Search test library by skills or roles
⌘ K

About the test:

The deep learning pre-employment test evaluates a candidate's understanding of core deep learning concepts like activation functions, backpropagation, RNNs & CNNs, learning rate, dropout, batch normalization, data processing pipelines, multi-layer perceptrons and data normalization. This test also focuses on their ability to apply deep learning algorithms to use cases like computer vision, image recognition, object detection, text classification etc.

Covered skills:

  • Neural Networks
  • Cost Functions and Activation Functions
  • Neural Networks
  • Recurrent Neural Networks
  • Natural Language Processing
  • Transfer Learning
  • Optimization Algorithms
  • Data Normalization
  • Backpropagation
  • Convolutional Neural Networks
  • Generative Adversarial Networks
  • Computer Vision
  • Autoencoders

9 reasons why
9 reasons why

Adaface Deep Learning Test is the most accurate way to shortlist Data Scientists



Reason #1

Tests for on-the-job skills

The Deep Learning 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:

  • Understanding and implementing neural networks
  • Applying data normalization techniques
  • Selecting appropriate cost functions and activation functions
  • Implementing backpropagation algorithm
  • Designing and evaluating convolutional neural networks
  • Developing recurrent neural networks
  • Creating generative adversarial networks
  • Applying natural language processing techniques
  • Implementing computer vision algorithms
  • Understanding and implementing transfer learning
  • Developing autoencoders
  • Optimizing deep learning models using optimization algorithms
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
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 Deep Learning Test will be non-googleable.

🧐 Question

Medium

Changed decision boundary
Solve
We have trained a model on a linearly separable training set to classify the data points into 2 sets (binary classification). Our intern recently labelled some new data points which are all correctly classified by the model. All of the new data points lie far away from the decision boundary. We added these new data points and re-trained our model- our decision boundary changed. Which of these models do you think we could be working with?
The 2 data sources use SQL Server and have a 3-character CompanyCode column. Both data sources contain an ORDER BY clause to sort the data by CompanyCode in ascending order. 

Teylor wants to make sure that the Merge Join transformation works without additional transformations. What would you recommend?
A: Perceptron
B: SVM
C: Logistic regression
D: Guassion discriminant analysis

Medium

CNN Architecture Tuning
Convolutional Neural Networks
Hyperparameter Optimization
Solve
You are fine-tuning a Convolutional Neural Network (CNN) for image classification. The network architecture is as follows:
 image
The model is trained using the following parameters:

- Batch size: 64
- Learning rate: 0.001
- Optimizer: Adam
- Loss function: Categorical cross-entropy

After several training epochs, you observe that the training accuracy is high, but the validation accuracy plateaus and is significantly lower. This suggests possible overfitting. Which of the following adjustments would most effectively mitigate this issue without overly compromising the model's performance?
A: Increase the batch size to 128
B: Add dropout layers with a dropout rate of 0.5 after each MaxPooling2D layer
C: Replace Adam optimizer with SGD (Stochastic Gradient Descent)
D: Decrease the number of filters in each Conv2D layer by half
E: Increase the learning rate to 0.01
F: Reduce the size of the Dense layer to 64 units

Medium

CNN for Imbalanced Image Dataset
Convolutional Neural Networks
Imbalanced Datasets
Solve
You are fine-tuning a Convolutional Neural Network (CNN) for an image classification task where the dataset is highly imbalanced. The majority class comprises 70% of the data. The initial model setup and subsequent experiments yield the following observations:

**Initial Setup:**

- CNN architecture: 6 convolutional layers with increasing filter sizes, followed by 2 fully connected layers.
- Activation function: ReLU
- No class-weighting or data augmentation.
- Results: High overall accuracy, but poor precision and recall for minority classes.

**Experiment 1:**

- Changes: Implement class-weighting to penalize mistakes on minority classes more heavily.
- Results: Improved precision and recall for minority classes, but overall accuracy slightly decreased.

**Experiment 2:**

- Changes: Add dropout layers with a rate of 0.5 after each convolutional layer.
- Results: Overall accuracy decreased, and no significant change in precision and recall for minority classes.

Given these outcomes, what is the most effective strategy to further improve the model's performance specifically for the minority classes without compromising the overall accuracy?
A: Increase the dropout rate to 0.7
B: Further fine-tune class-weighting parameters
C: Increase the number of filters in the convolutional layers
D: Add batch normalization layers after each convolutional layer
E: Use a different activation function like LeakyReLU
F: Implement more aggressive data augmentation on the minority class

Easy

Gradient descent optimization
Gradient Descent
Solve
You are working on a regression problem using a simple neural network. You want to optimize the model's weights using gradient descent with different learning rate schedules. Consider the following pseudo code for training the neural network:
 image
Which of the following learning rate schedules would most likely result in the fastest convergence without overshooting the optimal weights?

A: Constant learning rate of 0.01
B: Exponential decay with initial learning rate of 0.1 and decay rate of 0.99
C: Exponential decay with initial learning rate of 0.01 and decay rate of 0.99
D: Step decay with initial learning rate of 0.1 and decay rate of 0.5 every 100 epochs
E: Step decay with initial learning rate of 0.01 and decay rate of 0.5 every 100 epochs
F: Constant learning rate of 0.1

Medium

Less complex decision tree model
Model Complexity
Overfitting
Solve
You are given a dataset to solve a classification problem using a decision tree algorithm. You are concerned about overfitting and decide to implement pruning to control the model's complexity. Consider the following pseudo code for creating the decision tree model:
 image
Which of the following combinations of parameters would result in a less complex decision tree model, reducing the risk of overfitting?

A: max_depth=5, min_samples_split=2, min_samples_leaf=1
B: max_depth=None, min_samples_split=5, min_samples_leaf=5
C: max_depth=3, min_samples_split=2, min_samples_leaf=1
D: max_depth=None, min_samples_split=2, min_samples_leaf=1
E: max_depth=3, min_samples_split=10, min_samples_leaf=10
F; max_depth=5, min_samples_split=5, min_samples_leaf=5

Easy

n-gram generator
Solve
Our newest machine learning developer want to write a function to calculate the n-gram of any text. An N-gram means a sequence of N words. So for example, "black cats" is a 2-gram, "saw black cats" is a 3-gram etc. The 2-gram of the sentence "the big bad wolf fell down" would be [["the", "big"], ["big", "bad"], ["bad", "wolf"], ["wolf", "fell"], ["fell", "down"]]. Can you help them select the correct function for the same?
 image

Easy

Recommendation System Selection
Recommender Systems
Collaborative Filtering
Content-Based Filtering
Solve
You are tasked with building a recommendation system for a newly launched e-commerce website. Given that the website is new, there is not much user interaction data available. Also, the items in the catalog have rich descriptions. Based on these requirements, which type of recommendation system approach would be the most suitable for this task?

Easy

Sensitivity and Specificity
Confusion Matrix
Model Evaluation
Solve
You have trained a supervised learning model to classify customer reviews as either "positive" or "negative" based on a dataset with 10,000 samples and 35 features, including the review text, reviewer's name, and rating. The dataset is split into a 7,000-sample training set and a 3,000-sample test set.

After training the model, you evaluate its performance using a confusion matrix on the test set, which shows the following results:
 image
Based on the confusion matrix, what are the sensitivity and specificity of the model?
🧐 QuestionπŸ”§ Skill

Medium

Changed decision boundary

2 mins

Deep Learning
Solve

Medium

CNN Architecture Tuning
Convolutional Neural Networks
Hyperparameter Optimization

3 mins

Deep Learning
Solve

Medium

CNN for Imbalanced Image Dataset
Convolutional Neural Networks
Imbalanced Datasets

3 mins

Deep Learning
Solve

Easy

Gradient descent optimization
Gradient Descent

2 mins

Machine Learning
Solve

Medium

Less complex decision tree model
Model Complexity
Overfitting

2 mins

Machine Learning
Solve

Easy

n-gram generator

2 mins

Machine Learning
Solve

Easy

Recommendation System Selection
Recommender Systems
Collaborative Filtering
Content-Based Filtering

2 mins

Machine Learning
Solve

Easy

Sensitivity and Specificity
Confusion Matrix
Model Evaluation

2 mins

Machine Learning
Solve
🧐 QuestionπŸ”§ SkillπŸ’ͺ DifficultyβŒ› Time
Changed decision boundary
Deep Learning
Medium2 mins
Solve
CNN Architecture Tuning
Convolutional Neural Networks
Hyperparameter Optimization
Deep Learning
Medium3 mins
Solve
CNN for Imbalanced Image Dataset
Convolutional Neural Networks
Imbalanced Datasets
Deep Learning
Medium3 mins
Solve
Gradient descent optimization
Gradient Descent
Machine Learning
Easy2 mins
Solve
Less complex decision tree model
Model Complexity
Overfitting
Machine Learning
Medium2 mins
Solve
n-gram generator
Machine Learning
Easy2 mins
Solve
Recommendation System Selection
Recommender Systems
Collaborative Filtering
Content-Based Filtering
Machine Learning
Easy2 mins
Solve
Sensitivity and Specificity
Confusion Matrix
Model Evaluation
Machine Learning
Easy2 mins
Solve
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

Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment Deep Learning 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 Deep Learning 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

View sample scorecard
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 Deep Learning Assessment Test

Why you should use Pre-employment Deep Learning Online Test?

The Deep Learning 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:

  • Ability to build and train neural networks
  • Understanding of data normalization techniques
  • Knowledge of various cost functions and activation functions
  • Proficiency in implementing backpropagation
  • Capability to design and optimize convolutional neural networks
  • Familiarity with recurrent neural networks and their applications
  • Understanding of generative adversarial networks and their components
  • Knowledge of natural language processing techniques
  • Proficiency in computer vision algorithms and techniques
  • Ability to apply transfer learning in deep learning models

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 Deep Learning Online Test?

  • Neural Networks

    Neural networks are a computational model inspired by biological neural networks. They are composed of layers of interconnected nodes, or artificial neurons, that process and transmit information using weighted inputs. They are measured in this test to assess the understanding of fundamental concepts in deep learning.

  • Data Normalization

    Data normalization is a technique used to standardize the range of data values. It involves transforming the data to have a consistent scale, typically between 0 and 1. This skill is measured in this test to evaluate the ability to preprocess data effectively, which is crucial for training accurate neural networks.

  • Cost Functions and Activation Functions

    Cost functions are used to measure the difference between predicted and actual values in a neural network, guiding the learning process. Activation functions introduce non-linearity to the output of each neuron in a neural network, enabling complex computations. This skill is measured in this test to assess the knowledge of selecting appropriate cost and activation functions for different tasks.

  • Backpropagation

    Backpropagation is a key algorithm for training neural networks. It calculates the gradients of the network's parameters with respect to the loss, allowing for the adjustment of weights in previous layers. This skill is measured in this test to gauge the understanding of how gradients propagate backward through a neural network for efficient learning.

  • Convolutional Neural Networks

    Convolutional neural networks (CNNs) are deep learning models specifically designed for processing structured grid data, such as images. They are built on the idea of convolution, where filters scan and extract local patterns from input data. This skill is measured in this test to evaluate the knowledge of CNN architecture and its application in computer vision tasks.

  • Recurrent Neural Networks

    Recurrent neural networks (RNNs) are neural networks that process variable-length sequential data, such as text or time-series. They have feedback connections that allow information to persist throughout the network. This skill is measured in this test to assess understanding of RNNs and their ability to model sequential patterns.

  • Generative Adversarial Networks

    Generative adversarial networks (GANs) consist of two neural networks: a generator and a discriminator. They are trained together in a competitive process, where the generator aims to produce synthetic data that is indistinguishable from real data. This skill is measured in this test to evaluate knowledge of GAN architecture and its application in generating realistic data.

  • Natural Language Processing

    Natural language processing (NLP) involves the interaction between computers and human language. It encompasses tasks such as speech recognition, text classification, and machine translation. This skill is measured in this test to assess the understanding of NLP techniques and their application in various language-related tasks.

  • Computer Vision

    Computer vision is a branch of artificial intelligence that deals with interpreting visual information from images or videos. It involves tasks like object detection, image recognition, and image segmentation. This skill is measured in this test to evaluate the knowledge of computer vision algorithms and their application in solving visual perception problems.

  • Transfer Learning

    Transfer learning refers to leveraging pre-trained models on one task to improve performance on another task. By utilizing knowledge gained from previous tasks, transfer learning can significantly reduce the amount of training data and time required. This skill is measured in this test to assess the understanding of transferring learned features from one domain to another.

  • Autoencoders

    Autoencoders are neural networks designed to reconstruct the input data from a compressed representation, called the latent space. They are often used for unsupervised learning and dimensionality reduction. This skill is measured in this test to evaluate the knowledge of autoencoders and their application in tasks like data compression and anomaly detection.

  • Optimization Algorithms

    Optimization algorithms play a crucial role in training neural networks by iteratively adjusting the model's parameters to minimize the training loss. Examples include stochastic gradient descent (SGD), Adam, and RMSprop. This skill is measured in this test to assess the familiarity with different optimization algorithms and their impact on network convergence and performance.

  • 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 Deep Learning Test to be based on.

    Neuron
    Gradient descent
    Feedforward neural network
    Bias
    Activation function
    Weight initialization
    Overfitting
    Regularization
    Loss function
    Learning rate
    Batch normalization
    Dropout
    Convolutional layer
    Pooling
    Recurrent neural network
    LSTM
    GAN
    Language modeling
    Word embeddings
    CNN architecture
    Image classification
    Object detection
    Image segmentation
    RNN architecture
    Speech recognition
    Sentiment analysis
    Reinforcement learning
    Text generation
    Optimization algorithms
    Adam optimizer
    Stochastic gradient descent
    Learning rate decay
    Transfer learning techniques
    Pretrained models
    Autoencoder architecture
    Dimensionality reduction
    Encoder-decoder
    Hyperparameter tuning
    Data augmentation
    Regularized autoencoders
    Noise injection
    Vanishing gradient problem
    Generative models
    GAN training
    Image generation
    Adversarial attacks
    CNN interpretability
    Attention mechanisms
    Natural language understanding
    Visual question answering
    Image captioning
    Transformers
    BERT
    Deep reinforcement learning
    Policy gradient
    Value iteration
    Q-learning
    Autoencoders for anomaly detection
    Artificial neural networks

What roles can I use the Deep Learning Online Test for?

  • Data Scientist
  • Machine Learning Engineer
  • Artificial Intelligence Researcher
  • Deep Learning Engineer
  • Data Analyst
  • Computer Vision Engineer
  • Natural Language Processing Engineer
  • AI Consultant
  • Artificial Intelligence Roles
  • Research Scientist

How is the Deep Learning Online 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

  • Knowledge of autoencoders and their applications
  • Proficiency in optimization algorithms for neural networks
  • Capability to implement gradient descent and its variants
  • Understanding of stochastic gradient descent and its variants
  • Knowledge of learning rate scheduling techniques
  • Proficiency in batch normalization in neural networks
  • Ability to implement dropout regularization in models
  • Understanding of weight initialization strategies
  • Knowledge of early stopping in training neural networks
  • Proficiency in model evaluation and validation techniques
Singapore government logo

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

Deep Learning 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.

customers across world
Join 1200+ companies in 75+ countries.
Try the most candidate friendly skills assessment tool today.
g2 badges
Ready to use the Adaface Deep Learning Test?
Ready to use the Adaface Deep Learning Test?
logo
40 min tests.
No trick questions.
Accurate shortlisting.
Terms Privacy Trust Guide
ada
Ada
● Online
Previous
Score: NA
Next
βœ–οΈ