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About the test:

The PyTorch Test evaluates a candidate's knowledge and skills in PyTorch, a popular deep learning framework. It assesses their understanding of data science, deep learning, machine learning, Python, Python Pandas, Python Linux, Numpy, and data structures.

Covered skills:

  • PyTorch Tensors
  • Transforms in PyTorch
  • Optimzing Model Parameters with PyTorch
  • Python Basics
  • Datasets and DataLoaders in PyTorch
  • Building Models with PyTorch
  • Data Science Fundamentals
  • Programming in Python

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9 reasons why
9 reasons why

Adaface PyTorch Assessment Test is the most accurate way to shortlist Data Scientists



Reason #1

Tests for on-the-job skills

The PyTorch 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:

  • Understanding and working with PyTorch Tensors
  • Creating and utilizing Datasets and DataLoaders in PyTorch
  • Applying Transforms in PyTorch
  • Building models with PyTorch
  • Optimizing model parameters with PyTorch
  • Implementing Data Science fundamentals
  • Demonstrating proficiency in Python basics
  • Effective programming in Python
Reason #2

No trick questions

no trick questions

Traditional assessment tools use trick questions and puzzles for the screening, which creates a lot of frustration among candidates about having to go through irrelevant screening assessments.

View sample questions

The main reason we started Adaface is that traditional pre-employment assessment platforms are not a fair way for companies to evaluate candidates. At Adaface, our mission is to help companies find great candidates by assessing on-the-job skills required for a role.

Why we started Adaface
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Reason #3

Non-googleable 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 10,000+ questions. The actual questions on this PyTorch Test will be non-googleable.

🧐 Question

Medium

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

Medium

Session
File Handling
Dictionary
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 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
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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
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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
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What does the below function ‘fun’ does?
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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.

Medium

Amazon electronics product feedback
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Amazon's electronics store division has over the last few months focused on getting customer feedback on their products, and marking them as safe/ unsafe. Their data science team has used decision trees for this. 
The training set has these features: product ID, data, summary of feedback, detailed feedback and a binary safe/unsafe tag. During training, the data science team dropped any feedback records with missing features. The test set has a few records with missing "detailed feedback" field. What would you recommend?
A: Remove the test samples with missing detailed feedback text fields
B: Generate synthetic data to fill in missing fields
C: Use an algorithm that handles missing data better than decision trees
D: Fill in the missing detailed feedback text field with the summary of feedback field.

Easy

Fraud detection model
Logistic Regression
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Your friend T-Rex is working on a logistic regression model for a bank, for a fraud detection usecase. The accuracy of the model is 98%. T-Rex's manager's concern is that 85% of fraud cases are not being recognized by the model. Which of the following will surely help the model recognize more than 15% of fraud cases?

Medium

Rox's decision tree classifier
Decision Tree Classifier
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Your data science intern Rox was asked to create a decision tree classifier with 12 input variables. The tree used 7 of the 12 variables, and was 5 levels deep. Few nodes of the tree contain 3 data points. The area under the curve (AUC) is 0.86. As Rox's mentor, what is your interpretation?
A. The AUC is high, and the small nodes are all very pure- the model looks accurate.
B. The tree might be overfitting- try fitting shallower trees and using an ensemble method.
C. The AUC is high, so overall the model is accurate. It might not be well-calibrated, because the small nodes will give poor estimates of probability.
D. The tree did not split on all the input variables. We need a larger data set to get a more accurate model.
🧐 Question🔧 Skill

Medium

ZeroDivisionError and IndexError
Exceptions

2 mins

Python
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Medium

Session
File Handling
Dictionary

2 mins

Python
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Medium

Max Code
Arrays

2 mins

Python
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Medium

Recursive Function
Recursion
Dictionary
Lists

3 mins

Python
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Medium

Stacking problem
Stack
Linkedlist

4 mins

Python
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Medium

Amazon electronics product feedback

2 mins

Data Science
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Easy

Fraud detection model
Logistic Regression

2 mins

Data Science
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Medium

Rox's decision tree classifier
Decision Tree Classifier

2 mins

Data Science
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🧐 Question🔧 Skill💪 Difficulty⌛ Time
ZeroDivisionError and IndexError
Exceptions
Python
Medium2 mins
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Session
File Handling
Dictionary
Python
Medium2 mins
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Max Code
Arrays
Python
Medium2 mins
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Recursive Function
Recursion
Dictionary
Lists
Python
Medium3 mins
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Stacking problem
Stack
Linkedlist
Python
Medium4 mins
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Amazon electronics product feedback
Data Science
Medium2 mins
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Fraud detection model
Logistic Regression
Data Science
Easy2 mins
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Rox's decision tree classifier
Decision Tree Classifier
Data Science
Medium2 mins
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Reason #4

1200+ customers in 75 countries

customers in 75 countries
Brandon

With Adaface, we were able to optimise our initial screening process by upwards of 75%, freeing up precious time for both hiring managers and our talent acquisition team alike!


Brandon Lee, Head of People, Love, Bonito

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Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment PyTorch Test in your hiring process is that it is an elimination tool, not a selection tool. In other words: you want to use the test to eliminate the candidates who do poorly on the test, not to select the candidates who come out at the top. While they are super valuable, pre-employment tests do not paint the entire picture of a candidate’s abilities, knowledge, and motivations. Multiple easy questions are more predictive of a candidate's ability than fewer hard questions. Harder questions are often "trick" based questions, which do not provide any meaningful signal about the candidate's skillset.

Science behind Adaface tests
Reason #6

1 click candidate invites

Email invites: You can send candidates an email invite to the PyTorch Test from your dashboard by entering their email address.

Public link: You can create a public link for each test that you can share with candidates.

API or integrations: You can invite candidates directly from your ATS by using our pre-built integrations with popular ATS systems or building a custom integration with your in-house ATS.

invite candidates
Reason #7

Detailed scorecards & benchmarks

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Reason #8

High completion rate

Adaface tests are conversational, low-stress, and take just 25-40 mins to complete.

This is why Adaface has the highest test-completion rate (86%), which is more than 2x better than traditional assessments.

test completion rate
Reason #9

Advanced Proctoring


Learn more

About the PyTorch Online Test

Why you should use Pre-employment PyTorch Test?

The PyTorch Test makes use of scenario-based questions to test for on-the-job skills as opposed to theoretical knowledge, ensuring that candidates who do well on this screening test have the relavant skills. The questions are designed to covered following on-the-job aspects:

  • Creating and manipulating PyTorch Tensors
  • Using Datasets and DataLoaders in PyTorch
  • Applying Transforms in PyTorch
  • Building Models with PyTorch
  • Optimizing Model Parameters with PyTorch
  • Understanding Data Science Fundamentals
  • Python Basics and Syntax
  • Programming in Python
  • Working with Python Packages and Libraries
  • Data Manipulation and Analysis in Python

Once the test is sent to a candidate, the candidate receives a link in email to take the test. For each candidate, you will receive a detailed report with skills breakdown and benchmarks to shortlist the top candidates from your pool.

What topics are covered in the PyTorch Test?

  • PyTorch Tensors

    PyTorch Tensors are powerful multidimensional arrays used for efficient computation and storage of numerical data. They provide a flexible and convenient way to represent and manipulate data in PyTorch, making it an essential skill to measure in this test.

  • Datasets and DataLoaders in PyTorch

    Datasets and DataLoaders in PyTorch allow for efficient handling and processing of large-scale datasets. These components enable easy data loading, transformation, and batching, which are crucial for training and evaluating machine learning models.

  • Transforms in PyTorch

    Transforms in PyTorch provide a set of operations to preprocess and augment data. They enable tasks such as resizing, cropping, and normalizing data, enhancing the quality and variety of input for models. Testing expertise in PyTorch Transforms is important to ensure robust and effective data preparation.

  • Building Models with PyTorch

    Building models with PyTorch involves using its powerful tools and APIs to define and customize neural network architectures. This skill is crucial for designing models tailored to specific tasks, enabling flexibility and innovation in machine learning applications.

  • Optimizing Model Parameters with PyTorch

    Optimizing model parameters with PyTorch involves using techniques like backpropagation and gradient descent to efficiently update and optimize model weights. This skill is essential for improving model performance and achieving higher accuracy in machine learning tasks.

  • Data Science Fundamentals

    Data Science Fundamentals encompass a broad range of concepts and techniques used in analyzing and interpreting data. Measuring this skill ensures that a candidate has the foundational knowledge required for effectively working with data and making informed decisions.

  • Python Basics

    Python Basics include essential programming concepts and syntax in Python. Measuring this skill ensures that a candidate has the necessary knowledge to write and understand Python code, which is widely used in data analysis and machine learning.

  • Programming in Python

    Programming in Python involves applying Python language skills to solve real-world problems. This skill measures a candidate's proficiency in implementing algorithms, writing efficient code, and dealing with various data structures, all of which are important in the context of developing and deploying machine learning models.

  • Full list of covered topics

    The actual topics of the questions in the final test will depend on your job description and requirements. However, here's a list of topics you can expect the questions for PyTorch Test to be based on.

    PyTorch Tensors
    PyTorch autograd
    PyTorch forward and backward propagation
    PyTorch model training
    PyTorch loss functions
    PyTorch activation functions
    PyTorch optimizers
    PyTorch data loading
    PyTorch data augmentation
    PyTorch data preprocessing
    PyTorch dataset splitting
    PyTorch model architecture
    PyTorch model evaluation
    PyTorch hyperparameter tuning
    Data science principles
    Statistical analysis
    Data visualization
    Machine learning algorithms
    Python syntax and data types
    Conditional statements
    Loops and iteration
    Functions and modules
    File handling
    Object-oriented programming
    Exception handling
    Data structures
    Basic SQL and database interactions
    Regular expressions
    Debugging techniques
    Code optimization
    Documentation and commenting
    Unit testing in Python
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What roles can I use the PyTorch Test for?

  • Data Scientist
  • Machine Learning Engineer
  • Deep Learning Engineer
  • Data Analyst
  • Python Developer
  • Software Engineer
  • Research Scientist
  • Artificial Intelligence Engineer
  • Data Engineer
  • Data Architect

How is the PyTorch Test customized for senior candidates?

For intermediate/ experienced candidates, we customize the assessment questions to include advanced topics and increase the difficulty level of the questions. This might include adding questions on topics like

  • Implementing Machine Learning Algorithms with PyTorch
  • Evaluating Machine Learning Models using PyTorch
  • Understanding Neural Networks and Deep Learning
  • Applying Techniques for Neural Network Training
  • Implementing Convolutional Neural Networks (CNNs)
  • Working with Recurrent Neural Networks (RNNs)
  • Utilizing Transfer Learning in PyTorch
  • Implementing Natural Language Processing (NLP) with PyTorch
  • Applying Computer Vision Techniques with PyTorch
  • Implementing Reinforcement Learning Algorithms with PyTorch
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The hiring managers felt that through the technical questions that they asked during the panel interviews, they were able to tell which candidates had better scores, and differentiated with those who did not score as well. They are highly satisfied with the quality of candidates shortlisted with the Adaface screening.


85%
reduction in screening time

PyTorch Hiring Test FAQs

Can I combine multiple skills into one custom assessment?

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.

Do you have any anti-cheating or proctoring features in place?

We have the following anti-cheating features in place:

  • Non-googleable questions
  • IP proctoring
  • Screen proctoring
  • Web proctoring
  • Webcam proctoring
  • Plagiarism detection
  • Secure browser
  • Copy paste protection

Read more about the proctoring features.

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.

What experience level can I use this test for?

Each Adaface assessment is customized to your job description/ ideal candidate persona (our subject matter experts will pick the right questions for your assessment from our library of 10000+ questions). This assessment can be customized for any experience level.

Does every candidate get the same questions?

Yes, it makes it much easier for you to compare candidates. Options for MCQ questions and the order of questions are randomized. We have anti-cheating/ proctoring features in place. In our enterprise plan, we also have the option to create multiple versions of the same assessment with questions of similar difficulty levels.

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.

What is the cost of using this test?

You can check out our pricing plans.

Can I get a free trial?

Yes, you can sign up for free and preview this test.

I just moved to a paid plan. How can I request a custom assessment?

Here is a quick guide on how to request a custom assessment on Adaface.

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