IncludeHelp_logo
Skip Navigation Links

Home » Trending Technologies MCQs

Vector Databases MCQs (Multiple-Choice Questions)

Practice Vector Databases MCQs to test your knowledge of vector embeddings, similarity search, indexing, approximate nearest neighbor search, metadata filtering, and vector database architectures.

Vector Databases MCQs

These Vector Databases multiple-choice questions cover fundamental and advanced concepts used in AI search, semantic retrieval, recommendation systems, and Retrieval-Augmented Generation (RAG).

List of Vector Databases MCQs

Below is the list of Vector Databases MCQs with answers and explanations.

1. What is the primary purpose of a vector database?

  1. To store only relational tables
  2. To store and search vector embeddings efficiently
  3. To compile machine learning models
  4. To manage operating system processes

Answer: B) To store and search vector embeddings efficiently

Explanation:

A vector database is designed to store vector representations of data and perform similarity-based searches over those vectors.

2. What is a vector embedding?

  1. A database password
  2. A numerical representation of data in a vector space
  3. A database transaction
  4. A compressed SQL query

Answer: B) A numerical representation of data in a vector space

Explanation:

An embedding represents information such as text, images, or audio as a numerical vector whose dimensions encode learned semantic or feature information.

3. Which operation is central to vector database retrieval?

  1. Similarity search
  2. Source-code compilation
  3. File compression
  4. Memory allocation

Answer: A) Similarity search

Explanation:

Vector databases retrieve vectors that are closest or most similar to a query vector according to a selected distance or similarity metric.

4. What does k-nearest neighbors (kNN) search attempt to find?

  1. The k largest database tables
  2. The k vectors most similar to a query vector
  3. The k newest records
  4. The k longest documents

Answer: B) The k vectors most similar to a query vector

Explanation:

kNN search identifies the k nearest vectors to a query according to a chosen distance or similarity metric.

5. What does ANN stand for in vector search?

  1. Advanced Neural Network
  2. Approximate Nearest Neighbor
  3. Automatic Numeric Normalization
  4. Asynchronous Network Node

Answer: B) Approximate Nearest Neighbor

Explanation:

Approximate Nearest Neighbor search uses indexing techniques to find highly similar vectors without necessarily comparing the query against every stored vector.

6. Why is ANN search commonly used instead of exhaustive kNN search for large datasets?

  1. ANN always returns exact results
  2. ANN can reduce search time by searching a smaller candidate space
  3. ANN eliminates embeddings
  4. ANN stores only text

Answer: B) ANN can reduce search time by searching a smaller candidate space

Explanation:

Exhaustive search can become expensive as the number of vectors grows. ANN indexes reduce the amount of work required to identify likely nearest neighbors.

7. Which distance metric measures straight-line distance between vectors?

  1. Euclidean distance
  2. Cosine similarity
  3. Jaccard coefficient
  4. Hamming similarity

Answer: A) Euclidean distance

Explanation:

Euclidean distance, also called L2 distance, measures the geometric straight-line distance between points in vector space.

8. What does cosine similarity primarily compare?

  1. The file sizes of two documents
  2. The angle between two vectors
  3. The database storage capacity
  4. The number of vector dimensions only

Answer: B) The angle between two vectors

Explanation:

Cosine similarity measures the cosine of the angle between two vectors, making it useful for comparing their directional similarity.

9. What is the main characteristic of dot-product similarity?

  1. It compares only strings
  2. It calculates the inner product between vectors
  3. It requires vectors to contain integers
  4. It measures database latency

Answer: B) It calculates the inner product between vectors

Explanation:

Dot-product or inner-product similarity calculates the sum of pairwise products of vector components.

10. What is an index in a vector database?

  1. A structure designed to accelerate vector search
  2. A backup copy of the database
  3. A user authentication file
  4. A text document containing SQL commands

Answer: A) A structure designed to accelerate vector search

Explanation:

Vector indexes organize embeddings so that similarity searches can be performed more efficiently than scanning every stored vector.

11. Which indexing algorithm uses a graph structure for approximate nearest neighbor search?

  1. HNSW
  2. B-tree
  3. Hash join
  4. Merge sort

Answer: A) HNSW

Explanation:

HNSW, or Hierarchical Navigable Small World, uses a graph-based structure to support approximate nearest neighbor search.

12. What does HNSW stand for?

  1. Hierarchical Navigable Small World
  2. High Network Search Workflow
  3. Hierarchical Numeric Storage Wrapper
  4. Hybrid Nearest Search Window

Answer: A) Hierarchical Navigable Small World

Explanation:

HNSW is a graph-based approximate nearest neighbor indexing method commonly used for high-dimensional vector search.

13. In an HNSW index, what does the parameter commonly called M control?

  1. The number of database users
  2. The graph connectivity or number of links per node
  3. The vector dimension
  4. The number of databases

Answer: B) The graph connectivity or number of links per node

Explanation:

In HNSW implementations, M controls the graph connectivity by influencing how many neighboring links nodes maintain.

14. What does an HNSW search parameter such as efSearch generally influence?

  1. The number of database tables
  2. The size of the candidate search list
  3. The embedding dimension
  4. The metadata schema

Answer: B) The size of the candidate search list

Explanation:

Increasing the HNSW search candidate list can improve search quality but may require additional search computation.

15. What is IVF in vector indexing?

  1. Inverted File indexing
  2. Internal Vector Format
  3. Indexed Variable Function
  4. Integrated Vector Framework

Answer: A) Inverted File indexing

Explanation:

IVF, or Inverted File indexing, partitions vectors into groups or lists and searches selected lists rather than scanning the entire vector collection.

16. What does nprobe commonly control in an IVF-based search?

  1. The number of inverted lists searched
  2. The number of vector dimensions
  3. The number of metadata fields
  4. The number of database connections

Answer: A) The number of inverted lists searched

Explanation:

In IVF search, nprobe determines how many inverted lists are examined for candidate vectors.

17. What is the primary trade-off when increasing the number of IVF lists searched?

  1. Lower search cost and lower recall
  2. Higher search work and potentially higher recall
  3. Smaller vectors and larger embeddings
  4. Lower storage and zero computation

Answer: B) Higher search work and potentially higher recall

Explanation:

Searching more IVF lists can increase the chance of finding relevant neighbors, but it also increases the amount of search computation.

18. What is vector quantization commonly used for?

  1. Reducing memory usage or search cost through compressed representations
  2. Creating relational database schemas
  3. Generating HTML pages
  4. Encrypting database passwords

Answer: A) Reducing memory usage or search cost through compressed representations

Explanation:

Quantization can represent vectors more compactly, reducing memory requirements and potentially improving search performance at some cost to precision.

19. What does Product Quantization (PQ) do?

  1. Compresses vectors by representing subspaces with codebooks
  2. Converts vectors directly into SQL tables
  3. Removes all metadata
  4. Changes text into XML

Answer: A) Compresses vectors by representing subspaces with codebooks

Explanation:

Product Quantization divides vectors into subspaces and represents their components using learned codebooks, enabling compact vector representations.

20. What is a brute-force vector search?

  1. Search using every stored vector as a candidate
  2. Search using only metadata
  3. Search without a query
  4. Search using SQL joins only

Answer: A) Search using every stored vector as a candidate

Explanation:

Brute-force search compares the query vector with all candidate vectors. It can provide exact nearest-neighbor results but becomes expensive for very large collections.

21. What does recall measure in approximate vector search?

  1. The proportion of relevant true neighbors that were retrieved
  2. The number of database users
  3. The storage capacity of an index
  4. The vector dimension

Answer: A) The proportion of relevant true neighbors that were retrieved

Explanation:

Recall evaluates how many of the relevant or true nearest neighbors are successfully returned by a search system.

22. What does search latency represent in a vector database?

  1. The time required to process a search request
  2. The number of vectors stored
  3. The number of dimensions in a vector
  4. The number of metadata fields

Answer: A) The time required to process a search request

Explanation:

Search latency measures how long a vector search request takes from request processing to result delivery.

23. Why can increasing ANN search parameters improve recall?

  1. It can cause the search to examine more candidate vectors
  2. It removes the query vector
  3. It reduces vector dimensions automatically
  4. It disables indexing

Answer: A) It can cause the search to examine more candidate vectors

Explanation:

Many ANN algorithms expose search parameters that control the amount of candidate exploration. More exploration can improve the probability of finding true nearest neighbors.

24. What is metadata associated with a vector?

  1. Additional information describing the vector or source object
  2. The vector's mathematical distance only
  3. A database engine executable
  4. An index algorithm

Answer: A) Additional information describing the vector or source object

Explanation:

Metadata may contain information such as document ID, category, author, timestamp, language, or access permissions.

25. What is metadata filtering in vector search?

  1. Restricting vector search according to metadata conditions
  2. Changing vector dimensions
  3. Deleting all embeddings
  4. Changing the embedding model

Answer: A) Restricting vector search according to metadata conditions

Explanation:

Metadata filtering allows a vector search to consider only records satisfying specified conditions, such as category, tenant, date, or access level.

26. Which query is an example of metadata filtering?

  1. Find vectors similar to a query among documents where language = "English"
  2. Find every vector without calculating similarity
  3. Change all vectors to integers
  4. Delete the vector index

Answer: A) Find vectors similar to a query among documents where language = "English"

Explanation:

The condition on language restricts the candidate records before or during vector retrieval.

27. What is hybrid search?

  1. Combining vector-based retrieval with another retrieval method such as keyword search
  2. Using two database passwords
  3. Storing vectors in two files
  4. Converting vectors into relational tables

Answer: A) Combining vector-based retrieval with another retrieval method such as keyword search

Explanation:

Hybrid search can combine semantic vector retrieval with lexical or keyword-based retrieval to improve coverage and relevance.

28. Why can keyword search complement vector search?

  1. Exact terms, identifiers, or product codes may be important
  2. Keyword search always understands semantics better
  3. It eliminates the need for embeddings
  4. It automatically increases vector dimensions

Answer: A) Exact terms, identifiers, or product codes may be important

Explanation:

Semantic search is useful for conceptual similarity, while lexical search can be especially useful for exact names, codes, identifiers, and terminology.

29. What is reranking in a vector retrieval pipeline?

  1. Reordering retrieved candidates using a more detailed relevance model
  2. Deleting low-dimensional vectors
  3. Changing the database schema
  4. Creating a new embedding for every database field

Answer: A) Reordering retrieved candidates using a more detailed relevance model

Explanation:

Reranking is often performed after initial retrieval to produce a more accurate ordering of a relatively small candidate set.

30. What does top-k retrieval mean?

  1. Returning the k most relevant search results
  2. Returning exactly k database tables
  3. Returning only k-dimensional vectors
  4. Returning k metadata fields from every document

Answer: A) Returning the k most relevant search results

Explanation:

Top-k retrieval asks the vector search system to return the highest-ranked k candidates according to the selected similarity or distance metric.

31. What must generally be true for a query vector and stored vectors to be directly compared?

  1. They must be compatible in dimensionality and representation
  2. They must have different dimensions
  3. They must contain only strings
  4. They must be stored in separate databases

Answer: A) They must be compatible in dimensionality and representation

Explanation:

A similarity calculation normally requires the query and indexed vectors to use compatible dimensions and an appropriate embedding space.

32. Why is the embedding model important in a vector database system?

  1. It determines how source data is represented in vector space
  2. It controls database usernames
  3. It creates SQL indexes automatically
  4. It determines network bandwidth

Answer: A) It determines how source data is represented in vector space

Explanation:

The embedding model transforms source information into vectors. Its representation directly affects semantic similarity and retrieval quality.

33. What can happen if documents are embedded with one embedding model and queries with an incompatible model?

  1. Similarity results may become unreliable or invalid
  2. The database automatically fixes all vectors
  3. Metadata is automatically deleted
  4. The vectors become SQL records

Answer: A) Similarity results may become unreliable or invalid

Explanation:

Query and document embeddings generally need to belong to a compatible vector space for meaningful similarity calculations.

34. What is vector dimensionality?

  1. The number of numerical components in a vector
  2. The number of database tables
  3. The number of metadata records
  4. The number of search queries

Answer: A) The number of numerical components in a vector

Explanation:

A vector's dimensionality is the number of scalar values it contains. For example, a 768-dimensional embedding contains 768 numerical components.

35. What is a collection commonly used to represent in a vector database?

  1. A logical group of vector records
  2. A CPU instruction set
  3. A programming-language compiler
  4. A network cable

Answer: A) A logical group of vector records

Explanation:

Vector databases commonly organize related vectors and their metadata into logical collections, indexes, namespaces, or equivalent structures.

36. Which data type is commonly stored alongside an embedding to identify its source?

  1. Document or record ID
  2. CPU register
  3. Compiler flag
  4. Operating system kernel

Answer: A) Document or record ID

Explanation:

An identifier allows the application to associate a retrieved vector with its original document, image, product, or other source object.

37. What is a namespace useful for in a vector retrieval system?

  1. Logically separating groups of vectors or tenants
  2. Increasing vector dimensionality
  3. Changing cosine similarity into SQL
  4. Training an embedding model

Answer: A) Logically separating groups of vectors or tenants

Explanation:

Namespaces or equivalent logical partitions can isolate datasets, applications, environments, or tenants within a vector system.

38. What is a major benefit of storing metadata with embeddings?

  1. Retrieved vectors can be associated with useful source information and filtered
  2. It eliminates the need for an embedding model
  3. It automatically doubles vector accuracy
  4. It prevents all duplicate data

Answer: A) Retrieved vectors can be associated with useful source information and filtered

Explanation:

Metadata provides context for retrieved vectors and can be used to restrict searches to relevant subsets.

39. In a RAG system, what is the role of a vector database?

  1. Retrieve relevant embedded content for a user query
  2. Generate the final language model response by itself
  3. Train the operating system
  4. Replace the language model entirely

Answer: A) Retrieve relevant embedded content for a user query

Explanation:

In RAG, documents are represented as embeddings and retrieved based on similarity to the query. The retrieved context can then be supplied to a language model.

40. What is the usual sequence for semantic vector retrieval?

  1. Embed query → search vectors → retrieve matching records
  2. Search SQL tables → train embedding model → delete vectors
  3. Generate HTML → compile vectors → search metadata
  4. Delete index → embed database → create query

Answer: A) Embed query → search vectors → retrieve matching records

Explanation:

The user query is converted into a vector using a compatible embedding model, searched against stored vectors, and the most relevant records are returned.

41. What is data ingestion in a vector database pipeline?

  1. The process of preparing and inserting source data and embeddings
  2. The process of rendering database results as HTML
  3. The process of deleting an index
  4. The process of shutting down a database

Answer: A) The process of preparing and inserting source data and embeddings

Explanation:

Ingestion commonly includes loading source data, chunking or preprocessing it, generating embeddings, attaching metadata, and storing the resulting records.

42. Why is chunking important when storing documents for semantic retrieval?

  1. It divides large documents into searchable units
  2. It converts vectors into integers
  3. It removes all metadata
  4. It prevents similarity calculations

Answer: A) It divides large documents into searchable units

Explanation:

Chunking allows retrieval systems to return specific portions of documents rather than treating an entire large document as one retrieval unit.

43. What is vector normalization commonly used for?

  1. Scaling vectors to a standardized magnitude
  2. Changing text into HTML
  3. Adding metadata fields
  4. Creating database users

Answer: A) Scaling vectors to a standardized magnitude

Explanation:

Normalization can scale vectors, often to unit length. With normalized vectors, dot product can correspond directly to cosine similarity.

44. Which statement about cosine similarity is correct?

  1. It is affected only by vector magnitude
  2. It focuses on the directional relationship between vectors
  3. It requires both vectors to contain text
  4. It counts database rows

Answer: B) It focuses on the directional relationship between vectors

Explanation:

Cosine similarity compares vector direction rather than simply their magnitude, which is useful for semantic similarity tasks.

45. Which situation is most suitable for metadata filtering combined with vector search?

  1. Find similar support articles only from the year 2026
  2. Find every record regardless of its category
  3. Delete all vector indexes
  4. Change the embedding dimension

Answer: A) Find similar support articles only from the year 2026

Explanation:

The semantic query identifies similar articles while the metadata condition restricts the search to records from the required year.

46. Which approach can improve retrieval quality when both semantic meaning and exact terminology matter?

  1. Hybrid vector and keyword retrieval
  2. Removing all metadata
  3. Using only random vectors
  4. Disabling indexing

Answer: A) Hybrid vector and keyword retrieval

Explanation:

Combining semantic retrieval with lexical matching can capture both conceptual similarity and exact terms.

47. A system contains 100 million embeddings. Which approach is generally more suitable for scalable retrieval than comparing every vector for every query?

  1. An ANN index
  2. A plain text editor
  3. A CSV file without indexing
  4. A random scan of the filesystem

Answer: A) An ANN index

Explanation:

At large scale, ANN indexes can reduce the number of vectors examined during search and provide a practical latency-performance trade-off.

48. A search system returns relevant documents but misses some of the true nearest neighbors. Which metric should be investigated first to measure retrieval coverage?

  1. Recall
  2. File size
  3. CPU clock speed
  4. Database name length

Answer: A) Recall

Explanation:

Recall measures how many relevant true neighbors are retrieved. A low recall value can indicate insufficient candidate exploration or unsuitable index configuration.

49. A RAG application retrieves many semantically similar documents but frequently misses documents containing an exact product code such as AX-2047. Which retrieval strategy can help address this issue?

  1. Combine vector retrieval with keyword or lexical search
  2. Remove the product code from the documents
  3. Reduce all vectors to one dimension
  4. Disable metadata and indexing

Answer: A) Combine vector retrieval with keyword or lexical search

Explanation:

Vector similarity may capture semantic relationships but can miss exact identifiers. Hybrid retrieval can combine semantic matching with exact-term matching.

50. An enterprise RAG system stores document embeddings with tenant ID, department, document type, and access level. A user asks a semantic question. What retrieval design is most appropriate for enforcing tenant isolation while finding relevant documents?

  1. Perform vector search without any metadata conditions
  2. Apply appropriate access and tenant metadata filters together with vector similarity search
  3. Search every tenant and remove unauthorized documents after generation
  4. Store all tenants in one unrestricted result set

Answer: B) Apply appropriate access and tenant metadata filters together with vector similarity search

Explanation:

Access-control metadata should constrain the retrieval scope so that the similarity search operates only over records the user is authorized to access. This is particularly important in multi-tenant RAG systems.

Comments and Discussions!

Load comments ↻



Top MCQs
Top Programs/Examples
Top Tutorials
About
Student's Section
Subscribe

Copyright © 2026 www.includehelp.com. All rights reserved.