MLOps_quiz1

What does MLOps stand for?

• Machine Learning Operations

• Machine Learning Optimization

• Machine Learning Overload

• 

Which team is usually involved in MLOps?

• Only data scientists

• Data scientists and MLOps engineer

• Only DevOps engineers

• Marketing team

What is "data drift" in MLOps?

• A sudden change in the data distribution that affects model performance

• A type of model improvement

• The process of cleaning the data

• A type of model testing

Which phase of MLOps focuses on defining the goals, requirements, and resources for the machine learning project?

• Model Development

• Operations

• Design

• Monitoring

Which of the following is a key benefit of MLOps?

• Faster and more reliable ML model deployment

• Manual tracking of ML experiments

• Reducing the need for model retraining

• Avoiding cloud-based ML solutions

What is the first step in the data engineering process for MLOps?

• Model training

• Data ingestion

• Data monitoring

• Model deployment

In MLOps, which of the following is an additional step compared to DevOps?

• Automated testing of code

• Model validation and testing

• Continuous Delivery

• Continuous Integration

What is data labeling in MLOps?

• The process of cleaning and transforming raw data before training a model

• Assigning meaningful tags, categories, or annotations to raw data to make it useful for machine learning models

• A method for optimizing machine learning model performance using hyperparameter tuning

• The process of monitoring model performance after deployment

What is data splitting in of MLOps?

• A technique for dividing data into multiple storage locations for better security

• The process of separating data into training, validation, and test sets for machine learning model evaluation

• The process of merging different datasets to increase model accuracy

• A method for labeling and annotating datasets for supervised learning

What is data wrangling in MLOps?

• The process of encrypting and securing data before using it in machine learning models

• Cleaning, transforming, and structuring raw data into a usable format for machine learning

• A method for labeling and annotating datasets for supervised learning

• A technique used to split data into training, validation, and test sets