This post is a part of the DP-800: Developing AI-Enabled Database Solutions Exam Prep Hub.
This topic falls under these sections:
Implement AI capabilities in database solutions (25–30%)
   --> Design and implement models and embeddings
      --> Identify which columns to include in embeddings

Note that there are 10 practice questions (with answers) at the end of each section to help you solidify your knowledge of the material. Also, there are 4 practice tests with 30 questions each available from the hub's main page below the exam topics section.

Introduction

Embeddings are one of the most important technologies behind modern AI-powered applications such as semantic search, Retrieval-Augmented Generation (RAG), recommendation systems, document similarity, and intelligent chatbots. Rather than storing simple keywords, embeddings represent text as high-dimensional numerical vectors that capture semantic meaning.

One of the most important design decisions when implementing embeddings is determining which database columns should be embedded. Selecting the appropriate columns directly affects:

• Search relevance
• AI response quality
• Storage requirements
• Embedding generation cost
• Update frequency
• Query performance

The DP-800 exam expects candidates to understand how to evaluate database schemas and determine which columns should contribute meaningful semantic information to an embedding.

Why Column Selection Matters

Every embedding represents the semantic meaning of the text supplied to the embedding model.

For example, consider a Products table.

If only Name is embedded:

Surface Laptop

the embedding contains very little context.

If Name + Description + Category are embedded:

Surface Laptop
Lightweight business laptop with AI features.
Laptop

the vector contains much richer semantic information.

This significantly improves semantic search accuracy.

General Rule

Good embedding columns contain:

• Meaning
• Context
• Natural language
• Descriptive text

Poor embedding columns contain:

• IDs
• Numbers
• Codes
• Random values
• Technical metadata

Common Column Types

Excellent Candidates

These almost always improve embeddings.

Examples include:

• Product descriptions
• Document bodies
• Knowledge base articles
• Support tickets
• FAQs
• Customer reviews
• Employee biographies
• Blog articles
• Research papers
• Medical notes
• Legal clauses

These contain rich natural language.

Example

"This laptop includes an NPU for AI acceleration and 18-hour battery life."

An embedding model can understand concepts such as:

• AI laptop
• battery life
• portable computer
• Windows device

Very Good Candidates

These provide additional context.

Examples

• Product Name
• Job Title
• Category
• Department
• Brand
• Tags
• Keywords
• Topics

Example

Category:
Gaming Laptop
Brand:
Contoso
Description:
High-performance RTX graphics...

Combining these improves semantic similarity.

Sometimes Useful

Examples

• City
• Country
• Industry
• Region
• Language

These may improve retrieval depending on the application.

For example

Restaurant
Italian
Orlando

provides valuable context.

Usually Poor Candidates

These rarely improve embeddings.

Examples

• Identity columns
• GUIDs
• Invoice numbers
• SKUs
• Phone numbers
• ZIP codes
• Timestamps
• Row versions

Example

CustomerID = 28471

This carries no semantic meaning.

Never Useful

Examples

• Binary files
• Password hashes
• Encryption keys
• Checksums
• Internal IDs

These should never be embedded.

Combining Multiple Columns

Rather than embedding each column individually, applications often concatenate multiple descriptive columns into one text document before generating the embedding.

Example

Instead of embedding

Title

only

combine

Title
Category
Description
Features

Example generated text

Surface Laptop
Category:
Business Laptop
Features:
Touchscreen
NPU
AI Copilot+
18-hour battery
Description:
Premium lightweight laptop designed for professionals...

This produces much richer embeddings.

Typical SQL Example

SELECT
Name +
CHAR(13) +
Description +
CHAR(13) +
Category
AS EmbeddingText
FROM Products;

This creates a single block of text for embedding generation.

Avoid Including Irrelevant Data

Do not include unrelated information simply because it exists.

Bad example

Description
LastModifiedDate
ModifiedBy
RecordID
Checksum
InternalVersion

Only Description contributes semantic meaning.

Include Business Context

Business metadata often improves search.

Instead of

Description

consider

Product Name
Category
Brand
Description
Features

The additional context helps distinguish similar products.

Example: Knowledge Base

Table

Good embedding text

Title
Problem Description
Solution

Avoid

Author

unless searching by author.

Example: HR Resume Database

Good columns

• Name
• Skills
• Certifications
• Experience
• Summary

Poor columns

• EmployeeID
• HireDate
• PayrollNumber

Example: Healthcare

Useful

• Diagnosis
• Symptoms
• Treatment
• Clinical Notes

Not useful

• Patient ID
• Visit Number
• Insurance Number

Example: Retail

Useful

• Product Name
• Description
• Category
• Features
• Brand

Not useful

• Inventory Count
• Warehouse Bin
• SKU

Example: Support Tickets

Useful

• Ticket Title
• Problem Description
• Resolution

Avoid

• Ticket Number
• Assigned Agent ID
• Status Code

Structured Data vs Natural Language

Embedding models perform best with natural language.

Instead of

RAM=32
CPU=i9
GPU=RTX4090

consider

This gaming laptop includes 32 GB RAM, an Intel Core i9 processor, and an NVIDIA RTX 4090 GPU.

Natural language improves semantic understanding.

Sensitive Information

Avoid embedding:

• Passwords
• SSNs
• Credit card numbers
• API keys
• Authentication tokens
• Encryption secrets

Even if embeddings are stored securely, unnecessary sensitive information should never be included.

Column Size Considerations

Very large text columns increase:

• Token count
• API costs
• Storage
• Processing time

Strategies include:

• Chunk long documents
• Remove boilerplate text
• Eliminate duplicate content
• Exclude irrelevant sections

Embedding Maintenance

If embedded columns change frequently:

• Regenerate embeddings
• Track changes using Change Tracking or CDC
• Automate updates using Azure Functions, Logic Apps, or Microsoft Foundry

Only regenerate embeddings when relevant columns change.

SQL Server 2025 and Azure SQL

Modern SQL AI solutions support storing vectors directly in SQL databases, allowing developers to:

• Store embeddings in vector columns
• Perform vector similarity searches
• Integrate external embedding models
• Build semantic search and RAG applications entirely within SQL-centric architectures

Choosing the correct columns is one of the most important design decisions before generating these vectors.

Best Practices

• Embed descriptive text instead of identifiers.
• Combine multiple related text columns into a single embedding input.
• Include contextual metadata such as category or brand when it improves retrieval.
• Exclude numeric identifiers, timestamps, and internal metadata.
• Avoid embedding sensitive or confidential information.
• Chunk long documents before generating embeddings.
• Keep embeddings synchronized with changes to the source columns.
• Evaluate search quality periodically and refine the selected columns if retrieval results are poor.

DP-800 Exam Tips

For the exam, you should be able to:

• Identify which columns provide meaningful semantic content.
• Recognize when multiple columns should be combined into one embedding.
• Distinguish between descriptive text and operational metadata.
• Explain why identifiers and numeric values generally should not be embedded.
• Understand the tradeoffs between embedding more context versus increasing token usage and storage costs.
• Recommend strategies for maintaining embeddings when source data changes.
• Select appropriate columns for common business scenarios such as product catalogs, knowledge bases, customer support systems, healthcare records, HR profiles, and document repositories.

Practice Exam Questions

Question 1

A company is building a semantic search application for an online product catalog. The Products table contains the following columns:

• ProductID
• ProductName
• Category
• Description
• Price
• SKU

Which combination of columns should be included when generating embeddings?

A. ProductID, SKU, Price

B. ProductName, Category, Description

C. ProductID, ProductName, Price

D. SKU, Category, Price

Correct Answer: B

Explanation

Embeddings should be generated from columns containing meaningful natural language and business context. Product names, categories, and descriptions provide semantic information that improves search relevance.

Why the other answers are incorrect:

• A: IDs, SKUs, and prices contain little semantic meaning.
• C: ProductID and Price contribute little to semantic understanding.
• D: SKU and Price are operational values, not descriptive content.

Question 2

A knowledge base stores the following columns:

• ArticleTitle
• ProblemDescription
• Resolution
• CreatedDate
• ArticleID

The organization wants to optimize AI-powered question answering.

Which columns should be embedded?

A. CreatedDate and ArticleID

B. Resolution only

C. ArticleTitle, ProblemDescription, and Resolution

D. ArticleID, Resolution, and CreatedDate

Correct Answer: C

Explanation

The title, problem description, and resolution contain the contextual information required for semantic retrieval.

Why the other answers are incorrect:

• A: Dates and IDs provide no semantic value.
• B: Using only the resolution omits important context.
• D: IDs and dates should generally not be embedded.

Question 3

An HR department is building an AI assistant to help recruiters locate qualified candidates.

Which candidate profile columns are most appropriate for embeddings?

A. EmployeeID, HireDate, PayrollNumber

B. ResumeSummary, Skills, Certifications, Experience

C. Salary, EmployeeID, OfficeNumber

D. ManagerID, DepartmentID, HireDate

Correct Answer: B

Explanation

Resume summaries, skills, certifications, and experience contain rich natural language describing candidate qualifications.

Why the other answers are incorrect:

• A: Administrative identifiers have no semantic value.
• C: Salary and IDs are not useful for semantic similarity.
• D: Organizational metadata does not describe expertise.

Question 4

A retail company stores product specifications as structured fields:

• RAM = 32
• CPU = Intel Core i9
• GPU = RTX 4090

What is the best practice before generating embeddings?

A. Embed each numeric value separately.

B. Ignore the specification fields completely.

C. Store each field in separate vector columns.

D. Convert the structured values into descriptive natural language.

Correct Answer: D

Explanation

Embedding models understand natural language far better than isolated structured values. Converting structured data into descriptive text produces higher-quality embeddings.

Why the other answers are incorrect:

• A: Individual values lose important context.
• B: Specifications provide valuable search information.
• C: Multiple separate embeddings reduce semantic completeness.

Question 5

A legal document repository contains these columns:

• DocumentTitle
• ContractText
• Author
• VersionNumber
• LastModifiedDate

Which columns are most appropriate for embeddings?

A. DocumentTitle and ContractText

B. VersionNumber and LastModifiedDate

C. Author and VersionNumber

D. LastModifiedDate and Author

Correct Answer: A

Explanation

Titles and contract text provide meaningful semantic information for legal document retrieval.

Why the other answers are incorrect:

• B: Version numbers and dates are operational metadata.
• C: Author names generally contribute little to semantic similarity.
• D: Dates and authors are rarely useful for AI retrieval.

Question 6

A company stores customer support tickets with these columns:

• TicketID
• Title
• Description
• Resolution
• AssignedEngineer

Which column should generally not be included when generating embeddings?

A. Resolution

B. Description

C. AssignedEngineer

D. Title

Correct Answer: C

Explanation

The assigned engineer is administrative information and usually has no relationship to the semantic content of the issue.

Why the other answers are incorrect:

• A: Resolutions provide valuable context.
• B: Descriptions are one of the best embedding sources.
• D: Titles summarize the issue.

Question 7

A company wants to reduce embedding generation costs while maintaining high-quality semantic search.

Which strategy is most appropriate?

A. Embed every column in every table.

B. Embed only columns containing meaningful business context and descriptive text.

C. Embed only numeric columns.

D. Embed every database row regardless of relevance.

Correct Answer: B

Explanation

Embedding only semantically meaningful columns minimizes storage, token usage, and generation costs while maintaining search quality.

Why the other answers are incorrect:

• A: Creates unnecessary costs.
• C: Numeric values rarely provide semantic meaning.
• D: Irrelevant data wastes resources.

Question 8

A medical database contains the following fields:

• PatientID
• Diagnosis
• Symptoms
• TreatmentPlan
• InsurancePolicyNumber

Which field should not normally be included in embeddings?

A. Symptoms

B. Diagnosis

C. InsurancePolicyNumber

D. TreatmentPlan

Correct Answer: C

Explanation

Insurance policy numbers are identifiers and contribute no semantic value to AI retrieval.

Why the other answers are incorrect:

• A: Symptoms are highly descriptive.
• B: Diagnoses contain important clinical meaning.
• D: Treatment plans improve retrieval relevance.

Question 9

An organization notices that AI search results have become less accurate after adding several metadata columns to the embedding input.

What is the most likely cause?

A. Semantic quality decreased because irrelevant metadata diluted the embedding.

B. Embedding vectors became encrypted.

C. The embedding model requires additional indexes.

D. SQL Server cannot process metadata columns.

Correct Answer: A

Explanation

Adding irrelevant metadata introduces noise into the embedding, reducing its ability to represent the true semantic meaning of the content.

Why the other answers are incorrect:

• B: Embeddings are not automatically encrypted by adding metadata.
• C: Indexes improve retrieval speed, not embedding quality.
• D: SQL Server can process metadata columns; they simply should not be embedded unnecessarily.

Question 10

A company stores lengthy product manuals exceeding the token limit of its embedding model.

What is the recommended approach?

A. Truncate every document to the first paragraph.

B. Increase the vector dimension.

C. Store the manuals without embeddings.

D. Divide the manuals into logical chunks before generating embeddings.

Correct Answer: D

Explanation

Chunking large documents allows each section to remain within model limits while preserving semantic meaning. It also improves retrieval because searches return only the most relevant portions of a document.

Why the other answers are incorrect:

• A: Truncation discards valuable information.
• B: Vector dimensions do not increase a model’s token limit.
• C: Without embeddings, semantic search cannot be performed effectively.

DP-800 Exam Tips

For the exam, remember these key principles:

• Embed descriptive, natural-language columns such as names, descriptions, summaries, articles, reviews, and documentation.
• Avoid identifiers and operational metadata, including IDs, SKUs, timestamps, version numbers, and status codes.
• Combine related descriptive columns (for example, title + category + description) into a single text input to provide richer context.
• Convert structured specifications into natural language when appropriate to improve semantic understanding.
• Exclude sensitive information unless absolutely required and permitted by your security policies.
• Chunk large documents before generating embeddings to stay within model token limits and improve retrieval quality.
• Review embedding quality periodically and refine the selected columns based on search relevance and user feedback.

Go to the DP-800 Exam Prep Hub main page