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
  • 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

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.

これらは、10,000以上の質問のライブラリからのわずかなサンプルです。これに関する実際の質問 Deep Learning Test グーグルできません.

🧐 Question


Changed decision boundary
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
C: Logistic regression
D: Guassion discriminant analysis


n-gram generator
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?
🧐 Question🔧 Skill


Changed decision boundary
2 mins
Deep Learning


n-gram generator
2 mins
Machine Learning
🧐 Question🔧 Skill💪 Difficulty⌛ Time
Changed decision boundary
Deep Learning
Medium2 mins
n-gram generator
Machine Learning
Hard2 mins
Reason #4

1200+ customers in 75 countries



Brandon Lee, 人々の頭, 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.

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.

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
Feedforward Neural Network
Recurrent Neural Network
Learning Rate
Batch Normalization
Batch Gradient Descent
Stochastic Gradient Descent
Pooling Layer
Long-Short-Term Memory
Exploding Gradient
Generative Adversarial Network
Computer Vision
Object Detection
Singapore government logo

採用マネージャーは、パネルインタビュー中に尋ねた技術的な質問を通じて、どの候補者がより良いスコアを持っているかを知ることができ、得点しなかった人と差別化することができたと感じました。彼らです 非常に満足しています 候補者の品質は、ADAFACEスクリーニングで最終候補になりました。







  • グーグル不可能な質問
  • IP監督
  • Webの提案
  • ウェブカメラの監督
  • 盗作の検出
  • 安全なブラウザ












Join 1200+ companies in 75+ countries.
Ready to use the Adaface Deep Learning Test?
Ready to use the Adaface Deep Learning Test?
40 min tests.
No trick questions.
Accurate shortlisting.
条項 プライバシー トラストガイド


English Norsk Dansk Deutsche Nederlands Svenska Français Español Chinese (简体中文) Italiano Japanese (日本語) Polskie Português Russian (русский)
● Online
Score: NA