Use our engineer-created questions to interview and hire the most qualified Clojure developers for your organization.
Clojure is related to Lisp and uses a programming style called “functional programming.” It also uses well-known technologies like the Java Virtual Machine (JVM), JavaScript engines, and the NET runtime. It’s often used for web development, data processing and analysis, and distributed systems.
Our team has come up with practical coding tasks and interview questions that are designed to test developers’ Clojure skills during coding interviews. We’ve also put together a list of the best tips to make sure that your interview questions really test how knowledgeable the candidates are in Clojure.
Lisp is one of the oldest and most influential programming languages still in use today. Originally conceived by John McCarthy in the late 1950s, Lisp pioneered key computer science concepts like recursion, conditional expressions, dynamic typing, and garbage collection. While not as widely used as languages like Java or Python, Lisp remains an important tool for artificial intelligence, natural language processing, and symbolic computation.
As a Lisp programmer, you need a strong grasp of the language’s unique capabilities and design philosophy Expect Lisp-related questions in any programming interview, particularly for roles involving AI, machine learning, or data science Mastering the fundamentals of Lisp will prepare you to ace the technical screening and highlight your adaptability.
Here are some of the most common Lisp interview questions you’re likely to encounter:
1. What is Lisp and how does it differ from other languages?
Lisp (which stands for LISt Processing) is a high-level, general-purpose programming language. It uses symbolic expressions and list-based data structures rather than syntax familiar to programmers of languages like C++ or Java. Code and data have the same structure in Lisp, making the language homoiconic. This enables metaprogramming through macros and other powerful techniques. Lisp also relies heavily on recursion and supports dynamic typing.
2. What are the key features and principles of Lisp programming?
Some key features/principles of Lisp include:
- Symbolic expressions (S-expressions) as both code and data
- Use of lists as core data structures
- First-class functions that can be nested and passed as arguments
- Dynamic typing system
- Automatic garbage collection
- Support for recursion and functional programming
- Macros for code transformation and extending the language
- REPL environment for interactive development
3. How are data types handled in Lisp?
Lisp employs a dynamic typing system. Variables do not have a fixed data type; they can hold any kind of data. Common data types include numbers, characters, strings, symbols, lists, and functions. Lists are implemented as linked lists using cons cells. Symbols serve as identifiers and are unique. Functions are first-class objects in Lisp.
4. Explain how conditionals and flow control work in Lisp.
Lisp has conditional expressions like if, cond, and when. An if expression checks a condition and evaluates one of two forms based on whether the condition is true. cond allows chained conditional checks, while when executes a form when a condition holds.
Lisp also supports recursion instead of loops. Functions can call themselves recursively to achieve repetition. Special operators like mapcar and reduce support iteration over lists.
5. How does Lisp handle state and side effects?
Lisp allows side effects and state through special operators like setf, which alters data destructively. However, idiomatic Lisp style avoids side effects where possible and favors recursion over iteration. For concurrency, Lisp provides synchronization primitives like locks but uses shared memory and mutable state by default. Some dialects like Clojure emphasize immutability and isolation of side effects.
6. What are first-class functions and why are they important in Lisp?
First-class functions are programming language entities that can be created at runtime, stored in data structures, passed as arguments, and returned from other functions. Lisp supports first-class functions through constructs like lambda. They enable functional programming techniques and writing higher-order functions. First-class functions are essential for leveraging code-as-data in Lisp.
7. How does Lisp implement polymorphism?
Lisp enables polymorphism through its dynamic typing system and use of first-class functions. Functions written to accept arguments of a general type can process values of different concrete types at runtime. For example, a function operating on listp data works for lists of any element type.
In Common Lisp, the CLOS object system provides multi-method dispatch using generic functions. Methods can be specialized depending on argument types like classes.
8. Explain how recursion works in Lisp with an example.
Here is an implementation of factorial using recursion in Lisp:
(defun factorial (n) (if (<= n 1) 1 (* n (factorial (- n 1)))))This function calls itself recursively while decrementing n each time until the base case of n <= 1 is reached. Recursion in Lisp allows solving problems by breaking them down into simpler sub-problems.
9. What are Lisp macros and how are they used?
Lisp macros allow extending the language syntax itself at compile time. They work by transforming code into other code forms as data, rather than evaluating their arguments directly. Some common uses of Lisp macros include:
- Defining new domain-specific languages
- Adding new control flow constructs
- Implementing optimized iterative patterns via recursion
- Binding values dynamically
Macros help abstract away repetitive code patterns in Lisp.
10. Explain garbage collection in Lisp.
Lisp uses automatic memory management via garbage collection. The runtime environment periodically scans memory for objects no longer referenced by the program. It releases the memory occupied by such unreachable objects to be used again, avoiding leaks. This frees the programmer from manually allocating and deallocating memory. Lisp typically uses a stop-and-copy approach for garbage collection.
Key Takeaways
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Master the basics of Lisp’s symbolic expression syntax, unique data types like symbols and lists, dynamic typing system, and approach to state management.
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Understand Lisp’s capacities for recursion, higher-order functions, polymorphism, and metaprogramming with macros.
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Be able to discuss Lisp fundamentals like first-class functions, code homoiconicity, and garbage collection.
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Expect Lisp questions in interviews for AI/ML roles given its prominence in those fields.
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Highlight your adaptability as a programmer by demonstrating broad language familiarity spanning both traditional and functional paradigms.
Clojure skills to assess
Question:
Fix the code:
Answer:The code has a syntax error because it references an undeclared symbol c in the add-numbers function. To fix it, you can either remove the c or provide a default value for c. Here are two possible fixes:
Fix 1: Remove the c from the code:
Fix 2: Provide a default value for c:
Question:
What is the purpose of immutability in Clojure?
Answer:Immutability is a fundamental concept in Clojure. It means that once a value is assigned to a variable, it cannot be changed. The purpose of immutability in Clojure is to ensure predictable and reliable code. Because they can’t be changed, immutable data structures get rid of the need for defensive copying and make it easier to figure out what the program is doing. Functional programming paradigms and the use of pure functions are made possible by immutability. Pure functions have no side effects and always give the same output for the same input.
Question:Fix the code:
That’s because the code isn’t written correctly. The map function needs a sequence as its first argument, but numbers is already a sequence, and the * function should be called with two arguments, not a sequence. To fix it, you need to change the code so that the multiplication operation is applied to each number in the sequence. Here’s a possible fix:
In this fixed code, the #(* % factor) is an anonymous function that multiplies each element (%) of the numbers sequence by the factor.
Question:
What is the difference between a vector and a list in Clojure?
Answer: In Clojure, both vectors and lists can be used to show groups of items, but there are some key differences between the two:
- It is structured so that square brackets [] stand for vectors and parentheses () stand for lists.
- Random Access: Using indexes, vectors let you quickly get to elements at random, but lists don’t. It always takes the same amount of time to get an item from a vector by index, but it takes the same amount of time to get an item from a list by index.
- Modification: Vectors are good for adding and updating elements at the end, but they need to be remade with changed parts if you want to change elements at the beginning or middle. Lists, on the other hand, let you make changes quickly at both ends. It is possible to add or remove items from the beginning or end of a list at any time.
- When you change a vector, it creates a new vector while keeping most of the structure of the old one. This is called a persistent data structure. Lists, on the other hand, can’t be changed, so any changes make a new list.
In general, vectors are used for random access or fast appending, while lists are used for operations that need to be done in order and fast changes at both ends.
Question:Fix the code:
Answer:The code is correct in terms of functionality, but it can be simplified. The if statement already gives back a true or false Boolean value, so there’s no need to return true or false in each branch. Here’s a simplified version of the code:
The is-even? function in this fixed code returns the result of the (zero? (rem n 2)) expression, which is a Boolean value that tells you if n is even or not.
Question:
What are the advantages of using a persistent data structure in Clojure?
Answer:Persistent data structures are a key feature of Clojure and offer several advantages:
- Permanent data structures are immutable, which means they can’t be changed after they’ve been created. This immutability makes sure that code is reliable and predictable because it gets rid of the chance of changes being made by accident or shared mutable state.
- Sharing Structures: When a persistent data structure is changed, it lets the new version use as much of its old structure as possible. This sharing makes good use of memory because most of the original structure is used again, and only the changed parts need to be given out.
- Functional Programming: Data structures that don’t change over time work well with functional programming ideas. They tell you to write pure functions that don’t have any side effects and always give the same results for the same inputs. This makes it easier to test and understand the code.
- Efficient Changes: Even though persistent data structures can’t be changed, there are quick and easy ways to make changes to them. Changes don’t change the original structure; instead, they make new versions that share most of their data with the old ones. This method makes sure that most operations can be done quickly and with little memory usage.
- Thread Safety: Immutable persistent data structures are inherently thread-safe. Multiple threads can safely access and use the same data structure without the need for locks or synchronization because the data doesn’t change.
Overall, Clojure’s persistent data structures support a functional programming style, make concurrency easier, and make code that works well and is reliable.
Question:
Answer:The code is almost correct, but it is missing parentheses around the + function. The first thing you should give the apply function is a function, which should be enclosed in parentheses. Here’s the fixed code:
The apply function in this fixed code applies the function to the numbers in the sequence, which adds them up.
Question:
What is the purpose of a macro in Clojure?
Answer: Clojure’s macros are a powerful feature that let programmers add new language constructs and make the syntax of the language longer. Macros operate on the code itself, transforming and generating new code during the compilation phase.
The main goal of macros is to make it easier to abstract and automate code changes that are repeated or complicated, which can’t be done easily with functions. Because they let developers make their own domain-specific language (DSL) or syntactic shortcuts, code is clearer and more expressive.
Macros are evaluated at compile-time and expand into their transformed code before runtime. This makes it possible to write code with loops, conditional statements, and other features that aren’t possible with regular functions.
When Clojure developers use macros, they can write code that is more declarative, expressive, and specific to the problem. Macros provide a mechanism for metaprogramming, allowing developers to shape the language to fit their specific needs.
Question:Fix the code:
Answer:The code has a small issue that can lead to unexpected results. The reverse function returns a sequence, but the apply str expects individual arguments rather than a sequence. To fix it, we need to convert the reversed sequence back into a string. Here’s the fixed code:
In this set of code, the (seq (reverse str)) expression turns the reversed sequence into a string of characters that can be used with apply str to make the reversed string.
Question:
Explain the concept of laziness in Clojure and how it can be beneficial.
Laziness is one of the most important ideas in Clojure. It means not evaluating expressions until their results are needed. Lazy evaluation means that calculations can be put off until they are needed, instead of doing them right away and storing the results.
In Clojure, lazy sequences are one of the key ways laziness is implemented. A lazy sequence is a sequence whose elements are computed on-demand, as they are accessed or requested. The elements are computed one at a time, and only when needed.
The benefits of laziness in Clojure are as follows:
- Efficiency: Since computations are only done when they are needed, lazy evaluation makes better use of memory. It stops computations from being done on elements that aren’t being used, which saves memory and speeds things up.
- Infinite Sequences: Being lazy lets you make and change infinite sequences. Because the parts of a lazy sequence are only computed when they are needed, it is possible to work with sequences that are theoretically endless.
- Composition: It’s easy to put together lazy sequences and other sequences or transformations, which makes it possible to build powerful data processing pipelines. Laziness lets you chain operations together without making any extra data structures, which makes your code shorter and more efficient.
- Control Flow: Laziness makes it easy to write control flow constructs like looping or conditional branching. By using lazy sequences, you can write complicated algorithms and calculations in a clearer and more direct way.
Senior Clojure interview questions
Question:Fix the code:
Answer:The code is correct and functional. It sets up a function called greet that takes a name argument and prints a greeting message with that name.
Question:
Explain the concept of lazy sequences in Clojure and how they differ from eager sequences.
Answer:Lazy sequences in Clojure are sequences that are computed on-demand, as their elements are accessed. Functions like range, filter, and map are used to make them. These functions create sequences that are only evaluated when they are needed. Lazy sequences provide several benefits:
- Use of resources: Lazy sequences make good use of memory and computing power. They compute and create elements as they are accessed, so they don’t have to do the whole sequence of calculations at once.
- Lazy sequences can be used to show infinite sequences because they only create elements as needed, so they don’t use up all of your memory or computing power. This lets you work with collections that could go on forever, like making prime numbers or infinite series.
- Composition: Sequence functions like map, filter, take, and drop make it easy to put together lazy sequences. This lets you write code in a declarative and composable way, where you can change sequences in complex ways without evaluating them right away.
In Clojure, on the other hand, eager sequences are evaluated eagerly, which means that their parts are computed before the sequence is even created. They consume memory and computation resources even if not all elements are accessed.
Question:Fix the code:
Answer:The code is correct and functional. It defines a recursive function called factorial that calculates the factorial of a given number n.
Question:
What do the :pre and :post conditions in Clojure functions do? How do they help with debugging and assertions?
Answer:The :pre and :post conditions in Clojure functions are used for assertions and debugging purposes.
The :pre condition is a pre-condition that is checked before the function body is executed. It allows you to define assertions about the input arguments of a function. If a pre-condition fails, an exception is thrown, indicating that the function was called with invalid arguments.
The :post condition is a post-condition that is checked after the function body is executed. It allows you to define assertions about the return value or the state after the function execution. An exception is thrown if a post-condition fails. This means that the function did not produce the expected result or state.
With :pre and :post conditions, you can add assertions to your functions that help find and report bugs early in the development process. They act as contracts, documenting the expected behavior of the function and providing automatic validation. Bugs can be tracked back to the exact function call where the pre- or post-condition failed, which can help with debugging.
Question:Fix the code:
Answer:The code is correct and functional. It sets up a function called merge-maps that uses the merge function to join a set of maps together.
Question:
Explain the concept of STM (Software Transactional Memory) in Clojure and how it helps with concurrent programming.
Answer:STM, or Software Transactional Memory,
is a concurrency control mechanism provided by Clojure to manage shared state in a concurrent environment. STM helps make sure that everyone has the same and coordinated access to a shared state, which helps avoid problems like deadlocks, race conditions, and states that don’t match up.
In Clojure, STM is implemented using the ref and dosync constructs. The ref function is used to create a reference to a piece of mutable state. Multiple threads can read and modify the state referred to by a ref. A transaction is a set of coordinated operations on one or more ref objects. The dosync block is used to define a transaction.
The key features and benefits of STM in Clojure include:
- Atomicity: In STM, transactions are atomic, which means that they either succeed all at once or are lost. This makes sure that the shared state stays the same even if changes are made at the same time.
- Coordinated Changes: The dosync block in STM lets multiple threads work together to make changes to a shared state. Other threads won’t be able to see changes you make to ref objects during a transaction until the transaction commits.
- In the event that a conflict is found during the execution of a transaction (e.g. g. because other threads are making changes at the same time, the transaction is automatically rolled back and tried again. This makes sure that changes that don’t work together are fixed and that the same results are reached every time.
- Consistency and Isolation: STM gives each transaction a consistent and separate view of the shared state. It makes sure that each transaction sees the same copy of the shared state, as if they were run one after the other.
Clojure’s STM lets programs that run at the same time safely manage shared state without using explicit locks or synchronization. STM simplifies concurrent programming by providing a higher-level abstraction that handles the complexities of coordination and synchronization automatically.
Question:Fix the code:
Answer:The code is correct and functional. It sets up a function called calculate-sum that takes a list of numbers and adds them up using the reduce function, the operator as the combining function, and 0 as the starting value.
Question:
Explain the purpose and advantages of using persistent data structures in Clojure.
Answer:Persistent data structures are a fundamental concept in Clojure that provide efficient immutability and structural sharing. They are designed to support efficient updates while maintaining the benefits of immutability.
The purpose and advantages of using persistent data structures in Clojure include:
- Clojure’s persistent data structures are immutable, which means they can’t be changed after they’ve been created. Any change made to a persistent data structure creates a new version that is returned, while the original data structure stays the same. This makes it easier to understand the code, makes concurrent programming safer, and makes it easier to undo or redo changes or go back in time to fix bugs.
- Efficiency: Clojure’s persistent data structures use structural sharing to make updates quick and easy. When changes are made to a persistent data structure, most of the original structure is used again. This saves memory and cuts down on the cost of making copies or new versions. Because of this, persistent data structures work well for many tasks, such as updates, lookups, and traversals.
- If you use functional programming, persistent data structures work well with this style of programming. They are great for displaying and changing data in a declarative and functional way. As a result of persistent data structures, programming styles that focus on immutability, pure functions, and transformations become more popular. This makes code easier to understand, test, and write.
- Performance: Many common operations can be done quickly and efficiently with persistent data structures. They make updates, lookups, and additions very efficient in terms of time and space. The structural sharing mechanism makes sure that only the important parts of the data structure are copied or changed. This saves time and effort compared to copying or changing the whole structure.
By leveraging persistent data structures, Clojure allows developers to write expressive, immutable, and high-performance code. Persistent data structures are a big part of Clojure’s success as a language for building strong and scalable apps because they can’t be changed, can share structures, and can be updated quickly.
Question:Fix the code:
Answer:The code is correct and functional. It sets up a function called reverse-string that takes a string s, turns it around using the reverse function, and then joins the characters together using apply and str.
Question:
Explain the concept of multimethods in Clojure and how they differ from regular functions.
Answer:Multimethods in Clojure are a powerful mechanism for polymorphism and dynamic dispatch. They allow different behaviors to be associated with different combinations of arguments, enabling flexible and extensible code.
The key points about multimethods in Clojure are:
- Dispatch: Multimethods are defined based on a dispatch function. This dispatch function looks at the arguments given to the multimethod and sends back a dispatch value that tells the multimethod which specific method implementation to call. The dispatch value can be any Clojure data type.
- Method Implementation: A multimethod can have more than one method implementation linked to it. You can define each method’s implementation with the defmethod macro. This macro tells you the multimethod, the dispatch value(s) it handles, and the implementation logic.
- Dynamic Dispatch: Multimethods offer dynamic dispatch based on the dispatch value, while regular functions have static dispatch based on the function name. These words mean that the arguments passed determine at runtime which method implementation to use.
- Flexibility: Multimethods help with flexibility by letting new method implementations be added to multimethods that are already in use or by defining brand-new multimethods. This makes it easy to add new types of arguments or new combinations of arguments to code without changing the code that is already there.
Compared to regular functions, multimethods provide a more flexible and dynamic approach to polymorphism. For regular functions, dispatch is based on the name of the function, but for multimethods, it is based on the dispatch value(s) returned by the dispatch function. Multimethods make it possible to link different behaviors to different sets of arguments. This is a powerful way to make code that is flexible and can be added to.
Lisp programming Interview Questions and Answers 2019 Part-1 | Lisp programming | Wisdoomjobs
FAQ
What is Lisp programming used for?
Is Lisp still used for AI?
Is Lisp hard to learn?
What are the problems with Lisp programming language?
Is Lisp a function?
Every Lisp procedure is a function, and when called, it returns a data object as its value. It is also commonly referred to as “functions” even though they may have side effects. Lisp is the second-oldest high-level programming language in the world which is invented by John McCarthy in the year 1958 at the Massachusetts Institute of Technology.
Why is Lisp a good programming language?
Lisp is the second-oldest high-level programming language in the world which is invented by John McCarthy in the year 1958 at the Massachusetts Institute of Technology. It allows us to create and update the programs and applications dynamically. It provides high-level debugging. It supports object-oriented programming.
How do I learn Lisp programming?
Learn Lisp Programming with Free Books 1 COMMON LISP: An Interactive Approach. 2 Common Lisp: A Gentle Introduction to Symbolic Computation. 3 Performance and Evaluation of Lisp Systems. 4 Structure and Interpretation of Computer Programs. 5 Loving Common Lisp, or the Savvy Programmer’s Secret Weapon. 6 Lisp Web Tales.
What are the features of Lisp programming?
It supports object-oriented programming. It supports all kinds of data types like objects, structures, lists, vectors, adjustable arrays, set, trees,hash-tables, and symbols. It will also support different iterating statements like do, loop,loopfor, dotimes and dolist. These are the features of LISP Programming.
