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25 Advanced SQL Interview Questions for Data Scientists – KDnuggets

Check out this collection of advanced SQL interview questions with answers.

25 Advanced SQL Interview Questions for Data Scientists
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The standard language used for all databases is Structured Query Language(SQL). 
It plays a key role to fetch the demandable data from the database. All the big data platforms like Hadoop, and Spark uses SQL as a high-level language for their organizations.
Data science is the only field of study and analysis of data. The outcomes of the data should be accurate and correct to perform a survey. 
This is how SQL comes into the picture with data science. Though certain organizations have made a switch to NoSQL, the fundamentals lie in SQL. 
Gearing up for a data science interview? 
Here are some necessary interview questions and answers on SQL that you must learn.
Normalization is a method of organization of the data in the database to minimize redundancy from a set of relations. The concept of Normal forms is used to perform normalization on a relation. 
The ACID properties are as follows:
In terms of a general scenario, the approaches that can be followed to find all duplicates in a table are as follows:
Below are the differences between a clustered and non-clustered index:
The database optimization technique in which we add data(redundant)  to the table(s) is called denormalization. The technique is useful as it helps to reduce the costly joins in the database.
In a SQL server, collation provides sorting rules, accent, and case sensitivity properties to the data in the database. It represents each character in the database by defining the bit patterns using collation.
Some of the collation levels are as below:
Below are the differences between inner, outer, and full outer join:
Below are the differences between a join and a union:
Union keeps unique records whereas Union keeps all records including all the duplicates. Also, the union does a deduplication step before returning the data and the union all prints concatenated results 
Consider a table with the below data and table name as EMP-
Name    Salary
John     990000
Ellie      100000
Nick      400000
Sam      500000
The query would be:

Transpose in SQL is defined as changing a row or column into a particular format to visualize the data from a new perspective. 
One basic transpose is changing a row to a column or a column to a row. Typical transpose methods are dynamic and joined transposition.
All of the transpose methods are used for data analysis for a fruitful outcome. 
A B-tree is defined as a self-balancing search tree in which all leaves are at the same level. To insert data in a B-tree, it is inserted at the leaf node. 
In the B-trees index, the tree has a key-value pair which is called a payload. This value is referenced to the actual data record.
When we use a B-tree index, the database searches a given key with correspondence to B-tree and gets the index. 
Below are the differences between DELETE and TRUNCATE commands:
It eats up your space. The larger your table, the larger will be the index. 
ADD, DELETE or UPDATE of certain rows in the table must be also done to our index.
As a thumb rule, an index must be created on a table if the table in the indexed column will be required frequently.
Below are the advantages of  having an index:
Indexing is a data structure technique to optimize the database performance by reducing disk access during the time a query is being processed.  
Indexing has the following attributes:
Businesses have to comprehend a set of data that helps finance leaders and business teams to get insights into their users and the performance of multiple related factors.
A business can have various data across the business process and strategies that help teams to analyze the performance at a more granular level.
These data are stored in multiple database systems and thus require consistent, real-time, and more flexible processes that help the team analyze the data and process further for more analysis.
Online Analytical Processing or OLAP contains software tools that are used for analyzing business data and help get insights into the database.
For example, OLAP can be used to build the Spotify song recommendation engine that automatically generates a playlist for users based on their song choices and historical listening data.
The transaction is an integral part of every business. A business offering services to its customers receive an amount when the customer uses the services.
An invoice gets immediately generated when users pay for the services of their choice.
The transaction database comes into the picture when we have to store the transaction information of all users.
However, Online Transaction Processing (OLTP) offers transaction-oriented applications that are categorized in a 3-tier architecture.
25 Advanced SQL Interview Questions for Data Scientists
Source: InterviewBit

OLTP helps businesses to perform certain activities related to the transaction database.
For example, Transaction through online banking serves the same purpose where OLTP can be used to operate the user transaction information.
SQL is used for querying databases and modifying the values in large datasets. It also provides a way to manipulate data when there are multiple cases. 
For example, a table contains a list of employees(name) along with experience (EXP) and joining date(ac_date). 
Now, if we want to query the data in such a way that if the experience is greater than(>) “4”, then insert “Is a senior” in a new column named “exp_level”, otherwise insert NULL in the column.
You can query the database in such cases using the CASE WHEN statement.
The CASE WHEN statement is similar to the If/else condition in programming languages like C, C++, Python, etc.
The CASE statement is followed by one pair of WHEN and THEN statements. However, you can add nested statements based on your requirements.
Once the CASE statement is completed, it is finished by adding the ending statement that starts with ELSE and END AS.
In the example above, we have employee.database that lists out the employees of a company with attributes such as employee name, joining year, experience, and more. Using the CASE WHEN statement in this example, 

SQL offers HAVING clauses to filter a group of data in a database.
For example, company XYZ has a list of employees who have more than 1 year of experience.
The database contains rows and columns including the employee name, joining data, experience, and experience level.
You can retrieve a list of employees who have more than 5 years of experience.
The HAVING clause provides you with a way to control the information and modify the data with a bunch of data.
The syntax of the HAVING clause is the HAVING condition.
In the example, we can retrieve the list of employees having more than 5 years of experience by querying the database as below:

WHERE clause is used when you want to filter the records based on a particular condition.
For example, in an employee table, we can retrieve the list of all employees who have exactly 5 years of experience in a company. The syntax of the WHERE clause is, WHERE condition.
In the example, we can extract the name of employees who have 5 years of experience by querying the database as below:

The PL/SQL is a block-structured language that encourages developers to use the power of SQL using procedural statements.
It is a featured procedural language that has embedded the power of decision-making and many more features of POP.
Using a single block command, PL/SQL can run multiple queries with the support of extensive error checking.
PL/SQL is an extension of SQL that is used to create applications. 
ETL stands for ‘Extract, Transform and Load”. It uses the concept of data warehousing to make data visualization and data analysis. In a broader context, it is used for data integration.
Any system where data is taken from one system and stored/copied in another destination system and its data is represented differently is called an ETL process.
SQL offers Data Manipulation Language (DML) and Data Definition Language(DDL) to perform certain operations while querying the database.
When a particular type of operation is performed on the database, it automatically executes some actions on the database as well.
When these types of actions are triggered in the database, they are known as Nested triggers.
SQL categorizes the nested triggers into two main categories- AFTER triggers and INSTEAD OF triggers.
As the name suggests AFTER trigger is executed after a DML or DDL operation is performed on the database. 
However, INSTEAD OF trigger is executed in place of DML and DDL operations.
The commit makes sure the data is consistent and maintained in the updated state after the current transaction ends. A new record is added to the log memory when a commit is used.
In the case of a checkpoint, it is used to write all the changes that are committed to disk up the system change number in the control files and header files. 
In this article, we have discussed in detail all the SQL questions that are asked in interviews for data scientists.
If you are a beginner you can learn SQL from:
Happy Learning!
Vaishnavi Amira Yada is a technical content writer. She has knowledge of Python, Java, DSA, C, etc. She found herself in writing, and loves it.
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