Deep Learning Online Test

Q: What are the main AI and Machine Learning tests?

Our main AI and Machine Learning tests include: Machine Learning Assessment Test Natural Language Processing (NLP) Online Test Computer Vision Test Data Science Assessment 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
Data Normalization
Cost Functions and Activation Functions
Backpropagation
Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
Generative Adversarial Networks
Natural Language Processing
Computer Vision
Transfer Learning
Autoencoders
Optimization Algorithms

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About the Deep Learning Test

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

1200+ customers in 80 countries

Use Adaface tests trusted by recruitment teams globally. Adaface skill assessments measure on-the-job skills of candidates, providing employers with an accurate tool for screening potential hires.

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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 15,000+ 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: 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 Techniques For Handling Class Imbalance Hyperparameter Tuning	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 Learning Rate Schedules Optimization Techniques	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: 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 Data Transformation Overfitting Prevention	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: 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 String Manipulation Algorithm	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?
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 Sensitivity Specificity	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: Based on the confusion matrix, what are the sensitivity and specificity of the model?

	🧐 Question	🔧 Skill
	Medium Changed decision boundary	2 mins Deep Learning	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	3 mins Deep Learning	Solve
You are fine-tuning a Convolutional Neural Network (CNN) for image classification. The network architecture is as follows: 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 Techniques For Handling Class Imbalance Hyperparameter Tuning	3 mins Deep Learning	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 Learning Rate Schedules Optimization Techniques	2 mins Machine Learning	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: 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 Data Transformation Overfitting Prevention	2 mins Machine Learning	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: 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 String Manipulation Algorithm	2 mins Machine Learning	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?
	Easy Recommendation System Selection Recommender Systems Collaborative Filtering Content-Based Filtering	2 mins Machine Learning	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 Sensitivity Specificity	2 mins Machine Learning	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: Based on the confusion matrix, what are the sensitivity and specificity of the model?

	🧐 Question	🔧 Skill	💪 Difficulty	⌛ Time
	Changed decision boundary	Deep Learning	Medium	2 mins	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
	CNN Architecture Tuning Convolutional Neural Networks Hyperparameter Optimization	Deep Learning	Medium	3 mins	Solve
You are fine-tuning a Convolutional Neural Network (CNN) for image classification. The network architecture is as follows: 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
	CNN for Imbalanced Image Dataset Convolutional Neural Networks Imbalanced Datasets Techniques For Handling Class Imbalance Hyperparameter Tuning	Deep Learning	Medium	3 mins	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
	Gradient descent optimization Gradient Descent Learning Rate Schedules Optimization Techniques	Machine Learning	Easy	2 mins	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: 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
	Less complex decision tree model Model Complexity Overfitting Data Transformation Overfitting Prevention	Machine Learning	Medium	2 mins	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: 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
	n-gram generator String Manipulation Algorithm	Machine Learning	Easy	2 mins	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?
	Recommendation System Selection Recommender Systems Collaborative Filtering Content-Based Filtering	Machine Learning	Easy	2 mins	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?
	Sensitivity and Specificity Confusion Matrix Model Evaluation Sensitivity Specificity	Machine Learning	Easy	2 mins	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: Based on the confusion matrix, what are the sensitivity and specificity of the model?

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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

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Have questions about the Deep Learning Hiring Test?

What is Deep Learning Test?

The Deep Learning Test is designed to assess a candidate's knowledge and skills in deep learning, including various neural networks, data normalization, cost functions, and more. It is used by recruiters and organizations to identify proficient candidates for deep learning roles.

Can I combine Deep Learning Test with Machine Learning questions?

Yes, recruiters can request a single custom test that includes both Deep Learning and Machine Learning questions. For details on how we assess machine learning skills, you can check Machine Learning Assessment Test.

What type of questions are used to evaluate senior candidates in the Deep Learning Test?

The test includes questions on advanced topics such as autoencoders, optimization algorithms, gradient descent variants, learning rate scheduling, batch normalization, dropout regularization, weight initialization strategies, early stopping, and model evaluation techniques.

How to use Deep Learning Test in my hiring process?

Use our Deep Learning Test as a pre-screening tool early in your recruitment process. Add a link to the assessment in your job post or invite candidates via email. Identify skilled candidates quickly and effectively.

What are the main AI and Machine Learning tests?

Our main AI and Machine Learning tests include:

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?

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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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Along with scorecards that report the performance of the candidate in detail, you also receive a comparative analysis against the company average and industry standards.

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