What roles can I use the MLOps Skills Assessment Test for?

Here are few roles for which we recommend this test: 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

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 .

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: Machine Learning Assessment Test Machine Learning in AWS Test Machine Learning in Azure Test Machine Learning in GCP Test

MLOps Skills Test

Q: 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.

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.

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Screen candidates with a 40 mins test

Test duration: ~ 40 mins

Difficulty level: Moderate

Availability: Available as custom test

Questions:

9 ML Ops MCQs
6 Machine Learning MCQs

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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Adaface skill assessments measure on-the-job skills of candidates, providing employers with an accurate tool for screening potential hires.

Use the MLOps Skills Assessment Test to shortlist qualified candidates

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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Screen candidates with the highest quality 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	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	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	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	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	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	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	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	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	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	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	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	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?

Test candidates on core MLOps Skills Hiring Test topics

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.

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