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PyTorch 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.

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Screen candidates with a 30 mins test

Test duration:  ~ 30 mins
Difficulty level:  Moderate
Availability:  Available as custom test
Questions:
  • 4 Python MCQs
  • 6 PyTorch MCQs
  • 4 Data Science MCQs
Covered skills:
PyTorch Tensors
Datasets and DataLoaders in PyTorch
Transforms in PyTorch
Building Models with PyTorch
Optimzing Model Parameters with PyTorch
Data Science Fundamentals
Python Basics
Programming in Python
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Use the PyTorch Assessment Test to shortlist qualified candidates

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
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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 PyTorch Test will be non-googleable.

🧐 Question

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.

Medium

Amazon electronics product feedback
Solve
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
Solve
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
Solve
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
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

Medium

Amazon electronics product feedback

2 mins

Data Science
Solve

Easy

Fraud detection model
Logistic Regression

2 mins

Data Science
Solve

Medium

Rox's decision tree classifier
Decision Tree Classifier

2 mins

Data Science
Solve
🧐 Question🔧 Skill💪 Difficulty⌛ Time
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
Amazon electronics product feedback
Data Science
Medium2 mins
Solve
Fraud detection model
Logistic Regression
Data Science
Easy2 mins
Solve
Rox's decision tree classifier
Decision Tree Classifier
Data Science
Medium2 mins
Solve

Test candidates on core PyTorch Hiring Test topics

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.

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Have questions about the PyTorch Hiring Test?

What roles can I use the PyTorch Assessment Test for?

Here are few roles for which we recommend this test:

  • Data Scientist
  • Machine Learning Engineer
  • Deep Learning Engineer
  • Data Analyst
  • Python Developer
  • Software Engineer
  • Research Scientist
  • Artificial Intelligence Engineer
  • Data Engineer
  • Data Architect
Can I combine PyTorch Test with Data Science questions?

Yes, you can request a custom test that includes both PyTorch and Data Science questions. For more details on assessing Data Science skills, check out our Data Science Test.

How to use PyTorch Test in my hiring process?

Use the PyTorch Test as a pre-screening tool to streamline your recruitment. Add a link to the assessment in your job post or invite candidates directly by email. This helps you find skilled candidates early.

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.

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.

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.

What is the cost of using this test?

You can check out our pricing plans.

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.

What is PyTorch Test?

The PyTorch Test is designed to assess a candidate's proficiency in using the PyTorch library for machine learning. It is used by recruiters to identify individuals who are well-versed in PyTorch, Python, and data science fundamentals.

What skills are evaluated in the PyTorch Test?

The PyTorch Test covers PyTorch Tensors, Datasets and DataLoaders, Transforms, Building Models, Optimizing Model Parameters, Data Science Fundamentals, Python Basics, and Programming in Python. Senior roles are also assessed for advanced skills like implementing ML algorithms and evaluating models.

What are the main data science tests?
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.

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.

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.

Can I get a free trial?

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

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