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Numpy Online Test

The NumPy online test uses multiple-choice questions to evaluate a candidate's knowledge and skills related to NumPy arrays and operations, indexing and slicing, linear algebra and statistics, broadcasting, ufuncs and vectorization, and data input and output. The test aims to assess the candidate's proficiency in NumPy and their ability to apply numerical computing and data analysis techniques using Python.

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Screen candidates with a 40 mins test

Test duration:  ~ 40 mins
Difficulty level:  Moderate
Availability:  Available as custom test
Questions:
  • 8 NumPy MCQs
  • 7 Python MCQs
Covered skills:
Array Creation
Array Indexing and Slicing
Array Operations
Math Functions
Linear Algebra
File Handling
Broadcasting
Performance Optimization
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Use the Numpy Test to shortlist qualified candidates

The Numpy Online Test helps recruiters and hiring managers identify qualified candidates from a pool of resumes, and helps in taking objective hiring decisions. It reduces the administrative overhead of interviewing too many candidates and saves time by filtering out unqualified candidates at the first step of the hiring process.

The test screens for the following skills that hiring managers look for in candidates:

  • Able to efficiently create and manipulate arrays using numpy
  • Thorough understanding of array indexing and slicing concepts
  • Proficient in performing various array operations
  • Comfortable working with math functions and performing mathematical computations using numpy
  • Strong grasp of linear algebra concepts and applications in numpy
  • Familiarity with file handling and reading/writing data using numpy
  • Understanding of broadcasting in numpy and its benefits
  • Able to optimize performance and improve execution speed of numpy computations
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Screen candidates with the highest quality questions

We have a very high focus on the quality of questions that test for on-the-job skills. Every question is non-googleable and we have a very high bar for the level of subject matter experts we onboard to create these questions. We have crawlers to check if any of the questions are leaked online. If/ when a question gets leaked, we get an alert. We change the question for you & let you know.

How we design questions

These are just a small sample from our library of 15,000+ questions. The actual questions on this Online NumPy Test will be non-googleable.

🧐 Question

Medium

Array Manipulation and Summation
Array Manipulation
Mathematical Operations
Solve
Consider the following code snippet:
 image
What will be the value of G after executing the code?

Medium

Matrix Eigenvalues and Diagonalization
Linear Algebra
Matrix Operations
Solve
Consider the following code snippet:
 image
After running this code, which of the following statements is true regarding the B matrix?

Medium

ZeroDivisionError and IndexError
Exceptions
Solve
What will the following Python code output?
 image

Medium

Session
File Handling
Dictionary
Solve
 image
The function high_sess should compute the highest number of events per session of each user in the database by reading a comma-separated value input file of session data. The result should be returned from the function as a dictionary. The first column of each line in the input file is expected to contain the user’s name represented as a string. The second column is expected to contain an integer representing the events in a session. Here is an example input file:
Tony,10
Stark,12
Black,25
Your program should ignore a non-conforming line like this one.
Stark,3
Widow,6
Widow,14
The resulting return value for this file should be the following dictionary: { 'Stark':12, 'Black':25, 'Tony':10, 'Widow':14 }
What should replace the CODE TO FILL line to complete the function?
 image

Medium

Max Code
Arrays
Solve
Below are code lines to create a Python function. Ignoring indentation, what lines should be used and in what order for the following function to be complete:
 image

Medium

Recursive Function
Recursion
Dictionary
Lists
Solve
Consider the following Python code:
 image
In the above code, recursive_search is a function that takes a dictionary (data) and a target key (target) as arguments. It searches for the target key within the dictionary, which could potentially have nested dictionaries and lists as values, and returns the value associated with the target key. If the target key is not found, it returns None.

nested_dict is a dictionary that contains multiple levels of nested dictionaries and lists. The recursive_search function is then called with nested_dict as the data and 'target_key' as the target.

What will the output be after executing the above code?

Medium

Stacking problem
Stack
Linkedlist
Solve
What does the below function ‘fun’ does?
 image
A: Sum of digits of the number passed to fun.
B: Number of digits of the number passed to fun.
C: 0 if the number passed to fun is divisible by 10. 1 otherwise.
D: Sum of all digits number passed to fun except for the last digit.
🧐 Question🔧 Skill

Medium

Array Manipulation and Summation
Array Manipulation
Mathematical Operations

2 mins

NumPy
Solve

Medium

Matrix Eigenvalues and Diagonalization
Linear Algebra
Matrix Operations

3 mins

NumPy
Solve

Medium

ZeroDivisionError and IndexError
Exceptions

2 mins

Python
Solve

Medium

Session
File Handling
Dictionary

2 mins

Python
Solve

Medium

Max Code
Arrays

2 mins

Python
Solve

Medium

Recursive Function
Recursion
Dictionary
Lists

3 mins

Python
Solve

Medium

Stacking problem
Stack
Linkedlist

4 mins

Python
Solve
🧐 Question🔧 Skill💪 Difficulty⌛ Time
Array Manipulation and Summation
Array Manipulation
Mathematical Operations
NumPy
Medium2 mins
Solve
Matrix Eigenvalues and Diagonalization
Linear Algebra
Matrix Operations
NumPy
Medium3 mins
Solve
ZeroDivisionError and IndexError
Exceptions
Python
Medium2 mins
Solve
Session
File Handling
Dictionary
Python
Medium2 mins
Solve
Max Code
Arrays
Python
Medium2 mins
Solve
Recursive Function
Recursion
Dictionary
Lists
Python
Medium3 mins
Solve
Stacking problem
Stack
Linkedlist
Python
Medium4 mins
Solve

Test candidates on core Numpy Hiring Test topics

Array Creation: Array creation refers to the process of initializing arrays with data. It includes functions such as np.array(), np.zeros(), np.ones(), and np.arange() that allow users to create arrays of desired shapes and fill them with specific values. This skill is measured in the test to assess the candidate's ability to create and manipulate arrays efficiently, which is essential in many data manipulation and analysis tasks.

Array Indexing and Slicing: Array indexing and slicing involve accessing and extracting specific elements or portions of an array. The numpy library provides various indexing techniques like integer array indexing, Boolean array indexing, and advanced indexing using integers or arrays of indices. Assessing this skill in the test helps evaluate the candidate's proficiency in extracting and manipulating array data based on specific requirements.

Array Operations: Array operations refer to mathematical and logical operations performed on arrays, such as addition, subtraction, multiplication, division, exponentiation, and comparisons. These operations can be performed element-wise or matrix-wise, allowing for efficient computation on large datasets. Including this skill in the test helps measure a candidate's ability to perform basic array operations, which are fundamental in data analysis and scientific computing.

Math Functions: Math functions in numpy include various mathematical operations like trigonometric functions, logarithmic functions, exponential functions, and statistical functions. These functions allow for efficient computation and manipulation of numerical data in arrays. Evaluating this skill in the test helps assess a candidate's understanding and application of mathematical functions for data processing and analysis.

Linear Algebra: Linear Algebra in numpy involves operations related to vectors, matrices, and linear equations. It includes functions for matrix multiplication, matrix inversion, finding eigenvalues and eigenvectors, solving linear equations, and performing matrix decompositions. Measuring this skill in the test helps determine a candidate's knowledge and proficiency in essential linear algebra operations used in various fields such as machine learning and scientific computing.

File Handling: File handling in numpy refers to the ability to read and write array data from and to external files. Numpy provides functions like np.loadtxt() and np.savetxt() that enable reading and writing arrays in different file formats. Assessing this skill in the test helps evaluate a candidate's capability to handle data stored in files, which is crucial in real-world data processing and analysis tasks.

Broadcasting: Broadcasting is a powerful numpy feature that allows arithmetic operations to be performed between arrays of different shapes. It eliminates the need for explicit loops and enables efficient computation with arrays of different sizes. Testing this skill helps measure a candidate's understanding and usage of broadcasting, which is essential to avoid unnecessary code complexity and improve computational efficiency.

Performance Optimization: Performance optimization in numpy involves implementing techniques to improve the efficiency and speed of computations. This may include vectorizing operations, using optimized numpy functions, using appropriate data types, and employing algorithms tailored for performance. Evaluating this skill in the test helps determine a candidate's ability to optimize code execution, which is crucial when dealing with large datasets or computationally intensive tasks.

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Have questions about the Numpy Hiring Test?

How does pricing work?

You can check out our pricing plans.

Can I customize the test?

Yes, absolutely. Custom assessments are set up within 48 hours based on your job description, and will include questions on all must-have skills you specify. Here's a quick guide on how you can request a custom test. You can also customize a test by uploading your own questions.

Can I combine multiple skills into one test?

Yes, absolutely. Custom assessments are set up based on your job description, and will include questions on all must-have skills you specify. Here's a quick guide on how you can request a custom test.

What roles can I use the Numpy Test for?

Here are few roles for which we recommend this test:

  • Python Developer
  • NumPy Developer
  • Data Analyst
  • Data Scientist
  • Machine Learning Engineer
Can I see a sample test, or do you have a free trial?

Yes!

The free trial includes one sample technical test (Java/ JavaScript) and one sample aptitude test that you will find in your dashboard when you sign up. You can use it to review the quality of questions and the candidate experience of giving a test on Adaface.

You can preview any of the 500+ tests and see the sample questions to decide if it would be a good fit for your requirements.

How do I interpret test scores?

The primary thing to keep in mind is that an assessment is an elimination tool, not a selection tool. A skills assessment is optimized to help you eliminate candidates who are not technically qualified for the role, it is not optimized to help you find the best candidate for the role. So the ideal way to use an assessment is to decide a threshold score (typically 55%, we help you benchmark) and invite all candidates who score above the threshold for the next rounds of interview.

I'm a candidate. Can I try a practice test?

No. Unfortunately, we do not support practice tests at the moment. However, you can use our sample questions for practice.

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