Unlocking Success: Data Analytics Interview Questions & Answers for Accenture

Are you gearing up for a data analytics interview with Accenture? Excelling in the interview process requires more than just technical knowledge—it demands a strategic approach and a deep understanding of key concepts. In this blog, we’ll unravel essential data analytics interview questions and provide expert answers tailored for Accenture candidates. From supervised learning to feature engineering and beyond, equip yourself with the insights needed to stand out and secure your dream role in data analytics at Accenture.

SQL tables and database theory

Question: What is a table in a database?

Answer: A table in a database is a structured collection of data organized into rows and columns. Each row represents a record, and each column represents a different attribute or field of the record.

Question: What are primary keys and foreign keys?

Answer:

Primary Key: A primary key is a unique identifier for each record in a table. It ensures that each row in the table can be uniquely identified. Primary keys are usually implemented using a single column or a combination of columns.

Foreign Key: A foreign key is a column or a set of columns in a table that establishes a relationship with another table’s primary key. It enforces referential integrity by ensuring that the values in the foreign key column(s) match the values in the primary key column(s) of the referenced table.

Question: What is normalization, and why is it important?

Answer: Normalization is the process of organizing data in a database efficiently. It involves breaking down large tables into smaller tables and defining relationships between them to reduce redundancy and dependency. Normalization helps minimize data duplication, ensuring data consistency, and improving database efficiency.

Question: Explain the difference between a clustered and a non-clustered index.

Answer:

Clustered Index: In a clustered index, the rows of the table are stored physically on the disk in the order of the index key. Each table can have only one clustered index, and it determines the physical order of data in the table.

Non-Clustered Index: In a non-clustered index, a separate structure is created that contains the index key values along with pointers to the corresponding table rows. The table data is stored separately from the index structure. A table can have multiple non-clustered indexes.

Question: What is ACID in database transactions?

Answer: ACID stands for Atomicity, Consistency, Isolation, and Durability, which are the four properties that guarantee the reliability of database transactions.

• Atomicity: Ensures that a transaction is treated as a single unit of work, either all of its operations are completed successfully, or none of them are.
• Consistency: Ensures that the database remains in a consistent state before and after the transaction.
• Isolation: Ensures that the operations within a transaction are isolated from other concurrent transactions until the transaction is complete.
• Durability: Ensures that once a transaction is committed, its changes are permanently saved in the database even in the event of system failures.

Question: What is a database index, and why is it used?

Answer: A database index is a data structure that improves the speed of data retrieval operations on a database table at the cost of additional space and processing overhead during data modification. Indexes are used to quickly locate data without having to search every row in a table. They are typically created on columns frequently used in search conditions or join operations.

Question: Explain the difference between OLTP and OLAP.

Answer:

• OLTP (Online Transaction Processing): OLTP systems are designed for transaction-oriented applications where the emphasis is on fast query processing, maintaining data integrity, and ensuring concurrent access by multiple users. Examples include banking systems, e-commerce applications, and airline reservation systems.
• OLAP (Online Analytical Processing): OLAP systems are designed for analytical and decision-support applications where the emphasis is on complex queries, aggregation of large volumes of data, and multidimensional analysis. OLAP systems are optimized for read-heavy workloads and typically involve historical data analysis, data mining, and business intelligence applications.

Question: What is a stored procedure?

Answer: A stored procedure is a precompiled set of SQL statements that are stored in the database and can be executed multiple times by invoking its name. Stored procedures allow for modular programming, code reusability, and improved performance by reducing network traffic and database round-trips. They can accept parameters, perform calculations, execute queries, and return results to the calling application.

Question: What is the difference between bagging and boosting?

Answer: Bagging and boosting are both ensemble learning techniques used in machine learning to improve the performance of models. However, they differ in their approach and the way they combine multiple models.

• Approach: Bagging trains multiple models independently on random subsets of the data while boosting trains models sequentially, with each model focusing on the errors made by its predecessors.
• Model Combination: Bagging combines predictions through averaging or voting with equal weights while boosting combines predictions through weighted averaging, giving higher weights to better-performing models.
• Performance Improvement: Both bagging and boosting aim to reduce variance and improve model generalization, but they achieve this through different strategies. Bagging reduces variance by averaging out predictions from different models while boosting reduces bias by focusing on instances that are difficult to classify.
• Example Algorithms: Random Forest is a popular bagging algorithm, while AdaBoost and Gradient Boosting are popular boosting algorithms.

Question: What are various Data Modelling Techniques?

Answer: Data modeling is the process of creating a conceptual representation of the data structures and relationships within a domain.

• Entity-Relationship Modeling (ER Modeling): Represents entities, attributes, and relationships in relational databases using symbols like rectangles, diamonds, and lines.
• Dimensional Modeling: Organizes data in data warehousing using fact tables (quantitative measures) and dimension tables (descriptive attributes) in star or snowflake schema structures.
• Normalization: Reduces redundancy and dependency in relational databases by breaking down tables into smaller, more manageable forms following normal forms like 1NF, 2NF, 3NF, and BCNF.
• Object-Oriented Data Modeling: Designs data structures in object-oriented programming using classes, attributes, methods, and associations to represent real-world objects and their behaviors.
• Graph Data Modeling: Represents complex relationships and networks between entities using nodes and edges, commonly used in graph databases like Neo4j.

Question: What is Python?

Answer:

• High-Level Language: Python is a high-level programming language that abstracts away complex details, making it easy to write and understand code.
• Interpreted: Python code is executed line by line by an interpreter, allowing for rapid development and testing.
• Versatile: Python supports multiple programming paradigms, including procedural, object-oriented, and functional programming, making it suitable for a wide range of applications.
• Extensive Standard Library: Python comes with a rich standard library that provides modules and packages for various tasks, reducing the need for external dependencies.
• Active Community: Python has a large and active community of developers who contribute to its ecosystem, creating libraries, frameworks, and tools for different domains.

Question: What is SQL?

Answer: SQL (Structured Query Language) is a domain-specific language used for managing and manipulating relational databases. It provides a standardized way to interact with databases, allowing users to perform tasks such as querying data, inserting, updating, and deleting records, creating and modifying database schemas, and defining access controls. SQL is widely adopted across various industries and is essential for working with relational database management systems (RDBMS) like MySQL, PostgreSQL, SQL Server, and Oracle.

Question: Difference between the primary key and a foreign key in SQL?

Answer: The difference between a primary key and a foreign key in SQL lies in their roles and constraints within a database:

• Primary Key:

A primary key uniquely identifies each record in a table.

There can be only one primary key in a table.

It must contain unique values and cannot contain NULL values.

Primary keys enforce entity integrity and ensure data consistency within the table.

• Foreign Key:

A foreign key establishes a relationship between two tables.

It references the primary key of another table, defining a parent-child relationship.

Foreign keys maintain referential integrity by enforcing constraints that ensure that values in the foreign key column(s) match values in the primary key column(s) of the referenced table.

Multiple foreign keys can exist in a table, and they can contain NULL values unless specified otherwise.

Question: What are the main differences between data mining and data analysis?

Answer: Data mining and data analysis are both processes used to extract insights from data, but they differ in their focus, techniques, and objectives:

Focus: Data mining aims to discover patterns, trends, and relationships within large datasets using techniques like machine learning, clustering, and association rule mining, while data analysis involves examining, cleaning, and visualizing data to understand its structure and patterns.

Techniques: Data mining employs classification, regression, clustering, and predictive modeling to uncover patterns and make predictions, while data analysis utilizes statistical analysis, exploratory data analysis, and visualization to extract insights and validate hypotheses.

Objectives: Data mining seeks to discover new knowledge, identify patterns, and make predictions or recommendations based on historical data, whereas data analysis aims to understand underlying patterns, validate hypotheses, and derive actionable insights to support decision-making processes.

Scope: Data mining deals with large volumes of data from multiple sources to uncover hidden patterns, while data analysis can involve both large and small datasets, focusing on specific questions or areas of interest.

SQL-related questions (DDL and DML commands)

Question: What is DDL in SQL?

Answer: DDL (Data Definition Language) is a subset of SQL used for defining and managing database structures such as tables, indexes, views, and constraints.

Question: List some common DDL commands in SQL.

Answer: Common DDL commands include:

• CREATE: Used to create database objects like tables, indexes, and views.
• ALTER: Used to modify existing database objects by adding, modifying, or dropping columns, constraints, or indexes.
• DROP: Used to remove database objects such as tables, indexes, or views.
• TRUNCATE: Used to remove all records from a table while keeping the table structure intact.

Question: What is DML in SQL?

Answer: DML (Data Manipulation Language) is a subset of SQL used for manipulating data stored in the database, such as inserting, updating, deleting, and retrieving records from tables.

Question: List some common DML commands in SQL.

Answer: Common DML commands include:

• SELECT: Used to retrieve data from one or more tables based on specified criteria.
• INSERT: Used to add new records into a table.
• UPDATE: Used to modify existing records in a table based on specified conditions.
• DELETE: Used to remove records from a table based on specified conditions.
• MERGE: Used to perform an “upsert” operation, combining INSERT and UPDATE based on a specified condition.

Question: What is overfitting in Machine Learning?

Answer: Overfitting in machine learning occurs when a model learns the training data too well, capturing noise rather than underlying patterns. This results in poor generalization to new data, as the model performs well on training data but poorly on unseen data. Overfitting can be mitigated by using simpler models, increasing training data, applying regularization, and using cross-validation techniques.

Other questions

• How do you slice a list in Python?
• Questions mostly come from DBMS and coding-related, ML, SQL, Stats, Python, and Excel.
• What are the key requirements for becoming a Data Analyst?
• Why would you like to join our company?
• Why should we hire you?
• What are your strengths and weaknesses?
• Why do you want to work here?
• How do you manage the conflict with your colleagues in the work?
• How do you see yourself 5 years from now?

Conclusion

Mastering data analytics interview questions is crucial for landing a rewarding career opportunity at Accenture, a global leader in consulting and technology solutions. By understanding key concepts such as supervised and unsupervised learning, feature engineering, model evaluation, and data visualization tools, you’ll be well-prepared to impress recruiters and secure your dream job. Remember to stay updated with the latest trends in data analytics and continuously hone your skills to stand out in the competitive landscape. With dedication and preparation, you can embark on a successful journey in the dynamic field of data analytics at Accenture.