MLOps Skills Test

Q: What are the main Machine Learning tests available?

We offer several tests in the Machine Learning category: Machine Learning Assessment Test Machine Learning in AWS Test Machine Learning in Azure Test Machine Learning in GCP Test

The MLOps Test evaluates a candidate's proficiency in machine learning operations, covering key aspects of the ML lifecycle, model deployment, and CI/CD practices for ML. It assesses knowledge of model monitoring, data version control, and ML infrastructure through scenario-based MCQs, ensuring candidates can effectively manage and operationalize machine learning projects in production environments.

Covered skills:

MLOps Fundamentals
Machine Learning Lifecycle
Model Deployment
CI/CD for ML
Model Monitoring
Data Version Control
Feature Engineering
Model Serving
ML Infrastructure
Experiment Tracking
Model Versioning
Automated ML

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About the MLOps Skills Assessment Test

The MLOps Skills 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:

Demonstrate proficiency in implementing and managing MLOps pipelines for efficient model development and deployment
Effectively utilize version control systems for managing ML code, data, and model artifacts
Design and implement robust CI/CD pipelines specifically tailored for machine learning workflows
Develop strategies for monitoring model performance and detecting drift in production environments
Implement and manage feature stores for consistent and efficient feature engineering across ML projects
Utilize containerization and orchestration tools to create scalable and reproducible ML environments
Implement automated testing and validation procedures for ML models to ensure reliability and performance
Design and implement efficient model serving architectures for real-time and batch inference
Utilize experiment tracking tools to manage and compare multiple ML experiments effectively
Implement data versioning strategies to ensure reproducibility and traceability in ML projects
Develop automated machine learning pipelines to streamline model development and optimization processes

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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 MLOps Test will be non-googleable.

🧐 Question
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
	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
	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 MLOps Skills Test?

The MLOps 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 MLOps pipelines for model deployment
Containerizing ML models using Docker
Configuring CI/CD pipelines for ML projects
Versioning ML models and datasets
Monitoring model performance in production
Implementing feature stores for ML projects
Orchestrating ML workflows using Airflow
Managing experiment tracking with MLflow
Implementing automated testing for ML models
Optimizing ML models for production environments

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 MLOps Skills Test?

MLOps Fundamentals: MLOps Fundamentals cover the core principles and practices essential for operationalizing machine learning models. This includes understanding the end-to-end ML lifecycle, from development to deployment, and ensuring seamless integration with business processes. Knowledge of MLOps is critical as it allows data scientists and engineers to efficiently manage and maintain ML models in production.

Machine Learning Lifecycle: The Machine Learning Lifecycle encompasses all steps from data collection, model training, validation, and deployment to monitoring. Understanding each phase ensures the ability to deliver robust and high-performing ML models. This skill is crucial for ensuring systematic and reproducible ML workflows.

Model Deployment: Model Deployment involves transferring trained models into a production environment where they can make real-time predictions. Mastery of this skill ensures that models can be scaled efficiently and integrated with existing systems. It's important to measure this skill to ensure the candidate can effectively operationalize ML models.

CI/CD for ML: Continuous Integration and Continuous Deployment (CI/CD) for ML automates the pipeline for delivering ML models. This skill ensures that updates to ML models are seamlessly integrated and delivered without manual intervention. CI/CD is vital for maintaining the quality and consistency of the models throughout their lifecycle.

Model Monitoring: Model Monitoring involves tracking the performance of ML models in production to detect issues such as data drift, bias, or degradation. Skills in this area ensure prompt identification and resolution of problems. This capability is essential for maintaining accurate and reliable model predictions over time.

Data Version Control: Data Version Control (DVC) tracks changes to datasets and ensures reproducibility of experiments. It is crucial for maintaining the integrity and consistency of data used for training and evaluation. Mastery in DVC empowers teams to manage the evolution of datasets effectively.

Feature Engineering: Feature Engineering involves creating new input features from raw data to improve model performance. This skill is vital as it directly impacts the accuracy and effectiveness of machine learning models. By measuring this skill, we ensure the candidate can optimize data for better model outcomes.

Model Serving: Model Serving is the process of making trained models available for use in production environments. It ensures that models can make predictions on new data efficiently and in real time. Proficiency in this area is key to deploying scalable and responsive machine learning solutions.

ML Infrastructure: ML Infrastructure refers to the hardware and software environment required to support machine learning operations, including data storage, computing power, and network resources. Understanding infrastructure is crucial as it directly impacts the performance and scalability of ML projects. This knowledge ensures the candidate can build and maintain robust ML systems.

Experiment Tracking: Experiment Tracking allows for logging, organizing, and comparing various versions of ML experiments. This skill is fundamental for understanding what changes lead to improvements in model performance. It is essential for reproducible and systematic ML model development.

Model Versioning: Model Versioning is the practice of managing and storing different versions of machine learning models. It allows teams to track changes and revert to previous versions if necessary. This skill is important for ensuring that model updates are tracked and managed systematically.

Automated ML: Automated ML (AutoML) involves using automation to select and tune machine learning models and parameters, reducing the need for manual intervention. AutoML accelerates the ML model development process and ensures that models are optimized for performance. Mastering AutoML tools allows for efficient and scalable machine learning practices.

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 MLOps Test to be based on.

MLOps

Model Deployment

CI/CD

Model Monitoring

Feature Stores

Data Versioning

Experiment Tracking

Model Versioning

AutoML

Containerization

Orchestration

Kubernetes

Docker

Git

DVC

MLflow

Kubeflow

TensorFlow Serving

Model Registry

Feature Engineering

Data Pipelines

Model Optimization

Hyperparameter Tuning

A/B Testing

Model Interpretability

Bias Detection

Model Governance

ML Pipelines

Continuous Training

Model Artifacts

Model Serialization

API Development

Scalable Inference

Model Compression

Transfer Learning

Ensemble Methods

Cross-Validation

Data Drift

Concept Drift

Model Decay

Logging

Monitoring Metrics

Alert Systems

Data Labeling

Data Augmentation

Data Cleaning

Feature Selection

Dimensionality Reduction

Model Explainability

Model Debugging

Performance Optimization

What roles can I use the MLOps Skills Test for?

MLOps Engineer
Machine Learning Engineer
Data Scientist
DevOps Engineer
AI Engineer
Cloud Machine Learning Engineer
ML Platform Engineer
Data Engineer
AI/ML Architect
ML Research Scientist

How is the MLOps Skills 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

Designing scalable ML infrastructures
Implementing A/B testing for ML models
Automating model retraining and updating
Implementing data drift detection mechanisms
Managing model serving with tools like TensorFlow Serving
Implementing distributed training for large-scale ML
Optimizing ML pipelines for cost and efficiency
Implementing model explainability techniques
Designing fault-tolerant ML systems
Implementing federated learning systems

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Non-googleable Questions

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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

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Have questions about the MLOps Skills Hiring Test?

What is the MLOps Test - mlops-skills-test?

The MLOps Test assesses a candidate's skills in Machine Learning Operations, including fundamentals, lifecycle, and deployment. It is designed for recruiters to evaluate technical expertise efficiently.

Can I combine the MLOps Test with the Machine Learning skills?

Yes, you can request a custom test including both MLOps and Machine Learning questions. For more details, check out our Machine Learning Assessment Test.

What topics are evaluated in the MLOps Test?

The test covers MLOps Fundamentals, Machine Learning Lifecycle, Model Deployment, CI/CD for ML, Model Monitoring, Data Version Control, Feature Engineering, Model Serving, ML Infrastructure, Experiment Tracking, Model Versioning, and Automated ML. Senior roles may expect topics like scalable ML infrastructure design, A/B testing for models, and cost optimization.

How to use the MLOps Test in my hiring process?

You can use this test at the beginning of your recruitment process. Add a link to your job post or invite candidates via email. Adaface helps find the most skilled candidates earlier in the process.

Can I test MLOps and Data Engineer skills together in a test?

Yes, testing both skills together is possible and recommended. Check out our Data Engineer Test for more information.

What are the main Machine Learning tests available?

We offer several tests in the Machine Learning category:

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.

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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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