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🟣 Scala interview questions and answers to help you prepare for your next machine learning and data science interview in 2024.
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🟣 Scala interview questions and answers to help you prepare for your next machine learning and data science interview in 2024.

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# 70 Must-Know Scala Interview Questions









machine-learning-and-data-science






#### You can also find all 70 answers here 👉 [Devinterview.io - Scala](https://devinterview.io/questions/machine-learning-and-data-science/scala-interview-questions)







## 1. What is _Scala_ and why is it important for _machine learning_?



**Scala**, short for Scalable Language, is a robust, highly versatile programming language that runs on the **Java Virtual Machine (JVM)**. It combines functional and object-oriented paradigms, offering features beneficial for machine learning.



### Key Scala Features for Machine Learning



#### Static Typing



Scala's static typing ensures type safety, allowing for more robust and efficient code. For Machine Learning, this can help catch errors early in the development cycle.



#### Functional and Object-Oriented Paradigms



- **Functional Features**: Support for higher-order functions, immutability, and pattern matching.

- **OOP Features**: Encapsulation and inheritance.



#### Conciseness and Readability



Scala's expressive syntax is concise, making it easier to write and understand complex ML algorithms.



#### High Performance



Scala's compatibility with JVM translates to high performance and efficiency, crucial for resource-intensive ML tasks.



### Scala Libraries for Machine Learning



#### Breeze



A powerful numerical processing library, **Breeze**, provides support for linear algebra, signal processing, and statistics.



#### Smile



Specialized for ML tasks, **Smile** offers robust support for clustering, regression, and classification.



#### Spark ML



Built for scalability and integration with Apache Spark, **Spark ML** simplifies distributed ML tasks.



### Sample Code: Using Breeze for Linear Algebra



Here is the Scala code:



```scala

// Import Breeze Linear Algebra

import breeze.linalg.{DenseMatrix, DenseVector, sum}



// Create Matrices

val A = DenseMatrix((1, 2), (3, 4))

val B = DenseMatrix((5, 6), (7, 8))



// Matrix Operations

val C = A + B  // Element-wise addition

val D = A * B  // Dot product



// Compute the Sum of All Elements in 'C'

val matrixSum: Double = sum(C)



// Print Results

println(C)

println(D)

println(matrixSum)

```





## 2. Explain the difference between `var` and `val` in _Scala_.



In Scala, variables are declared with either `val` or `var`. The differences between them are related to **mutability** and **re-assignability**.



### Immutability in Scala



Both `val` and `var` allow the assignment of a value. However, `val` does not permit re-assignment after the initial value is set. This essentially makes `val`-bound variables **immutable**, while those bound with `var` are **mutable** and can be re-assigned during their lifetime.



### Code Example: `val` and `var`



Here is the Scala code example:



```scala

// Using `var` - Mutable

var age = 20

age = 21  // This is allowed



// Using `val` - Immutable

val name = "Alice"

// name = "Bob"  // This assignment will cause a compilation error

```



This strict separation between **immutable** and **mutable** states in program data not only enhances reliability, but also provides better support for **concurrent and parallel programming**. It is a core design principle of both **Scala** and **functional programming** in general, promoting data consistency and reducing the chances of subtle bugs caused by unexpected or inadvertent changes to variable values after their initial assignments.





## 3. What are the main features of _Scala_ that make it amenable for _data science_ and _machine learning_ tasks?



**Scala** has gained popularity in the **data science** and **machine learning** communities due to its unique blend of functional and object-oriented paradigms and its compatibility with Java. 



Here are the key features and tools that make Scala a strong choice for these domains:



### Key Features



#### Immutability



Many **machine learning** algorithms benefit from immutability, simplifying **multi-threading** and promoting safer, functional-style programming.



#### Concurrency



The combination of **immutability** and **actors** from tools such as Akka makes **concurrent** programming more approachable.



#### Type System



Scala's **strong type system** aids in catching errors early in the development cycle. Its support for **type inference** reduces verbosity, improving code readability.



#### Language Interoperability



Scala can **seamlessly integrate** with Java, allowing access to a vast array of libraries and tools built on the Java platform.



#### Ecosystem



**Literate  programming** and **Reactive Streams** support contribute to a more streamlined development experience.



#### Big Data Frameworks



Frameworks like **Apache Spark** and **Flink** offer native support for Scala, further enhancing its suitability for big data and distributed computing tasks.





## 4. Can you describe the type hierarchy in _Scala_?



**Scala**, being a **hybrid** of object-oriented and functional paradigms, features an expressive and intricate type hierarchy. This hierarchy is rooted in a single type, `Any`, which has two direct subclasses: `AnyVal` and `AnyRef`.



### Key Hierarchy Components



1. **Any**: Root type of Scala's type system. All other types are its subtypes. Divided into `AnyVal` and `AnyRef` subtypes.



2. **AnyVal**: Represents values. Subtypes include:



   - **Unit**: Correlates to `void` in Java and signals the absence of a "useful" value.

   - **Boolean**: Equivalent to its Java counterpart, representing `true` or `false`.

   - **Char**: Represents a single character, akin to Java's `char`.

   - **Byte, Short, Int, Long, Float, Double**: Finite-size numeric types, comparable to native types in Java like `int` or `double`.



3. **AnyRef**: Analogous to Java's `Object`, it is a reference type. This is Scala's class and interface types foundation.



### Union Types



Scala 3 introduces **union types**, enabling a value to be of multiple types simultaneously. This design choice aligns with Scala's principles of offering a blend of object-oriented and functional features. 



**Singleton types**, a related concept where a value is constrained to only be an instance of one particular type, are part of the newer Scala releases (courtesy of the Dotty initiative, which ultimately resulted in Scala 3). Collectively, these types contribute to a more comprehensive and adaptive type system in Scala.





## 5. How is _Scala_ interoperable with _Java_?



**Scala** provides extensive interoperability with **Java**, enabling seamless integration of libraries and frameworks from both languages.



### Key Interoperability Features



1. **Unified Object Model**:



   Both languages share an object model, with all classes treated as objects and capable of inheritance. Scala objects directly map to Java objects without the need for wrappers.



2. **Dynamic Dispatch**:

   

   Functions defined in Scala can be invoked dynamically from Java with standard method calls.



3. **Access Modes**:

   

   Scala methods that correspond to Java getters and setters can be accessed directly from Java code using the dot notation.



4. **Automatic Type Conversion**:



   Scala allows automatic conversions between its types and their Java counterparts, eliminating the need for tedious explicit type casting.



5. **Unified Collections Framework**:



   Scala melds seamlessly with Java's collections, either through implicit conversions or direct use of Java collections.



6. **Null-Safety**:

   

   Scala's **Option** type provides a clear and safe mechanism for handling null values, making any Java object `null`-safe when used in Scala.



7. **Interface Implementations**:



   Scala promotes flexible implementations by allowing Java's interfaces to be implemented directly.



8. **Package Visibility**:



   Java's **package-private** visibility modifier is translated to Scala, enabling controlled access.



### Example: Scala-Integration in Java



Here is the Scala code:



```scala

// Scala code

package example



class ScalaClass {

  private var data: Int = 42



  def getData: Int = data

  def setData(newData: Int): Unit = {

    if (newData > 0) data = newData

  }

}

```



And the corresponding Java code with interoperation:



```java

// Java code

package example;



public class JavaApp {

  public static void main(String[] args) {

    ScalaClass scalaObject = new ScalaClass();

    int num = scalaObject.getData();

    scalaObject.setData(num * 2);

  }

}

```





## 6. What are _case classes_ in _Scala_ and how do they benefit _pattern matching_?



A **case class** in **Scala** is automatically equipped with useful features such as `apply` and constructors for creating instances, `unapply` to support deconstruction, and more.



This makes it ideal for **pattern matching**, enhancing both readability and maintainability.



### Key Features



- **Immutability**: Fields are `val` by default, preventing accidental modification.

- **Automatic `equals()` and `hashCode()`**: Encourages safe comparisons and set membership.

- **`Copy` Method**: Enables immutable update operations while maintaining the original instance's integrity.

- **`toString`**: Offers concise textual representation for easy debugging.



### Code Example: Case Class



Here is the Scala code:



```scala

case class Person(name: String, age: Int)

val alice = Person("Alice", 25)

val updatedAlice = alice.copy(age = 26)

```



In this example, `copy` is used to create a new `Person` instance with an updated `age` field. The `equals()` and `hashCode` are also available with the case class `Person`.



### Pattern Matching with Case Classes



Out of the box, **case classes** support deconstruction via the `unapply` method. This feature is key to **pattern matching** in Scala.



Here's the code:



```scala

case class Person(name: String, age: Int)



val bob = Person("Bob", 30)



bob match {

  case Person(name, age) => println(s"Name: $name, Age: $age")

  case _ => println("Unexpected")

}

```



The `match` block unlocks `bob`'s constituent parts, which are then utilized in the first `case` to extract and print `Bob`'s `name` and `age`.



### Custom Extractors



You can further refine unapply's behavior, enabling more sophisticated deconstruction, by crafting a custom **extractor object**.



Here is the Scala code:



```scala

case class Employee(name: String, age: Int, role: String)



object OlderEmployee {

  def unapply(employee: Employee): Option[(String, Int, String)] = {

    if (employee.age > 40) Some((employee.name, employee.age, employee.role))

    else None

  }

}



val veteranEmployee = Employee("John Doe", 45, "Senior Developer")



veteranEmployee match {

  case OlderEmployee(name, age, role) => println(s"Congrats, $name! You're a $role at $age.")

  case _ => println("Keep up the hard work!")

}

```



In the example, the `match` block leverages the `unapply` method of the `OlderEmployee` object to distinguish employees older than 40. This process empowers both explicit and readable code.





## 7. Explain the concept of `object` in _Scala_ and its usage.



In Scala, an **object** is a central construct designed to provide a way to create singletons, contain **static members**, and serve as an entry point for running Scala applications. 



### Core Features of `object`



- **Singleton**: Objects are instantiated only once in the JVM and can't be initialized or recreated.



- **Automatic Instantiation**: Objects are instantiated automatically, making them akin to a `static` member of a class in Java.



- **Code Cohesion**: Facilitates bundling related functions and data without needing to create separate classes and instances of those classes.



- **Global Scope**: Objects are globally accessible within their packages, effectively acting as a global variable container.



- **Thread Safety**: Provides inherent thread safety due to single instantiation in a multithreaded environment.



### Code Example: Using `object`



Here is the Scala code:



```scala

object MathUtils {

  val pi: Double = 3.14159

  def add(a: Int, b: Int): Int = a + b

  def multiplyByPi(x: Double): Double = x * pi

}



object MyApp extends App {

  println(MathUtils.add(4, 5))        // Output: 9

  println(MathUtils.multiplyByPi(2))  // Output: 6.28318

}

```





## 8. How do you implement _traits_ in _Scala_?



In **Scala**, a trait is analogous to a **Java interface**, but can also include **method implementations** and even fields. Traits allow for **multiple inheritance**, defining a consistent structure for related classes.



### Syntax



A trait is declared using the `trait` keyword. You can add method and field definitions, along with their possible implementations:



```scala

trait Speaker {

  def speak(): Unit

  def greet(name: String): String

}



trait Greeter {

  def greet(name: String): String = {

    s"Hello, $name!"

  }

}

```



### Integrating Traits with Classes



You can integrate traits with a class through either of these methods:



1. **Inherited Methods**: These methods can be directly inherited from traits.

2. **Overridden Methods**: Your class can selectively override any methods from the trait.



### Code Example: Inheriting Traits



Here is the Scala code:



```scala

class Person extends Speaker with Greeter {

  override def speak(): Unit = {

    println("I'm speaking!")

  }

  // greet() from Greeter trait is provided as default implementation

}

```



In this example, the class `Person` acquires both methods from the `Speaker` and `Greeter` traits.



### Code Example: Overriding Trait Methods



Here is the Scala code:



```scala

class SalesPerson extends Person with Speaker with Greeter {

  override def speak(): Unit = {

    println("I'm a good speaker and motivator!")

  }

  // greet() from Speaker trait is overridden

  override def greet(name: String): String = {

    s"Hi $name, have a great day!"

  }

}

```



In this example, `SalesPerson` class will have its `speak()` method and override the `greet()` method provided by the `Speaker` trait. Similarly, it will inherit the `greet()` method from the `Greeter` trait and override it. This demonstrates that traits are **stackable**.





## 9. Discuss the importance of _immutability_ and how _Scala_ supports it.



**Immutability** is the key feature in many functional programming languages like **Scala**, which ensures that once an object is created, it cannot be changed. This principle has several advantages, particularly in the context of **concurrent programming**, **safety**, and **performance**.



The concept of **immutability** means that data, once created, cannot be modified, and as a result, does not change state over time. This paradigm is in contrast to mutability, where data can be freely modified after creation.



### Advantages of Immutability



- **Simplicity**: Avoiding state changes simplifies the code and makes it easier to understand, read, and maintain.



- **Safety and Predictability**: Multiple threads can access and work with immutable data without the risk of it being altered concurrently.



- **Concurrency Control**: Mutability can make concurrent programming error-prone. Immutable data structures can simplify multi-threaded programming, reducing the need for locks or atomic operations.



- **Thread-Safe by Design**: Immutability inherently provides thread safety, making it easy to write parallel and concurrent systems.



- **State Management**: By avoiding state changes, code becomes more predictable and easier to reason about, diminishing the potential for unexpected interactions.



- **Debugging and Testing**: Immutable data is easier to test and debug, as data doesn't change over time.



### Scala's Support for Immutability



- **val vs. var**: Scala provides distinct keywords for defining mutable (`var`) and immutable (`val`) variables. Once you assign a value to a `val`, you can't reassign it.



- **Collections**: Scala offers a rich set of immutable collections in the `scala.collection.immutable` package. These collections are designed to be thread-safe and provide methods that return new versions of the collection instead of modifying the existing one.



- **Case Classes**: Objects created from **case classes** are immutable by default. This immutability arises from their design as primarily data-holding structures, favoring immutable values.



- **Pattern Matching**: When used with case classes, pattern matching in Scala can ensure that the data within an object is not accidentally modified, further asserting immutability.



- **Subtle Immutability**: In Scala, even if a reference to a mutable object is assigned to `val`, it doesn't make the object inside the reference immutable. The immutability or mutability is at the data level, not at the reference level.



- **Support for Java Immutables**: Scala can work with Java's immutable objects like `java.util.Collections.unmodifiableList` if required.



### Code Example: Immutable and Mutable Data in Scala



Here is the Scala code:



```scala

// Immutable List

val immutableList = List(1, 2, 3)  // immutableList cannot be reassigned

val newList = 4 :: immutableList  // A new list with 4 added is created



// Mutable List

var mutableList = collection.mutable.ListBuffer(1, 2, 3)

mutableList += 4  // The list is modified

```





## 10. Explain the difference between a _sequence_ and a _list_ in _Scala_.



In **Scala**, both **sequences** and **lists** represent ordered collections of elements, but they differ in several key aspects.



### Primary Distinctions



- **Immutability**: Sequences can be mutable or immutable, while lists are always immutable.

- **Data Structure**: Sequences can be based on arrays or linked lists, whereas lists are strictly linked list-based.

- **Performance**: Arrays typically offer $O(1)$ access times and support efficient element replacement, while linked lists provide $O(1)$ prepend times.



### Core Interfaces



- **Seq**: The base trait for both sequences and lists.

- **LinearSeq**: A specialized trait for structures with efficient $O(1)$ head and tail retrieval, such as lists.



### Code Example: Selecting an Element by Index



Here is the Scala code:



**Array-based Seq**:



```scala

val arraySeq: Seq[Int] = Seq(1, 2, 3, 4, 5) // ArraySeq

val firstElement = arraySeq(0)  // O(1) access

```



**List-based Seq**:



```scala

val listSeq: Seq[Int] = List(1, 2, 3, 4, 5) // List

val firstElement = listSeq(0)  // O(n) access, n = 0

```



In the example above, the performance difference between accessing the first element of the two sequences is showcased.





## 11. What are the advantages of using _Option_ in _Scala_?



In Scala, **Option** is a powerful tool that offers clear benefits throughout the development process.



### Core Advantages of Using `Option`



1. **Robust Error Handling**: It provides a structured approach for situations where a method might not return a valid value, thereby reducing the chances of **null-pointer exceptions**.

2. **Distinct Semantics**: By explicitly differentiating between a valid result (Some) and the absence of one (None), it promotes logical clarity and aids in **code comprehension**.

3. **Forced Consideration**: The `Some` and `None` constructs compel developers to actively assess potential absence or presence of a value, which leads to a *more mindful* coding practice.

4. **Enhanced API Safety**: When a method accepts or returns an `Option`, it signals to users that no values are guaranteed, thereby ensuring safer, more **predictable interactions**.



### Code Example: Using Options for Error Handling



Consider the code:



```scala

// Return student grade from a Map

def getStudentGrade(studentId: Int): Option[Int] = {

  val grades = Map(1 -> 85, 2 -> 90, 3 -> 78, 4 -> 92)

  grades.get(studentId)

}



// Call to getStudentGrade

val grade = getStudentGrade(5)



// Process the grade

grade match {

  case Some(g) => println(s"The student's grade is $g")

  case None => println("Student not found or grade not available")

}

```



1. **Robustness**: The method is safe and won't throw NullPointerException even when the studentId is not in the Map.

2. **Clarity**: The `Option` return type clearly communicates to the caller that the result might be absent.

3. **Enforced Handling**: The match statement requires the developer to explicitly account for both `Some` and `None` cases.

4. **Predictability**: For the caller, the method's behavior is consistent – it either returns a grade or indicates its absence.



### When to Use Options



- **Returning Irregular Results**: Use it when a method might not always produce valid outputs.

- **API Design**: Use it when designing APIs to indicate optional return values or parameters.





## 12. Discuss the role of _implicit parameters_ in _Scala_.



In **Scala**, **implicit parameters** offer a powerful way to **reduce boilerplate** by letting the compiler fill in non-explicit function parameters.



### Why Use Implicit Parameters?



1. **Global Environment**: They're often used to propagate information through the codebase, reducing the need for manual parameter passing. For instance, you can set locale, database connections, or logging levels without passing them to every function.



2. **Type Classes**: They're foundational to defining and utilizing type classes. Such as Ordering in collections like sort.



3. **Fluent Interfaces**: They can help in building more expressive DSLs and fluent interfaces by eliminating repetitive parameters.



4. **Library Integration**: They're widely used in libraries like Akka, Play Framework, and Cats, which leverage implicits for improved flexibility and functionality.



### How Do Implicits Work?



#### Declaration



You use the `implicit` keyword before the **parameter** and **value** declarations:



```scala

// Implicit parameter

def printStr(str: String)(implicit printer: Printer): Unit = printer.printLn(str)



// Implicit value

implicit val defaultPrinter: Printer = new ConsolePrinter



```



#### Lookup Rules



1. **Local Scope**: If you provide an implicit declaration within the same scope where the function is being called, that declaration will be used. If there are multiple valid candidates, ambiguity arises.



2. **Companion Objects**: For implicit classes, the compiler checks the companion object of the class for implicit definitions. 



3. **Enclosing Scope**: If no implicit is found in the local scope or the companion object for an implicit parameter, the compiler searches up the scope chain.



#### Disambiguation



When the compiler encounters multiple valid candidates for an implicit, it results in ambiguity. Use **one of three approaches** to resolve the conflict:



- Declaring the type explicitly in the function call, informing the compiler of which implicit to pick

- Making use of non-implicit parameters

- Using different scoping mechanisms to limit the set of candidate implicits



### What to Consider



- **Readability**: While implicits can make the code shorter, overuse or misuse can lead to unclear code and "magic" behavior.



- **Surprise Factor**: Unaware developers may find the behavior introduced by implicits surprising or hard to track.



- **Flexibility vs Rigidity**: While implicits provide flexibility, they can also unexpectedly change behavior.



Experienced Scala developers use implicit parameters judiciously and document their usage to balance their benefits with potential drawbacks.





## 13. How do _for-comprehensions_ work in _Scala_?



In **Scala**, a **for-comprehension** is a high-level language construct that enables seamless iterations across **collections**, abstracts, or data types that define a specific behavior.



You can think of it as a more readable and intuitive **syntactic sugar** that simplifies working with `map`, `flatMap`, and `filter` operations.



### Key Components



- **Generators**: These specify the data sources you want to iterate over.

- **Filters**: Optional, they help you select specific elements meeting certain criteria.

- **Variables**: These are declared within the for-comprehension and preserve values throughout the loop.

- **Statements**: Enclosed within curly braces, they define actions to be carried out for each iteration.



### Example: List of Squares



Here is the corresponding Scala code:



```scala

val numbers = List(1, 2, 3, 4, 5)



val squares = for {

  n <- numbers          // Generator: n iterates over elements of 'numbers'

  if n % 2 == 0          // Filter: only selects even numbers

  square = n * n         // Variable: computes square of the current n

} yield square



println(squares)        // Output: List(4, 16)

```



### Syntax Breakdown



- **Generator**: `n <- numbers` indicates `n` iterates over the numbers list.

- **Filter**: `if n % 2 == 0` applies the filter, ensuring only even numbers get selected.

- **Variable** and **Statement**:  `square = n * n` declares the local variable `square` and computes its value.



### Under the Hood



When you evaluate a for-comprehension, Scala uses the underlying `map`, `flatMap`, and `filter` operations:



- **map**: Transform the iterator, in this case, by squaring each number.

- **filter**: Apply the condition, keeping only the numbers matching the requirement.

- **flatMap**: If present, this operation can be thought of as "flattening" nested structures. For instance, a list of lists would be converted to a single list.



Lastly, the for-comprehension wraps up these intermediate transformations and combines them in a way that's coherent with your program's logic, offering a clean and readable approach to handling collections.



### Common Use-Cases



- **I/O Abstractions**: When working with input/output routines such as reading from a file or database.

- **Error Handling**: For handling exceptions or results that might be erroneous.

- **Monads**: As a way to interact with monad types (e.g., `Optional`, `Try`, or `Future`) more intuitively.



The flexibility of for-comprehensions and their ability to abstract away the details of applying transformations make them a valuable tool in your Scala repertoire.





## 14. What is _functional programming_ and how does _Scala_ support it?



**Functional programming** (FP) is a programming paradigm that treats computation as the evaluation of mathematical functions. Instead of using program states, **FP** emphasizes **immutable data** and **expressions**.



### Key Concepts of Functional Programming



- **First-Class Functions**: Functions are treated like any other data type; they can be assigned, passed as arguments, and functions.



- **Pure Functions**: Functions have no side effects and the output depends only on the input. They are deterministic.



- **Immutable State**: Data once defined cannot be changed, promoting safer multi-threading.



- **Recursion over Loops**: Loops are replaced with recursive functions.



- **Pattern Matching**: A form of wildcard matching for concise conditional logic.



### Scala's Features for Functional Programming



**Scala** is a hybrid language, combining aspects of both object-oriented and functional programming. It incorporates several concepts that make it favorable for FP.



- **Higher-Order Functions**: Functions can accept other functions as arguments and return functions as results.



- **Lazy Evaluation**: Delayed computation until a result is necessary, aiding performance in some scenarios.



- **Type Inference**: Automatically assigns data types, reducing verbosity in function definitions when the type is evident.



- **Pattern Matching**: Simplifies conditional logic by matching data to patterns.



- **Immutability**: Scala supports both mutable and immutable data. However, it encourages the use of immutability for better parallelism and safety.



- **Tail-call Optimization**: Improves the efficiency of recursive functions.



- **Algebraic Data Types**: Introduced in Scala through case classes, they provide a structured way to define composite data types.



- **Type System**: Strong and static, detects most type-related errors at compile-time.



- **Concurrency Support**: With libraries like Akka and its actor model, Scala simplifies concurrent programming.



- **Library Support**: Frameworks like Spark and libraries like Cats and Scalaz further solidify Scala's standing as a versatile FP language.





## 15. Explain _higher-order functions_ in _Scala_.



In functional programming, **higher-order functions** treat other functions as input or output. This paradigm is core to languages such as Scala, enabling concise, elegant, and powerful programming.



The direct application of higher-order functions often includes:



- **Reducing code duplication**: By abstracting common operations.



- **Modularisation**: Encouraging a divide-and-conquer approach to problems, making solutions more maintainable and testable.



- **Flexible abstractions**: Offering various ways to pass and treat functions, boosting flexibility and code reusability.



### Higher-Order Function Types in Scala



Scala is a statically-typed language, which distinguishes it from many dynamically-typed languages when it comes to higher-order functions. Scala provides two main signatures for higher-order functions:



1. **Parameter Functions**: These take in at least one function as a parameter.

2. **Return Functions**: These return a function, possibly using another function argument in that process.



### Parameter Functions



The typical example in Scala is the `map` function, as found on sequences like lists, options, or futures. Here's its general signature:



```scala

def map[B](f: A => B): List[B]

```



### Return Functions



Higher-order functions that return functions provide a unique advantage in **abstraction on demand**. A common example in Scala is `andThen`, where a sequence of operations can be expressed concisely. In this illustration, `andThen` composes a sequence using two functions, `f` and `g`:



```scala

def andThen[C](g: B => C): A => C

```





#### Explore all 70 answers here 👉 [Devinterview.io - Scala](https://devinterview.io/questions/machine-learning-and-data-science/scala-interview-questions)









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