Tag: full-text search

AI, Databases, DP-800, Microsoft Certification August 1, 2026

Choose from full-text, semantic vector, and hybrid search (DP-800 Exam Prep)

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 intelligent search
      --> Choose from full-text, semantic vector, and hybrid search

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

One of the most important skills measured on the DP-800 exam is knowing which search technology is appropriate for different AI-enabled database scenarios. Modern applications no longer rely solely on keyword matching. Instead, they increasingly combine traditional SQL capabilities with semantic understanding powered by embeddings and vector databases.

Microsoft SQL Server 2025, Azure SQL Database, Azure SQL Managed Instance, Azure AI Search, and Microsoft Fabric all support architectures that combine relational data with AI-powered retrieval.

The DP-800 exam expects candidates to understand:

Traditional Full-Text Search
Semantic Vector Search
Hybrid Search
When each technique should be selected
Advantages and disadvantages of each approach
How embeddings enable semantic retrieval
How intelligent search supports Retrieval-Augmented Generation (RAG)

Understanding the strengths and weaknesses of each search strategy is critical because choosing the wrong approach can significantly reduce application quality, increase cost, or degrade performance.

Why Intelligent Search Matters

Traditional databases are excellent at retrieving structured information.

For example:

Find all customers named Smith.

Find invoices created after January 1.

However, AI applications often ask questions like:

Which support ticket is similar to this one?
Find documents about password recovery.
Find articles discussing authentication failures.
Recommend products similar to this description.

These questions require understanding meaning, not merely matching characters.

This is why semantic search has become an essential component of modern database applications.

Three Primary Search Approaches

Microsoft generally categorizes intelligent search into three approaches:

Full-Text Search
Semantic Vector Search
Hybrid Search

Each solves a different problem.

Full-Text Search

Full-text search is Microsoft’s traditional text search technology.

Instead of scanning every row with LIKE comparisons, SQL Server builds specialized indexes that understand words and language.

Example:

			
Find all documents containing:
database
security
Azure

Rather than performing:

WHERE Description LIKE '%Azure%'

Full-text indexes tokenize words and search efficiently.

Full-Text Search Features

Supports:

Word searches
Phrase searches
Prefix searches
Inflectional forms
Language-specific stemming
Stop words
Ranking

Example:

Searching for

run

may also find

running
runs
ran

depending on language settings.

Full-Text Index Architecture

A full-text index stores:

Tokens
Word locations
Linguistic metadata

instead of raw text.

This allows much faster retrieval than LIKE queries.

Common Full-Text Functions

Examples include:

CONTAINS()

FREETEXT()

CONTAINSTABLE()

FREETEXTTABLE()

Example:

			
SELECT *
FROM Articles
WHERE CONTAINS(Content,'Azure');

Advantages of Full-Text Search

Advantages include:

Mature technology
Extremely fast keyword searches
Built directly into SQL Server
Efficient indexing
Supports ranking
Low storage overhead
Easy implementation

Limitations of Full-Text Search

It still relies primarily on matching words.

It does not understand meaning.

For example:

Search:

vehicle repair

A document containing

automobile maintenance

might not be returned.

Although synonyms can sometimes help, semantic understanding remains limited.

When Full-Text Search Is Best

Choose Full-Text Search when:

Exact words matter
Legal document searches
Product catalogs
Article searches
Documentation portals
Knowledge bases
Compliance systems

It excels when users know the terminology they are searching for.

Semantic Vector Search

Vector search is fundamentally different.

Instead of searching words, it searches meaning.

The process is:

Text

↓

Embedding model

↓

Vector

↓

Similarity search

Every document becomes a numerical representation.

Example:

"Reset your password"

becomes

			
[0.183,
-0.912,
0.447,
...]

The numbers themselves are not important.

Their relative position in vector space is.

Embeddings Power Semantic Search

Embedding models place similar concepts near each other.

For example:

Dog

and

Puppy

produce vectors close together.

Likewise:

Laptop

and

Notebook computer

may generate highly similar vectors.

The model learns semantic relationships.

Similarity Search

Rather than asking:

“Does this document contain this word?”

Vector search asks:

“Which vectors are closest?”

Similarity is commonly measured using:

Cosine similarity
Euclidean distance
Dot product

Cosine similarity is the most common metric.

Example

User asks:

“How do I recover my account?”

Stored article:

“Reset your password”

Even though no identical words exist, vector search recognizes the concepts are related.

This is impossible using ordinary keyword matching.

Advantages of Semantic Vector Search

Benefits include:

Understands meaning
Finds similar content
Supports natural language
Excellent for AI assistants
Ideal for RAG
Handles synonyms automatically
Better user experience

Limitations of Vector Search

Tradeoffs include:

Requires embedding models
Consumes more storage
Embedding generation costs compute
Requires vector indexes
More complex infrastructure
Results can occasionally be less predictable than exact keyword searches

Typical Use Cases

Vector search is ideal for:

AI chatbots
Enterprise search
Recommendation engines
Similar document retrieval
Customer support assistants
Semantic knowledge bases
Question answering systems
RAG architectures

Understanding Hybrid Search

Neither full-text nor vector search is perfect for every workload.

Hybrid search combines both approaches.

Instead of choosing one search method, the application performs:

Full-text search
Vector search

simultaneously.

Results are then merged and ranked.

This provides higher-quality search than either technique alone.

Why Hybrid Search Works

Imagine a user searches:

“Azure SQL backup”

Keyword search finds:

Azure SQL backup documentation

Vector search finds:

Disaster recovery guidance
Database restore procedures
Business continuity articles

Combining both returns a richer, more relevant result set.

Benefits of Hybrid Search

Hybrid search offers:

Higher recall
Better ranking
Exact keyword matches
Semantic understanding
More complete search results
Improved user satisfaction
Better grounding for AI responses

Hybrid Search in RAG

Retrieval-Augmented Generation depends heavily on retrieving the most relevant context.

Hybrid search often performs best because it retrieves:

Exact terminology
Related concepts
Similar documents

The LLM then generates an answer using higher-quality evidence.

This significantly reduces hallucinations.

Choosing the Right Search Method

Requirement	Best Choice
Exact keywords	Full-Text Search
SQL documentation search	Full-Text Search
Product SKU lookup	Full-Text Search
Semantic similarity	Vector Search
AI chatbot	Vector Search
Recommendation engine	Vector Search
RAG system	Hybrid Search
Enterprise search	Hybrid Search
Large knowledge base	Hybrid Search
Customer support assistant	Hybrid Search

Comparison Table

Feature	Full-Text	Vector	Hybrid
Keyword matching	Excellent	Poor	Excellent
Semantic understanding	No	Yes	Yes
Finds synonyms	Limited	Excellent	Excellent
Natural language queries	Limited	Excellent	Excellent
Requires embeddings	No	Yes	Yes
Requires vector index	No	Yes	Yes
Best for RAG	Fair	Good	Excellent
AI chatbot support	Limited	Excellent	Excellent
Traditional SQL workloads	Excellent	Moderate	Good
Complexity	Low	Medium	Higher

DP-800 Exam Tips

Remember these key distinctions:

Full-text search is optimized for exact words and phrases.
Vector search retrieves semantically similar content using embeddings.
Hybrid search combines keyword precision with semantic relevance.
Embeddings are required only for vector and hybrid search.
Hybrid search is generally the preferred approach for enterprise AI assistants and RAG solutions because it balances precision and recall.
LIKE queries are not substitutes for full-text indexes in large-scale search applications.
Expect scenario-based questions asking you to recommend the most appropriate search technology based on application requirements, performance, and user experience.

Practice Exam Questions

Question 1

A development team is building an enterprise knowledge base for an AI chatbot. Users ask questions in natural language, and the chatbot retrieves relevant documents before generating a response.

Which search approach should you recommend?

A. Full-text search only

B. Semantic vector search

C. LIKE queries

D. Indexed views

Correct Answer: B

Explanation:
Semantic vector search uses embeddings to retrieve documents based on meaning rather than exact keywords. This makes it ideal for AI chatbots and Retrieval-Augmented Generation (RAG). LIKE queries and indexed views do not provide semantic understanding, while full-text search is limited to keyword matching.

Question 2

A legal department maintains millions of contracts. Attorneys usually know the exact legal terms they are searching for and require fast, precise keyword matching.

Which search technology is the best fit?

A. Hybrid search

B. Semantic vector search

C. Full-text search

D. Azure AI embeddings only

Correct Answer: C

Explanation:
Full-text search is optimized for exact words, phrases, stemming, ranking, and efficient indexing. Since attorneys typically search using precise terminology, full-text search provides the best balance of performance and accuracy.

Question 3

A company stores product manuals and wants search results to include documents discussing “automobile maintenance” when users search for “car repair.”

Which search capability provides this behavior?

A. SQL LIKE operator

B. Clustered indexes

C. Full-text search only

D. Semantic vector search

Correct Answer: D

Explanation:
Semantic vector search retrieves content based on meaning instead of exact words. Because embedding models understand semantic relationships, they recognize that “car repair” and “automobile maintenance” describe similar concepts.

Question 4

A RAG application must retrieve documents that contain both exact product names and semantically similar troubleshooting articles.

Which search strategy should you recommend?

A. Full-text search

B. LIKE queries

C. Hybrid search

D. Clustered columnstore indexes

Correct Answer: C

Explanation:
Hybrid search combines full-text search with semantic vector search. Exact product names are retrieved through keyword matching, while related troubleshooting content is found using semantic similarity.

Question 5

Which characteristic is unique to semantic vector search?

A. It stores documents in XML format.

B. It searches using vector similarity instead of exact text matching.

C. It requires clustered indexes.

D. It eliminates the need for embeddings.

Correct Answer: B

Explanation:
Semantic vector search converts content into embeddings and compares vectors using similarity metrics such as cosine similarity. It does not rely on exact text matching.

Question 6

Your application must support searches for:

“running”
“runs”
“ran”

using a single search term.

Which technology provides this capability without AI embeddings?

A. Full-text search

B. Azure OpenAI

C. Semantic vector search

D. Azure AI Search only

Correct Answer: A

Explanation:
Full-text search supports stemming and inflectional forms, allowing different grammatical variations of a word to match automatically without requiring embeddings.

Question 7

Which similarity metric is most commonly associated with vector search?

A. SHA-256

B. CRC32

C. Cosine similarity

D. Binary comparison

Correct Answer: C

Explanation:
Cosine similarity is the most widely used metric for measuring how similar two embedding vectors are by comparing the angle between them rather than their magnitude.

Question 8

An organization wants users to receive highly relevant search results even when they misspell keywords or use different terminology.

Which search method generally provides the highest quality results?

A. LIKE queries

B. Full-text search only

C. Hybrid search

D. Primary key lookups

Correct Answer: C

Explanation:
Hybrid search combines keyword matching with semantic understanding, improving recall and relevance by returning both exact matches and conceptually related documents.

Question 9

A database developer asks why embeddings are required for semantic search.

What is the primary purpose of embeddings?

A. Encrypt database rows.

B. Compress database backups.

C. Replace SQL indexes.

D. Represent content numerically so semantic similarity can be calculated.

Correct Answer: D

Explanation:
Embeddings transform text into high-dimensional numerical vectors that capture semantic meaning. Similar vectors represent similar concepts, enabling semantic search.

Question 10

Which scenario is the strongest candidate for using hybrid search instead of only full-text search?

A. Searching employee IDs

B. Retrieving rows by primary key

C. Supporting an AI assistant that answers questions using company documentation

D. Looking up invoice numbers

Correct Answer: C

Explanation:
AI assistants benefit from hybrid search because they require both exact keyword matching and semantic understanding. Hybrid search improves document retrieval quality, which directly improves the quality of RAG-generated responses.

DP-800 Exam Tips

Full-text search is best for exact keywords, phrases, and language-aware searches using stemming and ranking.
Semantic vector search retrieves information based on meaning by comparing embeddings with similarity metrics such as cosine similarity.
Hybrid search combines keyword precision with semantic relevance and is generally the preferred approach for enterprise AI search and RAG solutions.
Embeddings are required for vector and hybrid search but not for traditional full-text search.
Expect scenario-based exam questions where you must recommend the most appropriate search technology based on user requirements, data type, query style, and application architecture.
Remember that LIKE queries are suitable only for simple pattern matching and are not a replacement for full-text or semantic search in large-scale intelligent applications.

Go to the DP-800 Exam Prep Hub main page

AI, Databases, DP-800, Microsoft Certification August 1, 2026

Implement full-text search (DP-800 Exam Prep)

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 intelligent search
      --> Implement full-text search

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

Full-text search is one of the foundational search technologies available in Microsoft SQL Server and Azure SQL Managed Instance. Unlike traditional SQL searches that rely on exact text matching through operators such as LIKE, full-text search provides a much more efficient and intelligent mechanism for searching large collections of textual data.

For the DP-800: Developing AI-Enabled Database Solutions exam, you should understand:

What full-text search is
When it should be used
How it works internally
Full-text indexes and catalogs
Supported query predicates and functions
Language-aware searching
Stoplists and thesaurus files
Ranking search results
Performance considerations
When to choose full-text search instead of vector or hybrid search

Although AI-powered semantic search is becoming increasingly popular, full-text search remains an important technology for applications that require fast keyword-based retrieval.

What Is Full-Text Search?

Full-text search is a SQL Server feature that enables efficient searching of large text columns.

Unlike:

WHERE Description LIKE '%backup%'

full-text search creates a specialized index that understands words rather than simple character sequences.

It supports searching within:

CHAR
VARCHAR
NCHAR
NVARCHAR
TEXT (legacy)
NTEXT (legacy)
XML
FILESTREAM documents through filters

Instead of scanning every row, SQL Server searches an optimized full-text index.

Why Traditional LIKE Queries Are Limited

Many developers initially use:

			
SELECT *
FROM Articles
WHERE Content LIKE '%security%'

Although this works, it has several disadvantages:

Table scans on large datasets
Poor performance
Cannot rank results
No language awareness
No stemming
No synonym support
Limited search capabilities

For enterprise search applications, LIKE queries do not scale effectively.

Benefits of Full-Text Search

Full-text search provides:

Fast keyword searches
Phrase searching
Prefix matching
Inflectional searches
Linguistic processing
Word breaking
Ranking of results
Stop word removal
Efficient indexing
Large-scale text retrieval

Full-Text Search Architecture

Several components work together.

Source Tables

Contain text data.

Example:

			
Articles
Products
KnowledgeBase
SupportTickets
Policies

		

Full-Text Index

Instead of indexing every character, SQL Server stores:

Tokens
Word positions
Language metadata

This dramatically speeds searches.

Full-Text Catalog

A full-text catalog is a logical container for one or more full-text indexes.

Modern SQL Server versions automatically manage catalogs, but understanding the concept remains important for the DP-800 exam.

Word Breakers

SQL Server separates text into words using language-specific rules.

Example:

SQL Server enables intelligent search.

becomes

			
SQL
Server
enables
intelligent
search

		

Different languages use different tokenization rules.

Stemmers

Stemmers recognize grammatical variations.

Searching:

run

may also find

running
runs
ran

depending on the configured language.

Enabling Full-Text Search

Before using full-text search:

Install Full-Text Search feature.
Create a unique key index.
Create a full-text catalog (optional in newer versions).
Create a full-text index.

Example:

			
CREATE FULLTEXT INDEX
ON Articles(Content)
KEY INDEX PK_Articles;

The index is then populated.

Full-Text Predicates

The DP-800 exam expects familiarity with common predicates.

CONTAINS()

Searches for precise words or phrases.

Example:

			
SELECT *
FROM Articles
WHERE CONTAINS(Content,'Azure');

Phrase Search

CONTAINS(Content,'"Azure SQL"')

Returns only rows containing the complete phrase.

Boolean Operators

Supports:

			
AND
OR
AND NOT

Example:

CONTAINS(Content,'"Azure" AND "Backup"')

Prefix Search

CONTAINS(Content,'"cloud*"')

Matches

cloud
clouds
cloud-based
clouding

Proximity Search

Finds words located near each other.

Example:

database NEAR backup

Useful when context matters.

FREETEXT()

Unlike CONTAINS(), FREETEXT searches for the meaning of words rather than exact expressions.

Example:

			
SELECT *
FROM Articles
WHERE FREETEXT(Content,'database recovery');

SQL Server automatically considers:

synonyms
stemming
inflectional forms

It is more natural-language oriented than CONTAINS().

Ranking Results

Often multiple documents match.

SQL Server can assign relevance rankings.

Functions include:

			
CONTAINSTABLE()
FREETEXTTABLE()

Example:

			
SELECT *
FROM CONTAINSTABLE
(
Articles,
Content,
'Azure'
)

		

Returns:

KEY
RANK

Applications can sort using the ranking score.

Stoplists

Certain words appear so frequently that indexing them offers little value.

Examples:

These are called stop words.

Stoplists improve:

Index size
Query performance
Search quality

Custom stoplists may also be created.

Thesaurus Files

SQL Server supports synonym expansion through thesaurus XML files.

Example:

Searching:

car

may automatically include

			
automobile
vehicle

This improves keyword searches without requiring embeddings.

Supported Languages

Full-text search supports dozens of languages.

Language-specific processing includes:

tokenization
stemming
stop words
word breakers

Examples include:

English
French
German
Spanish
Japanese
Chinese

Each language has its own linguistic rules.

Maintaining Full-Text Indexes

Indexes require updates when data changes.

Population modes include:

Full Population

Rebuilds the entire index.

Suitable for:

initial creation
major updates

Automatic Change Tracking

Automatically updates the index after data modifications.

Recommended for most OLTP workloads.

Manual Population

Administrators trigger updates manually.

Useful when:

large batch loads occur
maintenance windows exist

Performance Considerations

Full-text search is highly optimized but requires planning.

Consider:

index storage
population time
update frequency
large document sizes
language configuration
stoplists

For massive document repositories, automatic population should be monitored to avoid excessive resource usage.

When to Use Full-Text Search

Choose full-text search when users search by:

keywords
phrases
document titles
product names
legal terminology
technical documentation

Examples:

Knowledge bases
Product catalogs
Documentation portals
Legal document repositories
Medical reference systems

When NOT to Use Full-Text Search

Full-text search is not ideal when users expect semantic understanding.

Example:

User searches:

“recover my account”

Stored document:

“reset your password”

These phrases contain different words.

Full-text search may not match them effectively.

Semantic vector search would perform much better.

Full-Text Search vs LIKE

Feature	LIKE	Full-Text Search
Performance	Poor on large tables	Excellent
Uses indexes	Limited	Specialized full-text indexes
Phrase search	Limited	Yes
Word stemming	No	Yes
Stop words	No	Yes
Ranking	No	Yes
Prefix search	Limited	Yes
Language awareness	No	Yes

Full-Text Search vs Semantic Vector Search

Feature	Full-Text	Vector Search
Keyword matching	Excellent	Limited
Semantic understanding	No	Excellent
Embeddings required	No	Yes
Natural language	Limited	Excellent
Synonym understanding	Limited	Excellent
AI chatbot support	Moderate	Excellent
RAG support	Moderate	Excellent
Complexity	Low	Medium

Common DP-800 Scenarios

Scenario 1

A legal team searches contracts using exact legal terminology.

Best solution: Full-text search.

Scenario 2

A documentation portal searches millions of technical articles.

Best solution: Full-text search.

Scenario 3

An AI assistant answers questions using company documentation.

Best solution: Hybrid search (full-text + vector search).

Scenario 4

A recommendation engine finds similar documents.

Best solution: Vector search.

Best Practices

Use full-text indexes instead of LIKE for large text searches.
Configure the correct language for linguistic processing.
Enable automatic change tracking for frequently updated data.
Use stoplists to reduce index size and improve relevance.
Use CONTAINS() for precise searches and FREETEXT() for natural-language style queries.
Use CONTAINSTABLE() or FREETEXTTABLE() when relevance ranking is required.
Consider hybrid search when applications require both keyword precision and semantic understanding.
Monitor full-text index population and maintenance in production environments.

DP-800 Exam Tips

Know the differences between CONTAINS(), FREETEXT(), CONTAINSTABLE(), and FREETEXTTABLE().
Understand how full-text indexes differ from traditional SQL indexes.
Remember that full-text search is keyword-based, while vector search is meaning-based.
Understand the purpose of stoplists, word breakers, stemmers, and thesaurus files.
Expect scenario-based questions asking you to choose between LIKE queries, full-text search, vector search, and hybrid search based on application requirements.
Know when full-text search is sufficient and when semantic search or hybrid search provides a better user experience.

Practice Exam Questions

Question 1

A company stores millions of technical articles in an Azure SQL Database. Users frequently search for exact product names and technical terms. Developers currently use the following query:

			
SELECT *
FROM Articles
WHERE Content LIKE '%Azure SQL%'

The search is becoming increasingly slow as the table grows.

Which feature should you recommend?

A. Full-text search
B. Columnstore indexes
C. Semantic vector search
D. Table partitioning

Correct Answer: A

Explanation

Full-text search is specifically designed for efficient searching of large text columns. It creates specialized indexes that support keyword searches, phrase matching, ranking, and linguistic analysis. While table partitioning and columnstore indexes improve other workloads, they do not replace full-text search functionality.

Question 2

Which SQL Server function searches for exact words, phrases, Boolean expressions, and prefix terms?

A. FREETEXT()
B. CONTAINS()
C. PATINDEX()
D. CHARINDEX()

Correct Answer: B

Explanation

CONTAINS() supports advanced search expressions including:

Exact words
Exact phrases
Boolean operators (AND, OR, AND NOT)
Prefix searches
Proximity searches

FREETEXT() is intended for natural-language searching rather than precise keyword expressions.

Question 3

A developer wants search results to include different grammatical forms of the word run, such as:

running
runs
ran

Which SQL Server component provides this capability?

A. Stoplists

B. Full-text catalogs

C. Stemmers

D. Clustered indexes

Correct Answer: C

Explanation

Stemmers recognize different inflectional forms of words based on language-specific rules. This allows a search for “run” to also return documents containing “running,” “runs,” or “ran.”

Question 4

Which statement best describes a full-text catalog?

A. It stores database backups.

B. It replaces clustered indexes.

C. It is a logical container that organizes one or more full-text indexes.

D. It stores vector embeddings.

Correct Answer: C

Explanation

A full-text catalog is a logical container for full-text indexes. While SQL Server automatically manages catalogs in newer versions, understanding their role remains important for administration and exam scenarios.

Question 5

Which function is most appropriate when users enter natural-language search phrases rather than precise keywords?

A. CONTAINS()

B. LIKE

C. FREETEXT()

D. PATINDEX()

Correct Answer: C

Explanation

FREETEXT() performs natural-language searches by considering linguistic analysis, stemming, and synonyms. It is designed for less structured search input compared to CONTAINS().

Question 6

Which full-text search feature helps reduce index size by excluding commonly occurring words such as the, is, and and?

A. Word breakers

B. Stoplists

C. Stemmers

D. Ranking tables

Correct Answer: B

Explanation

Stoplists contain common words, known as stop words, that are ignored during indexing and searching. This improves both index efficiency and search relevance.

Question 7

Your application must display search results ordered from the most relevant document to the least relevant.

Which functions are specifically designed for this purpose?

A. CONTAINS() and FREETEXT()

B. LIKE and PATINDEX()

C. CONTAINSTABLE() and FREETEXTTABLE()

D. CHARINDEX() and STRING_SPLIT()

Correct Answer: C

Explanation

CONTAINSTABLE() and FREETEXTTABLE() return a RANK value that indicates the relevance of each result, allowing applications to sort documents by search quality.

Question 8

Which scenario is the best use case for traditional full-text search?

A. Finding semantically similar customer support tickets

B. Building a Retrieval-Augmented Generation (RAG) chatbot

C. Recommending similar research papers based on meaning

D. Searching legal documents using exact legal terminology

Correct Answer: D

Explanation

Full-text search excels when users search using precise words and phrases, making it well suited for legal, compliance, technical documentation, and product catalog scenarios. Semantic vector search is generally preferred for AI assistants and recommendation systems.

Question 9

Which component is responsible for separating text into searchable words based on language-specific rules?

A. Word breakers

B. Stoplists

C. Embedding models

D. Full-text catalogs

Correct Answer: A

Explanation

Word breakers tokenize text into individual searchable terms according to the linguistic rules of the configured language. Proper tokenization is essential for accurate indexing and querying.

Question 10

A company is building an AI-powered knowledge assistant. Users expect searches such as:

“recover my account”

to return documents titled:

“reset your password”

Which recommendation is most appropriate?

A. Continue using LIKE queries

B. Use only full-text search

C. Replace all searches with clustered indexes

D. Combine full-text search with semantic vector search using hybrid search

Correct Answer: D

Explanation

Full-text search primarily matches keywords and phrases, while semantic vector search retrieves documents based on meaning. Hybrid search combines both approaches, producing more accurate results for AI-powered applications such as RAG systems and enterprise knowledge assistants.

DP-800 Exam Tips

Use full-text search when exact keywords, phrases, and language-aware matching are required.
Understand the differences between CONTAINS(), FREETEXT(), CONTAINSTABLE(), and FREETEXTTABLE().
Remember that word breakers tokenize text, stemmers recognize grammatical variations, and stoplists remove common words to improve search efficiency.
Use ranking functions when applications need to order search results by relevance.
Recognize that LIKE queries are not appropriate for large-scale enterprise text search.
Know that full-text search is keyword-based, while vector search is meaning-based; hybrid search combines the strengths of both and is often the preferred approach for AI-enabled search solutions.