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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
  • Multi-layer Perceptron
  • Hidden Layer
  • Data Normalization
See all covered skills

9 reasons why
9 reasons why

Adaface Deep Learning Online Test is the most accurate way to shortlist Deep Learning Engineers



Reason #1

Tests for on-the-job skills

The Deep Learning Online Test helps recruiters and hiring managers to 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.

The Adaface Deep Learning Online test screens candidates for the typical skills recruiters look for Deep Learning Engineer roles:

  • Experience designing and building deep learning models
  • Expertise in turning unstructured data into useful information
  • Experience solving complex problems with multi-layered data sets, and optimizing existing machine learning libraries and frameworks

The insights generated from this assessment can be used by recruiters and hiring managers to identify the best candidates for the Deep Learning Engineer role. Anti-cheating features enable you to be comfortable with conducting assessments online. The Deep Learning Engineer test is ideal for helping recruiters identify which candidates have the skills to do well on the job.

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.

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.

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

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

Medium

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

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?
🧐 Question🔧 Skill

Medium

Changed decision boundary
2 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

Medium

n-gram generator
2 mins
Machine Learning
Solve

Easy

Sensitivity and Specificity
Confusion Matrix
Model Evaluation
2 mins
Machine Learning
Solve
🧐 Question🔧 Skill💪 Difficulty⌛ Time
Changed decision boundary
Deep Learning
Medium2 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
Medium2 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.

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 & comparative results

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


About Deep Learning Engineer Roles

Deep learning is a subfield of machine learning that imitates the way humans gain certain types of knowledge.

Typical responsibilities of a Deep Learning Engineer include:

  • Perform statistical analysis
  • Design and develop Machine Learning and Deep Learning models
  • Train and re-train ML systems and models
  • Extend and enrich existing frameworks and libraries
  • Run tests, perform statistical analysis, and interpret test results

What roles can I use the Deep Learning Test for?

  • Deep Learning Engineer
  • Research Engineer - Deep Learning
  • Machine Learning Engineer

What topics are covered in the Deep Learning Online Test?

Deep Learning
Neural Network
Hidden Layer
Multi-layer Perceptron
Data Normalization
Boltzmann Machine
Activation Function
Cost Function
Gradient Descent
Backpropagation
Feedforward Neural Network
Recurrent Neural Network
ReLU
Hyperparameters
Learning Rate
Batch Normalization
Batch Gradient Descent
Stochastic Gradient Descent
Overfitting
Underfitting
Pooling Layer
Long-Short-Term Memory
Exploding Gradient
Epoch
Generative Adversarial Network
Computer Vision
Object Detection
Auto-encoder
Boosting
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

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.

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
  • Web proctoring
  • Webcam proctoring
  • Plagiarism detection
  • Secure browser

Read more about the proctoring features.

How do I interpret test scores?

The primary thing to keep in mind is that an assessment is an elimination tool, not a selection tool. A skills assessment is optimized to help you eliminate candidates who are not technically qualified for the role, it is not optimized to help you find the best candidate for the role. So the ideal way to use an assessment is to decide a threshold score (typically 55%, we help you benchmark) and invite all candidates who score above the threshold for the next rounds of interview.

What experience level can I use this test for?

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

Does every candidate get the same questions?

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

I'm a candidate. Can I try a practice test?

No. Unfortunately, we do not support practice tests at the moment. However, you can use our sample questions for practice.

What is the cost of using this test?

You can check out our pricing plans.

Can I get a free trial?

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

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

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

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