Why you should use PyTorch Test?

The Prueba de pytorch 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: Creación y manipulación de tensores de pytorch Uso de conjuntos de datos y dataloaders en Pytorch Aplicar transformaciones en Pytorch Construir modelos con pytorch Optimización de parámetros del modelo con pytorch Comprender los fundamentos de la ciencia de datos Python conceptos básicos y sintaxis Programación en Python Trabajar con paquetes y bibliotecas de Python Manipulación y análisis de datos en Python

PyTorch Test

Q: What topics are covered in the PyTorch Test?

Tensores de pytorch Conjuntos de datos y dataloaders en Pytorch Transformaciones en pytorch Construir modelos con pytorch Parámetros del modelo de optimización con pytorch Fundamentos de ciencia de datos Python conceptos básicos Programación en Python

About the test:

La prueba de Pytorch evalúa los conocimientos y habilidades de un candidato en Pytorch, un marco popular de aprendizaje profundo. Evalúa su comprensión de la ciencia de datos, el aprendizaje profundo, el aprendizaje automático, Python, Python Pandas, Python Linux, Numpy y estructuras de datos.

Covered skills:

Tensores de pytorch
Transformaciones en pytorch
Parámetros del modelo de optimización con pytorch
Python conceptos básicos

Conjuntos de datos y dataloaders en Pytorch
Construir modelos con pytorch
Fundamentos de ciencia de datos
Programación en Python

Preview this test

Ver cuadro de mando de muestra

9 reasons why

Adaface PyTorch Assessment Test is the most accurate way to shortlist Científico de datoss

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:

Comprender y trabajar con Pytorch Tensors
Creación y utilización de conjuntos de datos y dataloaders en Pytorch
Aplicar transformaciones en Pytorch
Construir modelos con pytorch
Optimización de parámetros del modelo con pytorch
Implementación de los fundamentos de la ciencia de datos
Demostrando el dominio de los conceptos básicos de Python
Programación efectiva en Python

Reason #2

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

Preview this test

Ver cuadro de mando de muestra

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

Estas son solo una pequeña muestra de nuestra biblioteca de más de 10,000 preguntas. Las preguntas reales sobre esto Prueba de pytorch no se puede obtener.

🧐 Question
Medium ZeroDivisionError and IndexError Exceptions	Solve
What will the following Python code output?
Medium Session File Handling Dictionary	Solve
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?
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:
Medium Recursive Function Recursion Dictionary Lists	Solve
Consider the following Python code: 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? 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
What will the following Python code output?
	Medium Session File Handling Dictionary	2 mins Python	Solve
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?
	Medium Max Code Arrays	2 mins Python	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:
	Medium Recursive Function Recursion Dictionary Lists	3 mins Python	Solve
Consider the following Python code: 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	4 mins Python	Solve
What does the below function ‘fun’ does? 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	2 mins Data Science	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	2 mins Data Science	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	2 mins Data Science	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	💪 Difficulty	⌛ Time
	ZeroDivisionError and IndexError Exceptions	Python	Medium	2 mins	Solve
What will the following Python code output?
	Session File Handling Dictionary	Python	Medium	2 mins	Solve
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?
	Max Code Arrays	Python	Medium	2 mins	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:
	Recursive Function Recursion Dictionary Lists	Python	Medium	3 mins	Solve
Consider the following Python code: 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?
	Stacking problem Stack Linkedlist	Python	Medium	4 mins	Solve
What does the below function ‘fun’ does? 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.
	Amazon electronics product feedback	Data Science	Medium	2 mins	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.
	Fraud detection model Logistic Regression	Data Science	Easy	2 mins	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?
	Rox's decision tree classifier Decision Tree Classifier	Data Science	Medium	2 mins	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.

Reason #4

1200+ customers in 75 countries

Con Adaface, pudimos optimizar nuestro proceso de selección inicial en más de un 75 %, liberando un tiempo precioso tanto para los gerentes de contratación como para nuestro equipo de adquisición de talentos.

Brandon Lee, jefe de personas, Love, Bonito

Preview this test

Ver cuadro de mando de muestra

Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment Prueba de pytorch 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 Prueba de pytorch 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.

Reason #7

Detailed scorecards & benchmarks

Ver cuadro de mando de muestra

Preview this test

Ver cuadro de mando de muestra

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.

Reason #9

Advanced Proctoring

Learn more

Preview this test

Ver cuadro de mando de muestra

About the PyTorch Online Test

Why you should use Pre-employment PyTorch Test?

The Prueba de pytorch 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:

Creación y manipulación de tensores de pytorch
Uso de conjuntos de datos y dataloaders en Pytorch
Aplicar transformaciones en Pytorch
Construir modelos con pytorch
Optimización de parámetros del modelo con pytorch
Comprender los fundamentos de la ciencia de datos
Python conceptos básicos y sintaxis
Programación en Python
Trabajar con paquetes y bibliotecas de Python
Manipulación y análisis de datos en 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?

tensores de pytorch
Los tensores de pytorch son potentes matrices multidimensionales utilizadas para un cálculo eficiente y el almacenamiento de datos numéricos. Proporcionan una forma flexible y conveniente de representar y manipular datos en Pytorch, por lo que es una habilidad esencial para medir en esta prueba.
conjuntos de datos y dataloaders en Pytorch
conjuntos de datos y dataloaders en Pytorch Permitir un manejo y procesamiento eficientes de conjuntos de datos a gran escala. Estos componentes permiten una fácil carga de datos, transformación y lotes, que son cruciales para capacitar y evaluar modelos de aprendizaje automático.
Se transforma en Pytorch
Las transformaciones en PyTorch proporcionan un conjunto de operaciones para el preprocesamiento y aumentar los datos. Habilitan tareas como cambiar el tamaño, el recorte y la normalización de los datos, mejorando la calidad y la variedad de entrada para los modelos. Prueba de experiencia en transformaciones de pytorch es importante para garantizar una preparación de datos robusta y efectiva.
Construir modelos con Pytorch
Los modelos de construcción con Pytorch implica el uso de sus potentes herramientas y API para definir y personalizar la red neuronal arquitecturas. Esta habilidad es crucial para diseñar modelos adaptados a tareas específicas, permitiendo flexibilidad e innovación en aplicaciones de aprendizaje automático.
Optimización de parámetros del modelo con Pytorch
Optimización de parámetros del modelo con PyTorch implica usar técnicas como backpropagationation y descenso de gradiente para actualizar y optimizar eficientemente los pesos del modelo. Esta habilidad es esencial para mejorar el rendimiento del modelo y lograr una mayor precisión en las tareas de aprendizaje automático.
Fundamentos de ciencia de datos
Los fundamentos de la ciencia de datos abarcan una amplia gama de conceptos y técnicas utilizadas para analizar e interpretar datos. La medición de esta habilidad garantiza que un candidato tenga el conocimiento fundamental requerido para trabajar de manera efectiva con los datos y tomar decisiones informadas.
Python Basics
Python Basics incluye conceptos de programación esenciales y sintaxis en Python. La medición de esta habilidad garantiza que un candidato tenga el conocimiento necesario para escribir y comprender el código de Python, que se usa ampliamente en el análisis de datos y el aprendizaje automático.
La programación en Python
en Python implica Aplicación de habilidades lingüísticas de Python para resolver problemas del mundo real. Esta habilidad mide la competencia de un candidato en la implementación de algoritmos, la redacción de código eficiente y el tratamiento de diversas estructuras de datos, todas las cuales son importantes en el contexto de desarrollar e implementar modelos de aprendizaje automático.
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 Prueba de pytorch to be based on.
Tensores de pytorch
Pytorch autogrado
Propagación hacia adelante y hacia atrás de Pytorch
Entrenamiento modelo de pytorch
Funciones de pérdida de Pytorch
Funciones de activación de Pytorch
Optimizadores de pytorch
Carga de datos de Pytorch
Aumento de datos de Pytorch
Preprocesamiento de datos de Pytorch
División del conjunto de datos de Pytorch
Arquitectura modelo de pytorch
Evaluación del modelo de Pytorch
Tuning de hiperparámetro de Pytorch
Principios de ciencia de datos
análisis estadístico
Visualización de datos
Algoritmos de aprendizaje automático
Sintaxis de Python y tipos de datos
Declaraciones condicionales
Bucles e iteración
Funciones y módulos
Manejo de archivos
Programación orientada a objetos
Manejo de excepciones
Estructuras de datos
Interacciones básicas de SQL e bases de datos
Expresiones regulares
Técnicas de depuración
Optimización de código
Documentación y comentarios
Prueba unitaria en Python

Preview this test

Ver cuadro de mando de muestra

What roles can I use the PyTorch Test for?

Científico de datos
Ingeniero de aprendizaje automático
Ingeniero de aprendizaje profundo
Analista de datos
Desarrollador de Python
Ingeniero de software
Investigador científico
Ingeniero de inteligencia artificial
Ingeniero de datos
Arquitecto de datos

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

Implementación de algoritmos de aprendizaje automático con Pytorch
Evaluación de modelos de aprendizaje automático con Pytorch
Comprender las redes neuronales y el aprendizaje profundo
Aplicación de técnicas para capacitación en redes neuronales
Implementación de redes neuronales convolucionales (CNN)
Trabajar con redes neuronales recurrentes (RNN)
Utilizando el aprendizaje de transferencia en Pytorch
Implementación de procesamiento del lenguaje natural (PNL) con Pytorch
Aplicación de técnicas de visión por computadora con Pytorch
Implementación de algoritmos de aprendizaje de refuerzo con Pytorch

Preview this test

Ver cuadro de mando de muestra

Los gerentes de contratación sintieron que a través de las preguntas técnicas que hicieron durante las entrevistas del panel, pudieron decir qué candidatos tenían mejores puntajes y diferenciarse de aquellos que no obtuvieron tan buenos puntajes. Ellos son altamente satisfecho con la calidad de los candidatos preseleccionados con la selección de Adaface.

85%

Reducción en el tiempo de detección

Ver estudio de caso completo

PyTorch Hiring Test Preguntas frecuentes

¿Puedo combinar múltiples habilidades en una evaluación personalizada?

Si, absolutamente. Las evaluaciones personalizadas se configuran en función de la descripción de su trabajo e incluirán preguntas sobre todas las habilidades imprescindibles que especifique.

¿Tiene alguna característica anti-trato o procuración en su lugar?

Tenemos las siguientes características anti-trate en su lugar:

Preguntas no postradas
Procuración de IP
Procedor web
Procedores de cámara web
Detección de plagio
navegador seguro

Lea más sobre las funciones de procuración.

¿Cómo interpreto los puntajes de las pruebas?

Lo principal a tener en cuenta es que una evaluación es una herramienta de eliminación, no una herramienta de selección. Una evaluación de habilidades está optimizada para ayudarlo a eliminar a los candidatos que no están técnicamente calificados para el rol, no está optimizado para ayudarlo a encontrar el mejor candidato para el papel. Por lo tanto, la forma ideal de usar una evaluación es decidir un puntaje umbral (generalmente del 55%, lo ayudamos a comparar) e invitar a todos los candidatos que obtienen un puntaje por encima del umbral para las próximas rondas de la entrevista.

¿Para qué nivel de experiencia puedo usar esta prueba?

Cada evaluación de AdaFace está personalizada para su descripción de trabajo/ persona candidata ideal (nuestros expertos en la materia elegirán las preguntas correctas para su evaluación de nuestra biblioteca de más de 10000 preguntas). Esta evaluación se puede personalizar para cualquier nivel de experiencia.

¿Cada candidato tiene las mismas preguntas?

Sí, te hace mucho más fácil comparar los candidatos. Las opciones para las preguntas de MCQ y el orden de las preguntas son aleatorizados. Tenemos características anti-trato/procuración en su lugar. En nuestro plan empresarial, también tenemos la opción de crear múltiples versiones de la misma evaluación con cuestiones de niveles de dificultad similares.