ML Infrastructure Engineer Test

The ML Infrastructure Engineer Test assesses a candidate's proficiency in designing and managing machine learning infrastructure. Through a mix of scenario-based MCQs and a Python coding challenge, it evaluates knowledge in areas like Generative AI, DevOps, MLOps, and core machine learning algorithms, ensuring candidates can effectively support ML operations.

Covered skills:

Generative AI
DevOps Practices
Machine Learning Operations
Machine Learning Algorithms
Python Programming
Infrastructure as Code
Continuous Integration Continuous Deployment
Model Deployment Strategies
Monitoring and Logging
Cloud Platforms Usage

Test Duration

~ 40 mins

Difficulty Level

Moderate

Questions

4 Generative AI MCQs
4 DevOps MCQs
4 ML Ops MCQs
4 Machine Learning MCQs
1 Python Coding Question

Availability

Available as custom test

Get started for free

Preview questions

About the ML Infrastructure Engineer Assessment Test

The ML Infrastructure Engineer 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:

Proficient in designing and implementing machine learning models.
Skilled in deploying models using MLOps best practices.
Understanding of Generative AI concepts and frameworks.
Ability to automate infrastructure using Infrastructure as Code (IaC).
Competent in setting up continuous integration and deployment pipelines.
Experience in monitoring and logging for ML models and applications.
Strong problem-solving skills using Python programming.
Able to optimize machine learning algorithms for performance.
Knowledgeable about cloud platform services and their application in ML workflows.
Expertise in DevOps practices to enhance collaboration and productivity.

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.

Get started for free

Preview questions

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 ML Infrastructure Engineer Test will be non-googleable.

🧐 Question
Medium Resource Analysis Process Management System Performance Log Analysis	Solve
As a senior DevOps engineer, you are tasked with diagnosing performance issues on a Linux server running Ubuntu 20.04. The server hosts several critical applications, but lately, users have been experiencing significant slowness. Initial monitoring shows that CPU and memory utilization are consistently high. To identify the root cause, you check the output of `top` and `ps` commands, which indicate that a particular process is consuming an unusually high amount of resources. However, the process name is generic and does not clearly indicate which application or service it belongs to. You also examine `/var/log/syslog` for any unusual entries but find nothing out of the ordinary. Based on this situation, which of the following steps would most effectively help you identify and resolve the performance issue? A: Increase the server's physical memory and CPU capacity. B: Use the `lsof` command to identify the files opened by the suspect process. C: Reboot the server to reset all processes. D: Examine the `/etc/hosts` file for any incorrect configurations. E: Run the `netstat` command to check for abnormal network activity. F: Check the crontab for any recently added scheduled tasks.
Medium Streamlined DevOps Continuous Integration Scripting Git Shell Scripting	Solve
You are in charge of developing a Bash script for setting up a continuous integration pipeline for a web application. The source code is hosted in a Git repository. The script's goals include: 1. Ensuring the local copy of the repository in /var/www/html is updated to the latest version. 2. Creating a .env file with APP_ENV=production in the project root if it doesn't already exist. 3. Running a test suite with ./run_tests.sh and handling any test failures appropriately. 4. Logging the current timestamp and commit hash in deployment_log.txt in the project root if tests pass. Which of the following script options would most effectively and safely accomplish these tasks?
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 Resource Analysis Process Management System Performance Log Analysis	3 mins DevOps	Solve
As a senior DevOps engineer, you are tasked with diagnosing performance issues on a Linux server running Ubuntu 20.04. The server hosts several critical applications, but lately, users have been experiencing significant slowness. Initial monitoring shows that CPU and memory utilization are consistently high. To identify the root cause, you check the output of `top` and `ps` commands, which indicate that a particular process is consuming an unusually high amount of resources. However, the process name is generic and does not clearly indicate which application or service it belongs to. You also examine `/var/log/syslog` for any unusual entries but find nothing out of the ordinary. Based on this situation, which of the following steps would most effectively help you identify and resolve the performance issue? A: Increase the server's physical memory and CPU capacity. B: Use the `lsof` command to identify the files opened by the suspect process. C: Reboot the server to reset all processes. D: Examine the `/etc/hosts` file for any incorrect configurations. E: Run the `netstat` command to check for abnormal network activity. F: Check the crontab for any recently added scheduled tasks.
	Medium Streamlined DevOps Continuous Integration Scripting Git Shell Scripting	2 mins DevOps	Solve
You are in charge of developing a Bash script for setting up a continuous integration pipeline for a web application. The source code is hosted in a Git repository. The script's goals include: 1. Ensuring the local copy of the repository in /var/www/html is updated to the latest version. 2. Creating a .env file with APP_ENV=production in the project root if it doesn't already exist. 3. Running a test suite with ./run_tests.sh and handling any test failures appropriately. 4. Logging the current timestamp and commit hash in deployment_log.txt in the project root if tests pass. Which of the following script options would most effectively and safely accomplish these tasks?
	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
	Resource Analysis Process Management System Performance Log Analysis	DevOps	Medium	3 mins	Solve
As a senior DevOps engineer, you are tasked with diagnosing performance issues on a Linux server running Ubuntu 20.04. The server hosts several critical applications, but lately, users have been experiencing significant slowness. Initial monitoring shows that CPU and memory utilization are consistently high. To identify the root cause, you check the output of `top` and `ps` commands, which indicate that a particular process is consuming an unusually high amount of resources. However, the process name is generic and does not clearly indicate which application or service it belongs to. You also examine `/var/log/syslog` for any unusual entries but find nothing out of the ordinary. Based on this situation, which of the following steps would most effectively help you identify and resolve the performance issue? A: Increase the server's physical memory and CPU capacity. B: Use the `lsof` command to identify the files opened by the suspect process. C: Reboot the server to reset all processes. D: Examine the `/etc/hosts` file for any incorrect configurations. E: Run the `netstat` command to check for abnormal network activity. F: Check the crontab for any recently added scheduled tasks.
	Streamlined DevOps Continuous Integration Scripting Git Shell Scripting	DevOps	Medium	2 mins	Solve
You are in charge of developing a Bash script for setting up a continuous integration pipeline for a web application. The source code is hosted in a Git repository. The script's goals include: 1. Ensuring the local copy of the repository in /var/www/html is updated to the latest version. 2. Creating a .env file with APP_ENV=production in the project root if it doesn't already exist. 3. Running a test suite with ./run_tests.sh and handling any test failures appropriately. 4. Logging the current timestamp and commit hash in deployment_log.txt in the project root if tests pass. Which of the following script options would most effectively and safely accomplish these tasks?
	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?

Get started for free

Preview questions

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

It's very easy to share assessments with candidates and for candidates to use. We get good feedback from candidates about completing the tests. Adaface are very responsive and friendly to deal with.

Kirsty Wood, Human Resources, WillyWeather

We were able to close 106 positions in a record time of 45 days! Adaface enables us to conduct aptitude and psychometric assessments seamlessly. My hiring managers have never been happier with the quality of candidates shortlisted.

Amit Kataria, CHRO, Hanu

We evaluated several of their competitors and found Adaface to be the most compelling. Great library of questions that are designed to test for fit rather than memorization of algorithms.

Swayam Narain, CTO, Affable

Why you should use Pre-employment ML Infrastructure Engineer Test?

The ML Infrastructure Engineer 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:

Implementing generative AI models.
Using DevOps tools for automation.
Applying ML algorithms to datasets.
Developing CI/CD pipelines.
Writing efficient Python scripts.
Deploying models with ML Ops practices.
Using cloud platforms for deployments.
Implementing monitoring and logging tools.
Creating infrastructure with code.
Managing model lifecycle in production.

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 ML Infrastructure Engineer Test?

Generative AI: Generative AI focuses on creating models that generate new content, such as images, text, or music, based on learned patterns from input data. This skill is crucial for roles that require innovative solutions and automation in content creation. It signals an engineer's capability to build complex models that can enhance productivity and create novel outputs.

DevOps Practices: DevOps practices encompass the methodologies that integrate development and operations, aiming for faster development cycles and more reliable software releases. Proficiency in these practices ensures that engineers can work efficiently in collaborative environments, overseeing seamless updates and maintenance of systems.

Machine Learning Operations: Machine Learning Operations, or MLOps, involves deploying and maintaining machine learning models in production reliably and efficiently. Testing for MLOps proficiency indicates an engineer's ability to scale, automate, and manage ML workflows in a stable manner.

Machine Learning Algorithms: Understanding machine learning algorithms is essential for building models that can predict or classify data based on learned patterns. Mastery of these algorithms reflects a candidate's capacity to select and apply the right techniques to solve specific problems effectively.

Python Programming: Python programming is a cornerstone in the data science and machine learning fields due to its simplicity and powerful libraries. Measuring this skill highlights an engineer's ability to implement complex algorithms and build data-driven applications.

Infrastructure as Code: Infrastructure as Code (IaC) is the process of managing and provisioning computing infrastructure through machine-readable configuration files. This skill is vital for enabling consistent environments, automating setups, and enhancing resource management efficiency.

Continuous Integration Continuous Deployment: Continuous Integration Continuous Deployment (CI/CD) refers to the automated process of integrating code changes and deploying them to production. A strong grasp of CI/CD ensures that engineers can maintain high software quality with reduced risk of deployment failures.

Model Deployment Strategies: Model deployment strategies focus on deploying ML models into production systems effectively and efficiently. This competency suggests the candidate's ability to ensure models are accessible and perform optimistically in a live environment.

Monitoring and Logging: Monitoring and logging involve tracking system performance and recording data for troubleshooting and optimization. Being adept with these skills ensures that engineers can detect and resolve issues promptly, maintaining system integrity and performance.

Cloud Platforms Usage: Cloud platforms provide scalable resources and services needed for complex computations and storage in machine learning projects. Familiarity with cloud platforms indicates an engineer's capability to leverage these services for cost-effective and flexible infrastructure management.

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 ML Infrastructure Engineer Test to be based on.

Generative Models

GANs

Transformer Models

DevOps Pipelines

CI/CD

IaC

Docker Basics

Kubernetes

Terraform

AWS

Azure

Google Cloud

MLOps Lifecycle

Model Deployment

Data Versioning

Experiment Tracking

Error Handling

Python Syntax

Python Libraries

Scikit-learn

TensorFlow

PyTorch

Hyperparameter Tuning

Feature Engineering

Apache Kafka

ML Monitoring

Logging Tools

Git

Version Control

Cloud Storage

API Integration

Security Best Practices

Load Balancing

Autoscaling

Environment Management

Code Refactoring

Test Automation

Resource Allocation

Parallel Processing

Scripting

Model Serving

Latency Optimization

Data Ingestion

Job Scheduling

Configuration Management

Deployment Strategies

What roles can I use the ML Infrastructure Engineer Test for?

ML Infrastructure Engineer
Machine Learning Engineer
DevOps Engineer
Data Scientist
AI Engineer
Cloud Engineer
Site Reliability Engineer
Software Engineer
Data Engineer
Automation Engineer

How is the ML Infrastructure Engineer 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

Optimizing model performance in deployment.
Scaling ML infrastructures efficiently.
Ensuring data security in cloud deployments.
Automating DevOps operations at scale.
Implementing advanced monitoring solutions.
Designing robust integration strategies.
Building complex pipelines with CI/CD.
Debugging ML deployment issues.
Enhancing generative AI capabilities.
Utilizing cutting-edge ML frameworks.

The coding question for experienced candidates will be of a higher difficulty level to evaluate more hands-on experience.

Preview this test

View sample scorecard

Try the most advanced candidate assessment platform

AI Cheating Detection with Honestly

ChatGPT Protection

Non-googleable Questions

Web Proctoring

IP Proctoring

Webcam Proctoring

MCQ Questions

Coding Questions

Typing Questions

Personality Questions

Custom Questions

Ready-to-use Tests

Custom Tests

Custom Branding

Bulk Invites

Public Links

ATS Integrations

Multiple Question Sets

Custom API integrations

Role-based Access

Priority Support

GDPR Compliance

Screen candidates in 3 easy steps

Pick a test from over 500+ tests

The Adaface test library features 500+ tests to enable you to test candidates on all popular skills- everything from programming languages, software frameworks, devops, logical reasoning, abstract reasoning, critical thinking, fluid intelligence, content marketing, talent acquisition, customer service, accounting, product management, sales and more.

Invite your candidates with 2-clicks

Make informed hiring decisions

Get started for free

Preview questions

Have questions about the ML Infrastructure Engineer Hiring Test?

What is the ML Infrastructure Engineer Test?

The ML Infrastructure Engineer Test is designed to assess candidates on skills such as Generative AI, DevOps, MLOps, Machine Learning, and Python. It is used by recruiters to identify top talents for senior ML infrastructure engineering roles.

Can I combine the ML Infrastructure Engineer Test with DevOps questions?

Yes, recruiters can request a custom test with multiple skills. You can explore our DevOps Online Test for more details on how we assess DevOps skills.

What topics are evaluated in the ML Infrastructure Engineer Test?

The test evaluates topics including Generative AI capabilities, ML operations, model deployment strategies, code infrastructure, CI/CD pipelines, and cloud platform usage.

How to use the ML Infrastructure Engineer Test in my hiring process?

Use the test as a pre-screening tool. Include a link to the test in your job post or invite candidates by email to streamline and improve your candidate selection process.

What are some main AI and machine learning tests?

Explore other tests like Deep Learning Test and Machine Learning Assessment Test for broader assessments on AI and ML.

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:

Hidden AI Tools Detection with Honestly
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.

View sample scorecard

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.

View sample scorecard