Top 100+ Data Modelling Interview Questions for Data Professionals

Getting ready for a data modeling job interview can seem daunting, especially when facing a wide range of questions. Whether you’re aspiring to become a data analyst, data scientist, or data engineer, being prepared is key.

That’s where we come in. We’ve compiled an extensive list of over 100+ data modeling interview questions and answers to help you get ready. This guide is your go-to resource, packed with all the information you need to ace your interview and secure your dream job in data management.

By studying these questions, you’ll develop the knowledge and confidence needed to excel in your interview. Whether you’re covering fundamental aspects of data modeling or exploring more advanced topics, this thorough guide will support you every step of the way. With this preparation, you’ll be well-prepared to impress your interviewers and secure a desired position in data management.

Introduction to Data Modeling
Data Modelling Interview Questions and Answers
FAQs on Data Modeling Interview Questions
Conclusion

Introduction to Data Modeling and its Importance

Data modeling plays a crucial role in data management, focusing on designing and organizing data structures to meet business needs. As organizations increasingly depend on data-driven decision-making, the need for proficient data analysts, scientists, and engineers skilled in data modeling continues to grow. Here, we explore over 100 top data modeling interview questions and answers to help you prepare for your next interview.

Data Modeling Interview Questions and Answers

This guide offers a comprehensive collection of data modeling interview questions, addressing various skill levels within the data analysis field. It includes questions for beginners, intermediate and advanced practitioners, experienced professionals, and scenario-based questions, as well as questions specifically designed for business analysts and data scientists.

Beginners Data Modeling Interview Questions

Q1) Explain the three different types of data models.

Conceptual data model:

The conceptual data model outlines key entities and relationships but lacks specific attribute details. It serves as a blueprint during initial planning, allowing data modelers to construct it and submit it for evaluation by functional teams. Conceptual data modeling involves creating these models.

Physical data model:

The physical data model incorporates all necessary elements for database implementation, including tables, columns, relationship constraints, and database attributes. Parameters like database performance, indexing strategies, and physical storage are crucial in this model. A table, comprising rows and columns linked to other tables through relationships, forms the core component. The process of creating physical data models is known as physical data modeling.

Logical data model:

A logical data model articulates an organization’s business requirements either fully or partially. It translates a conceptual data model into actionable components, extending its scope. Logical data models encompass entities, attributes, super types, subtypes, primary keys, alternate keys, inversion key entries, rules, relationships, and definitions. This process is termed logical data modeling.

Q2) Differentiate between the logical data model and the physical data model.

Logical Data Model	Physical Data Model
Entity	Table
Attribute	Column
Primary Key	Primary Key Constraint
Alternate Key	Unique Constraint
Inversion Key Entry	Non-Unique Indexes
Rule	Check Constraint, Default Value
Relationship	Foreign Key
Definition	Comment

Intermediate Data Modeling Interview Questions

Q3) Explain the ER (entity-relationship) diagram or ERD with an example.

An ER diagram provides a visual representation of entities and their relationships. In a data model, entities (or tables) are depicted as square or rectangular boxes containing attributes, connected by lines indicating relationships.

Q4) Explain the concepts of subtypes and supertypes.

Entities can undergo division into sub-entities based on distinctive attributes. These sub-entities, identified as subtype entities, possess distinct properties unique to them.

Certain attributes are exclusive to individual entities and are exclusively present in higher or parent-level entities. Hence, they are termed as supertype entities.

Advanced Interview Questions on Data Modeling

Q5) Differentiate between OLAP and OLTP databases.

SQL database workloads can be categorized into two types:

Online Transactional Processing (OLTP): These involve simple queries with high concurrency and low latency, typically involving the reading or modification of a few records simultaneously while maintaining data integrity, such as in bank account transactions.
Online Analytical Processing (OLAP): These workloads, akin to periodic reporting, often consist of complex queries (including aggregations and joins) that require scanning a large number of records.

Aspect	OLTP Databases	OLAP Databases
Normalization in Tables	Enabled	Not Enabled
Database Management Approach	Traditional	Data Warehouse
Response Time	Milliseconds	Seconds to Minutes
Transaction Type	Quick Online Transactions	Large Data Volumes
System Type	Real-Time Transactional	Business Metrics Analysis

Q6) What do you mean by the CAP Theorem? How does it work?

The CAP theorem states that no distributed system can simultaneously provide Consistency (C), Availability (A), and Partition Tolerance (P). In other words, a distributed system cannot guarantee more than two of these assurances at any given time.

Consistency: Ensures that data remains consistent after an operation. For example, after updating the database, all queries should return the same result.
Availability: Ensures that the database remains accessible and active without any downtime.
Partition Tolerance: Ensures that the system remains functional even if communication between servers is inconsistent.

Data Modeling Interview Questions for Experienced

Q7) Differentiate between primary and foreign keys.

Primary Key	Foreign Key
A primary key, represented by a clustered index, arranges the physical order of data within the database table based on the sequence of the clustered index.	A foreign key on its own does not create an index, whether clustered or non-clustered. However, an index can be manually created for the foreign key.
The primary key uniquely identifies each record within the database.	A foreign key is a column in a table that references the primary key of another table.
Primary key fields cannot contain null values.	Foreign key fields can include null values.
Each table can have only one primary key.	A table can contain multiple foreign keys.

Q8) How many types of normalization exist? Mention the rules for the second normal form and third normal form.

Normalization encompasses various levels, including

- First normal form
- Second normal form
- Third normal form
- Boyce Codd’s fourth normal form
- Fifth normal form

- Second Normal Form (2NF) Rules:
  - It must already be in the first normal form.
  - Non-prime attributes should not be functionally dependent on any subset of the table relation’s candidate keys.

- Third Normal Form (3NF) Rules:
  - It must already be in the second normal form.
  - There should be no transitive functional dependencies present.

Data Modeling Interview Questions for Business Analysts

Q9) What do you mean by a network model?

The network model, similar to the hierarchical model, allows for multiple relationships to link records, signifying the presence of more than one relationship. It facilitates the creation of parent-child relationships, where each record can belong to several sets, enabling the establishment of complex table relationships.

Q10) Mention some of the fundamental data models.

Fully-Attributed (FA) Model: This model, adhering to the third normal form, furnishes all the data necessary for a specific implementation approach.
Transformation Model (TM): This model delineates the transformation of a relational data model into a structure suitable for the utilized DBMS. In many cases, the TM may not conform to the third normal form. The structures are optimized based on the capabilities of the DBMS, data levels, and projected data access patterns.
DBMS Model: The DBMS Model encompasses the database design for the system. It can be at the project or area level for the complete integrated system.

Scenario-based Data Modeling Interview Questions

Q11)What is the primary focus of CleanAlp, owned by Lisa?

Lisa owns CleanAlp, a cleaning service catering primarily to property management businesses. These businesses oversee residential complexes, which are CleanAlp’s main clients. Clients book various services for each property, often signing multiple contracts with CleanAlp. These contracts may include temporary additional services or multiple contracts for the same property, such as after termination and reassignment. Each service has a defined period in the contract (weekly, fortnightly, or monthly), and there’s a catalog of cleaning services offered by CleanAlp. Despite a large workforce, each cleaning service is handled by a single employee, with the possibility of personnel changes during the contract period. Customers receive monthly invoices detailing the agreed-upon amounts. An ER diagram is needed to represent this scenario.

Q12) How do you model a set of entities in a NoSQL database using an optimal technique?

Start by establishing a 3NF model, where the “Order” entity utilizes the “order id” as its primary key, and the “Detail” entity incorporates foreign keys.

Once denormalized, the 3NF model transforms into the structure depicted above.

Subsequently, you can convert the denormalized model into a NoSQL entity model featuring embedded parent-child relationships.

{
"order_id": "12345",
"order_date": "1/21/2019",
"customer_id": "123456",
"order_details": [
{
"item_id": "2345",
"qty": 2,
"unit_price": 157.75,
"sales_price": 315.5
},
{
"item_id": "2110",
"qty": 1,
"unit_price": 75.25,
"sales_price": 75.25
},
{
"item_id": "1760",
"qty": 3,
"unit_price": 55,
"sales_price": 165
}
]
}

Data Modeling Interview Questions Amazon

Q13) What is Amazon’s RDBMS service?

Amazon’s Relational Database Service (RDS) is a web service designed for the setup, management, and scaling of a relational database in the cloud. It supports standard relational database engines, empowering users to control, extend, and access them on demand. RDS streamlines management operations, allowing users to prioritize their applications over database maintenance.

Q14) What is Amazon Aurora?

Amazon Aurora is a high-availability, automated failover relational database engine supporting MySQL and PostgreSQL. It combines features of both MySQL and PostgreSQL, serving as a multi-threaded, multiprocessor database engine that prioritizes performance, availability, and operational efficiency.

Unlike traditional MySQL and PostgreSQL engines, Amazon Aurora operates without a Write-Ahead Log but features a robust crash recovery mechanism. Additionally, Amazon offers data duplication between Amazon Aurora and MySQL or PostgreSQL databases, facilitating seamless integration between these platforms.

Facebook Data Modeling Interview Questions

Q15) What significance does the third normal form have?

The third normal form (3NF) is attained when a relation eliminates transitive dependencies for non-prime attributes, distinct from the second normal form. By achieving the third normal form, data duplication and anomalies are primarily eradicated.

Q16) Define Recursive Relationships.

Recursive relationships emerge when an entity establishes a relationship with itself. These intricate relationships necessitate sophisticated data conversion techniques for schema development. Consider a scenario where a doctor is enlisted as a care provider in a medical database. If the doctor becomes ill, they must seek treatment from another doctor as a patient, resulting in recursive relationships. Addressing this involves adding a foreign key linking to the health center number in each patient’s record. In such entity relations, careful management ensures the recursion is managed effectively.

Accenture Data Modeling Interview Questions

Q17) What types of techniques are available for visual data modeling?

Visual data modeling employs two primary categories of techniques:

Entity-Relationship (ER) Model: This method aids in constructing traditional databases, emphasizing data normalization to reduce redundancy. It offers a high-level view of data within a database structure.
Unified Modeling Language (UML): Widely utilized in software engineering, UML serves as a versatile language for creating, visualizing, and modeling databases. Among its various diagrams, the Class Diagram stands out, sharing similarities with ER diagrams.

Q18) What exactly is an artificial (derived) primary key? When should you use it?

An artificial primary key is intentionally created, unlike a natural key that already exists in the database. Using names as primary keys violates the stability rule. While a social security number could serve as a suitable option, foreign employees may not possess one. Hence, a derived primary key becomes preferable over a natural one.

SAP Hana Data Modeling Interview Questions

Q19) Are aggregates or indexes necessary for SAP HANA?

In SAP HANA, there’s no requirement for distinct main indexes since each column functions as an index. However, for OLTP applications like the Business Suite, secondary indexes with multiple columns are both feasible and utilized. Additionally, to optimize multi-column joins, HANA automatically generates helper indexes.

Q20) What license keys exist in the HANA system?

Temporary License key: Upon setting up the HANA database, temporary license keys are automatically installed. These keys remain valid for 90 days post-installation, necessitating the acquisition of permanent license keys from the SAP Market Place before expiration.
Permanent License Key: Unlike temporary keys, permanent license keys retain validity until their specified expiration date. These keys define the memory allocation for the intended HANA installation.

Salesforce Data Modeling Interview Questions

Q21) Define the term Data Skew.

Data skew emerges when managing extensive datasets, typically exceeding 10,000 records within a large business environment. This issue, termed “ownership data skew” by experts, occurs when a single individual possesses a substantial number of records. Consequently, users encountering data skew may experience performance setbacks, particularly during update operations. This situation arises when the bulk of records about a specific object are under the ownership of a solitary user or members of a singular role.

Q22) What happens to the detail record after deleting the master record? What happens to the child record when the parent record is removed?

In a master-detail relationship, deleting the master record triggers the immediate deletion of associated detail records (Cascade delete).
Conversely, in a Lookup relationship, the removal of the parent record does not affect the existence of the child record.

Oracle Data Modeling Interview Questions and Answers

Q23) What is the relation between a database, a tablespace, and a data file?

In an Oracle database, a tablespace serves as a logical storage unit. Each tablespace encompasses one or more files referred to as data files. These data files house the entirety of the database’s data, constituting the physical structure that interacts with the operating system running the Oracle software.

Q24) What exactly does the ANALYZE command do?

The ANALYZE command facilitates modifications to an index, table, or cluster within a database. By employing this command, one can identify chained and migrated rows within a table or cluster, thereby validating the object’s structure. Additionally, the ANALYZE command aids in gathering statistics about the object’s usage, which are subsequently incorporated into the data dictionary.

SQL Data Modeling Interview Questions

Q25) Name some popular DBMS software.

Here are a few widely-used DBMS software:

- MySQL
- PostgreSQL
- Dbase
- Microsoft SQL Server
- FoxPro
- Microsoft Access
- Oracle
- SQLite
- IBM DB2

Q26) Define an Apex transaction.

An Apex transaction encompasses a series of actions executed together, incorporating DML activities responsible for querying records. If any error occurs while saving records during the DML processes within a transaction, the entire transaction is either committed as successful or rolled back.

Snowflake Data Modeling Interview Questions

Q27) How does one gain access to the Snowflake Cloud data warehouse?

Here are the available approaches to access Snowflake’s data warehouse:

- ODBC Drivers: A connection driver tailored for Snowflake.
- JDBC Drivers: Facilitates communication between a Java application and a database.
- Python Libraries: Enables the development of Python applications interfacing with Snowflake and executing common operations.
- Web User Interface: Supports a wide range of activities akin to SQL and the command line.
- SnowSQL Command-line Client: A command-line interface, powered by Python, enabling connection to Snowflake across Windows, Linux, and macOS platforms.

Q28) Briefly explain snowflake clustering.

Clustering in Snowflake involves partitioning data, and assigning unique cluster keys to each table. These cluster keys represent subsets of a table’s columns, facilitating the grouping of data within the table. Re-clustering is the process of managing clustered data within a table.

Power BI Data Modeling Interview Questions

Q29) How do Power BI Desktop and Power Pivot for Excel handle data modeling differently?

Power Pivot for Excel supports only one import mode, computed column, and one-to-many relationship. Conversely, Power BI Desktop offers a broader range of features including support for security, computed tables, bidirectional cross-filtering connections, and various import options.

Q30) What is bi-directional cross-filtering?

Bi-directional cross-filtering allows data modelers to control the flow of filters in Power BI Desktop for data utilizing table relationships. With this feature, a second related table linked to the opposite side of a given table relationship receives the filter context. This method helps address the many-to-many problem without requiring intricate DAX calculations.

Redshift Data Modeling Interview Questions

Q31) What does Amazon Redshift’s AQUA stand for?

AQUA stands for Advanced Query Accelerator in Amazon Redshift. Utilizing a hardware-accelerated cache, AQUA enables Redshift to achieve speeds up to ten times faster than other enterprise cloud data warehouses. Unlike traditional architectures where all data processing occurs in computing clusters after being sent from centralized storage, AQUA optimizes performance by conducting a significant portion of data processing directly within the cutting-edge cache.

Q32) Define materialized views in Redshift.

A materialized view in Redshift is based on an SQL query involving one or more base tables, storing a precomputed result set. Similar to querying other tables and views within the database, you can execute SELECT queries against a materialized view.

Database Modeling Interview Questions

Q33) What exactly do you mean by Star schema?

A star schema comprises a central fact table linked to multiple connecting dimension tables. The primary key of the fact table serves as a foreign key for the dimension tables. This schema derives its name from the entity-relationship diagram, resembling a star with branches stemming from the central fact table.

Q34) Briefly explain the snowflake schema database design.

In a snowflake schema, dimension tables connect to the fact table through other dimension tables rather than directly. Snowflaking involves normalizing dimension tables in the star schema to form a snowflake-like structure, with the fact table at the core. Each level of dimension in the snowflake schema corresponds to a hierarchy level, and the tables adhere to the third normal form (3NF). Multiple dimension tables establish connections with other dimension tables via many-to-many or many-to-one relationships, all linked to the central fact table. This design optimizes query performance, requiring minimal disk space and facilitating the joining of smaller lookup tables.

Data Vault Modeling Interview Questions

Q35) What is Data Vault Modeling?

Data Vault Modeling combines elements of dimensional modeling and the third normal form to create a logical enterprise data warehouse. This approach is adaptable, extensible, flexible, and reliable, catering to the organization’s specific requirements. Data Vault is suitable for handling big, structured, and unstructured datasets, employing a bottom-up, incremental, and modular methodology.

Q36) How many types of tables exist in data vault modeling?

Data Vault Modeling encompasses three primary table categories:

Hubs: Contain a distinct set of business keys representing business objects.
Links: Capture unique relationships and transactions, serving as the fundamental components of business processes.
Satellites: Store descriptive information associated with hubs and links.

Data Warehouse Modeling Interview Questions

Q37) Define Virtual Data Warehouses.

Virtual data warehouses offer a consolidated view of the final data without including historical data. They are essentially logical data models accompanied by metadata.

Q38) What is real-time data warehousing?

Real-time data warehousing involves a system that continuously updates the warehouse’s status. Queries run on a real-time data warehouse yield results reflecting the entity’s current status at the time of the query, unlike traditional warehouses that often contain highly latent data. Real-time data warehousing provides low-latency current data, offering insights into the business’s ongoing operations.

Data Science Modeling Interview Questions

Q39) Briefly define the Data Modeling Development Life Cycle.

Identify business requirements and gather accurate data from stakeholders like users, subject matter experts, and business analysts.
Create a project plan specifically for the Data Modeling Development Life Cycle.
Establish guidelines for data modeling development.
Develop Conceptual data models.
Develop Logical data models.
Construct a physical data model.
Generate Lookup Data and build lookup scripts.
Expand with additional entities and attributes as needed.
Generate DDL scripts necessary for database creation.
Execute DDL scripts and lookup scripts in the designated database for data modeling development. Once verified, share results with Database Administrators (DBAs).
Compare the Data Model with the actual database. Release the data model version if everything is satisfactory.

Q40) When should you use dimensional modeling as opposed to normalized modeling?

Normalized Modeling:
- Suitable for predictable data usage.
- Typically employed for Enterprise-Wide use cases.
- When the effort and complexity of building a data store outweigh the maintenance requirements.
Dimensional Modeling:
- Preferable for Line of Business use cases.
- When there’s a need for rapid setup of a data store.
- When there are numerous analytics use cases querying is a primary focus.

ERwin Data Modeler Interview Questions

Q41) What does “Name Hardening” mean in terms of ERWIN?

The Name Hardening Wizard in ERWIN provides a method to prevent the inheritance of names and avoid changes or resets within a model. This tool allows you to specify which object types and instances should have their logical names, physical names, or both hardened within the model, rather than relying on inherited name values.

Q42) What function does Report Designer perform in the ERwin Tool?

Report Designer in ERwin facilitates the creation of a diverse range of reports based on the current data within your data models. With the Workgroup Edition, you can generate Mart administration reports and cross-model reports. Reports can be exported in HTML and CSV formats, and customized report templates can be created for commonly used reports. The tool also enables the construction of report solutions using various templates

Data Modeling Exercises With Answers

Q43) What entities & attributes would you start with when creating a data model for a bank’s customer data?

When initiating the creation of a data model for a bank’s customer data, you would typically begin with the following entities and attributes:

- Customer: Includes attributes such as Name and Date of Birth.
- Contact Information: Contains details like Address and Phone number.
- Products: Encompasses offerings like Auto-Loan, Debit Card, and Credit Card.
- Risk Profile: Involves attributes such as Risk Score, Risk Appetite, and Customer Segment.

Q44) How do you model tables with the same dimensions in a data warehouse? (Consider the below three tables relevant to the hiring process)

Here’s how we can represent the provided tables with similar dimensions:

- Table 1: Displays details of candidates who have been accepted.
- Table 2: Provides information about the candidate currently undergoing evaluation.
- Table 3: Presents the outcomes of the interviews.
- This is how we can structure the provided tables with consistent dimensions:

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Conclusion

With this list of data modeling interview questions and answers, you’re now ready to ace your next job interview. Whether you’re talking about basic concepts, advanced techniques, real-world scenarios, or specific tools, this guide gives you the knowledge and confidence to succeed in data modeling. Go into your interview with confidence, ready to show your skills in managing and analyzing data.

FAQs on Data Modeling Interview Questions

Q1) What are the 4 different types of data models?

The four different types of data models are Relational Model, Hierarchical Model, Network Model, and Entity Relationship Model. The Relational Model organizes data into tables or relations, comprising columns and rows, making it the most popular data model. The Hierarchical Model structures data into a tree-like layout with a single parent or root for each record. The Network Model extends the hierarchical model by facilitating many-to-many links between connected records, indicating multiple parent records. Lastly, the Entity Relationship Model represents relationships between real-world entities, though it's not as closely tied to the database's physical design as the network model.

Q2) What is the purpose of data modeling?

The objective of data modeling is to illustrate the various types of data utilized and stored within the system, their interconnections, potential groupings, and structures, as well as their formats and characteristics. Data modeling seeks to furnish high-quality, precise, structured data that aids in the deployment of business applications and the generation of consistent outputs.

Q3) What is data modeling in Excel?

Data modeling in Excel makes it simple to build links between simple metrics and their underlying data sources. It simplifies data analysis. Creating relationships between matching columns enables the unification of data from various tables across different worksheets.

Q4) What is data modeling in SQL?

Data modeling is a method of organizing and connecting data to perform data analysis. We use data modeling to arrange data for numerous tables.

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