎learn-pr/wwl-data-ai/design-implement-intelligent-search-with-sql/01-introduction.yml‎

Lines changed: 13 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,13 @@
1+
### YamlMime:ModuleUnit
2+
uid: learn.wwl.design-implement-intelligent-search-with-sql.introduction
3+
title: Introduction
4+
metadata:
5+
title: Introduction
6+
description: Get started with intelligent search capabilities in SQL Server and Azure SQL Database.
7+
author: calopez
8+
ms.author: calopez
9+
ms.date: 02/03/2026
10+
ms.topic: unit
11+
durationInMinutes: 3
12+
content: |
13+
[!include[](includes/01-introduction.md)]

‎learn-pr/wwl-data-ai/design-implement-intelligent-search-with-sql/02-choose-intelligent-search-approach.yml‎

Lines changed: 13 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,13 @@
1+
### YamlMime:ModuleUnit
2+
uid: learn.wwl.design-implement-intelligent-search-with-sql.choose-intelligent-search-approach
3+
title: Choose an intelligent search approach
4+
metadata:
5+
title: Choose an intelligent search approach
6+
description: Learn how to choose between full-text, semantic vector, and hybrid search approaches for your SQL workloads.
7+
author: calopez
8+
ms.author: calopez
9+
ms.date: 02/03/2026
10+
ms.topic: unit
11+
durationInMinutes: 8
12+
content: |
13+
[!include[](includes/02-choose-intelligent-search-approach.md)]

‎learn-pr/wwl-data-ai/design-implement-intelligent-search-with-sql/03-implement-full-text-search.yml‎

Lines changed: 13 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,13 @@
1+
### YamlMime:ModuleUnit
2+
uid: learn.wwl.design-implement-intelligent-search-with-sql.implement-full-text-search
3+
title: Implement full-text search
4+
metadata:
5+
title: Implement full-text search
6+
description: Learn how to implement full-text search for keyword-based queries in SQL Server and Azure SQL Database.
7+
author: calopez
8+
ms.author: calopez
9+
ms.date: 02/03/2026
10+
ms.topic: unit
11+
durationInMinutes: 8
12+
content: |
13+
[!include[](includes/03-implement-full-text-search.md)]

‎learn-pr/wwl-data-ai/design-implement-intelligent-search-with-sql/04-prepare-sql-vector-search.yml‎

Lines changed: 13 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,13 @@
1+
### YamlMime:ModuleUnit
2+
uid: learn.wwl.design-implement-intelligent-search-with-sql.prepare-sql-vector-search
3+
title: Prepare SQL for vector search
4+
metadata:
5+
title: Prepare SQL for vector search
6+
description: Learn how to design for vector data including vector data type, indexes, size, and choosing between ANN and ENN search.
7+
author: calopez
8+
ms.author: calopez
9+
ms.date: 02/03/2026
10+
ms.topic: unit
11+
durationInMinutes: 10
12+
content: |
13+
[!include[](includes/04-prepare-sql-vector-search.md)]

‎learn-pr/wwl-data-ai/design-implement-intelligent-search-with-sql/05-implement-vector-search-query-patterns.yml‎

Lines changed: 13 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,13 @@
1+
### YamlMime:ModuleUnit
2+
uid: learn.wwl.design-implement-intelligent-search-with-sql.implement-vector-search-query-patterns
3+
title: Implement vector search query patterns
4+
metadata:
5+
title: Implement vector search query patterns
6+
description: Learn how to implement vector search using VECTOR_NORMALIZE, VECTOR_DISTANCE, VECTORPROPERTY, and VECTOR_SEARCH functions.
7+
author: calopez
8+
ms.author: calopez
9+
ms.date: 02/03/2026
10+
ms.topic: unit
11+
durationInMinutes: 8
12+
content: |
13+
[!include[](includes/05-implement-vector-search-query-patterns.md)]

‎learn-pr/wwl-data-ai/design-implement-intelligent-search-with-sql/06-implement-hybrid-search-ranking.yml‎

Lines changed: 13 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,13 @@
1+
### YamlMime:ModuleUnit
2+
uid: learn.wwl.design-implement-intelligent-search-with-sql.implement-hybrid-search-ranking
3+
title: Implement hybrid search and ranking
4+
metadata:
5+
title: Implement hybrid search and ranking
6+
description: Learn how to implement hybrid search combining full-text and vector search, and use Reciprocal Rank Fusion for result ranking.
7+
author: calopez
8+
ms.author: calopez
9+
ms.date: 02/03/2026
10+
ms.topic: unit
11+
durationInMinutes: 10
12+
content: |
13+
[!include[](includes/06-implement-hybrid-search-ranking.md)]

‎learn-pr/wwl-data-ai/design-implement-intelligent-search-with-sql/07-exercise-implement-intelligent-search.yml‎

Lines changed: 13 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,13 @@
1+
### YamlMime:ModuleUnit
2+
uid: learn.wwl.design-implement-intelligent-search-with-sql.exercise-implement-intelligent-search
3+
title: Exercise - Implement intelligent search with full-text, vector, and hybrid queries
4+
metadata:
5+
title: Exercise - Implement intelligent search with full-text, vector, and hybrid queries
6+
description: Practice implementing full-text search, vector search, and hybrid search with Reciprocal Rank Fusion in SQL.
7+
author: calopez
8+
ms.author: calopez
9+
ms.date: 02/03/2026
10+
ms.topic: unit
11+
durationInMinutes: 30
12+
content: |
13+
[!include[](includes/07-exercise-implement-intelligent-search.md)]

‎learn-pr/wwl-data-ai/design-implement-intelligent-search-with-sql/08-knowledge-check.yml‎

Lines changed: 71 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,71 @@
1+
### YamlMime:ModuleUnit
2+
uid: learn.wwl.design-implement-intelligent-search-with-sql.knowledge-check
3+
title: Knowledge check
4+
metadata:
5+
title: Knowledge check
6+
description: "Knowledge check"
7+
ms.date: 02/03/2026
8+
author: calopez
9+
ms.author: calopez
10+
ms.topic: unit
11+
durationInMinutes: 5
12+
content: |
13+
quiz:
14+
title: "Check your knowledge"
15+
questions:
16+
- content: "Which full-text predicate finds documents containing words that are inflectional forms of a search term, such as finding 'running' when searching for 'run'?"
17+
choices:
18+
- content: "CONTAINS with FORMSOF(INFLECTIONAL)"
19+
isCorrect: false
20+
explanation: "Incorrect. CONTAINS with FORMSOF(INFLECTIONAL) does support inflectional matching, but it's not the only option."
21+
- content: "FREETEXT"
22+
isCorrect: false
23+
explanation: "Incorrect. FREETEXT automatically expands to inflectional forms, but it's not the only option."
24+
- content: "Both CONTAINS with FORMSOF(INFLECTIONAL) and FREETEXT"
25+
isCorrect: true
26+
explanation: "Correct. FREETEXT automatically expands to inflectional forms, and CONTAINS can do this explicitly using FORMSOF(INFLECTIONAL, term)."
27+
- content: "Neither - inflectional matching requires vector search"
28+
isCorrect: false
29+
explanation: "Incorrect. Full-text search in SQL Server supports inflectional matching through both FREETEXT and CONTAINS with FORMSOF."
30+
- content: "What is the range of cosine distance values when comparing two vectors?"
31+
choices:
32+
- content: "-1 to 1"
33+
isCorrect: false
34+
explanation: "Incorrect. This range describes cosine similarity, not cosine distance."
35+
- content: "0 to 1"
36+
isCorrect: false
37+
explanation: "Incorrect. Cosine distance has a wider range than 0 to 1."
38+
- content: "0 to 2"
39+
isCorrect: true
40+
explanation: "Correct. Cosine distance ranges from 0 (identical vectors) to 2 (completely opposite vectors)."
41+
- content: "0 to infinity"
42+
isCorrect: false
43+
explanation: "Incorrect. This range describes Euclidean distance, not cosine distance."
44+
- content: "Which function should be used for vector search on a table with 500,000 rows when query speed is the priority?"
45+
choices:
46+
- content: "VECTOR_DISTANCE"
47+
isCorrect: false
48+
explanation: "Incorrect. VECTOR_DISTANCE performs exact search by calculating distance to every row, which is slow for large datasets."
49+
- content: "VECTOR_SEARCH"
50+
isCorrect: true
51+
explanation: "Correct. VECTOR_SEARCH uses a vector index (DiskANN) for approximate nearest neighbor search, which is much faster for large datasets."
52+
- content: "VECTOR_NORMALIZE"
53+
isCorrect: false
54+
explanation: "Incorrect. VECTOR_NORMALIZE scales vectors to unit length but doesn't perform search operations."
55+
- content: "FREETEXTTABLE"
56+
isCorrect: false
57+
explanation: "Incorrect. FREETEXTTABLE is used for full-text search, not vector search."
58+
- content: "What problem does Reciprocal Rank Fusion (RRF) solve in hybrid search?"
59+
choices:
60+
- content: "It improves the speed of vector search"
61+
isCorrect: false
62+
explanation: "Incorrect. RRF is a ranking algorithm, not a performance optimization for vector search."
63+
- content: "It combines ranked results from different sources without requiring score normalization"
64+
isCorrect: true
65+
explanation: "Correct. RRF merges ranked lists from full-text and vector search by using rank positions instead of raw scores, avoiding the problem of different scoring scales and preventing one source from dominating."
66+
- content: "It converts full-text results to vectors"
67+
isCorrect: false
68+
explanation: "Incorrect. RRF doesn't convert data types. It combines ranked results from multiple search methods."
69+
- content: "It creates full-text indexes automatically"
70+
isCorrect: false
71+
explanation: "Incorrect. RRF is a ranking fusion algorithm, not an indexing feature."

‎learn-pr/wwl-data-ai/design-implement-intelligent-search-with-sql/09-summary.yml‎

Lines changed: 13 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,13 @@
1+
### YamlMime:ModuleUnit
2+
uid: learn.wwl.design-implement-intelligent-search-with-sql.summary
3+
title: Summary
4+
metadata:
5+
title: Summary
6+
description: Review what you learned about designing and implementing intelligent search for Azure SQL.
7+
author: calopez
8+
ms.author: calopez
9+
ms.date: 02/03/2026
10+
ms.topic: unit
11+
durationInMinutes: 2
12+
content: |
13+
[!include[](includes/09-summary.md)]

0 commit comments

Comments
 (0)