Programming concepts

Question 1

What is data normalization?

Answer 1

Data normalization in programming is the process of organizing and structuring data within a database to reduce redundancy, eliminate anomalies, and enhance overall data integrity.

Question 2

What are main objectives of data normalization?

Answer 2

The main objectives of data normalization are:
- Reduce data redundancy and improve data integrity.
- Eliminate undesirable insertion, update, and deletion dependencies.
- Make the database more informative and adaptable to changes.
- Enhance the efficiency of data storage and retrieval.

Question 3

What are benefits of data normalization? (4)

Answer 3

1. Improved data consistency and accuracy.
2. Reduced data redundancy and storage requirements.
3. Easier data maintenance and updates5.
4. Enhanced query performance and data analysis capabilities3.

Question 4

What are the 3 normal forms in data normalization?

Answer 4

First Normal Form (1NF)
- Ensures that each column contains atomic (indivisible) values.
Eliminates repeating groups and ensures each row is unique2.

Second Normal Form (2NF)
- Builds on 1NF by ensuring all non-key attributes are fully dependent on the primary key.
- Requires the creation of separate tables for subsets of data that apply to multiple rows.

Third Normal Form (3NF)
- Eliminates transitive dependencies between non-key attributes.

Question 5

What are the 5 kind of relationships in databases?

Answer 5

One-to-One (1:1) Relationship
One-to-Many (1:M) Relationship
Many-to-One (M:1) Relationship
Many-to-Many (M:M) Relationship
Self-Referencing (Recursive) Relationship

Question 6

One-to-One (1:1) Relationship

Answer 6

Definition: In this relationship, each record in one table is associated with exactly one record in another table.

Example: A person has one passport. Each passport is assigned to exactly one person.

Use Case: This relationship is used when one entity is closely linked to another, and both share a unique identifier.

Question 7

One-to-Many (1:M) Relationship

Answer 7

Definition: In a one-to-many relationship, a single record in one table can be associated with multiple records in another table, but each record in the second table is associated with only one record in the first table.

Example: A department can have many employees, but each employee belongs to only one department.

Use Case: This is the most common type of relationship. It is typically used to represent parent-child relationships between entities.

Question 8

Many-to-One (M:1) Relationship

Answer 8

Definition: This is essentially the reverse of the one-to-many relationship. Many records in one table can be linked to a single record in another table.

Example: Many employees work in one department. Here, each employee belongs to one department, but a department can have many employees.

Use Case: This is used when multiple entities point to a single entity.

Question 9

Many-to-Many (M:M) Relationship

Answer 9

Definition: In this relationship, many records in one table can be related to many records in another table. This is often implemented using a junction (or associative) table that holds the foreign keys of both related tables.

Example: A student can enroll in many courses, and a course can have many students.

Use Case: This relationship is used when entities are mutually related and both sides can have multiple associations.

Question 10

Self-Referencing (Recursive) Relationship (in databases)

Answer 10

Definition: This is when a table has a relationship with itself. This can represent hierarchical or recursive relationships within the same table.

Example: An employee manages other employees. The “employees” table would have a “manager_id” column that references the same table.

Use Case: Used for hierarchical structures or when an entity has relationships with other instances of the same entity (e.g., an organizational structure).

Question 11

What is A/B testing?

Answer 11

A/B testing is a method of comparing two versions of a webpage, app, or other variable to determine which one performs better. It involves randomly showing different variants to users and using statistical analysis to measure which version achieves better results for specific conversion goals.

Question 12

Why would you use generators instead of list/arrays in Python/JS? (3)

Answer 12

1. Memory Efficiency
2. Working with a big files
3. Creating Infinite Sequences
4. Generators are substantially more memory efficient than lists or arrays because they generate values on-the-fly rather than storing the entire sequence in memory at once.
5. Generators employ lazy evaluation, computing values only when requested through iteration
6. def infinite_counter():
   i = 0
   while True:
   yield i
   i += 1

4.