1. What is MLOps?
A. Machine Learning Optimization
B. Machine Learning Operations
C. Machine Learning Options
D. Machine Learning Outreach
Answer: B. Machine Learning Operations
2. Which phase of the machine learning lifecycle does MLOps primarily focus on?
A. Data preprocessing
B. Model training
C. Model deployment
D. Model evaluation
Answer: C. Model deployment
3. What is the primary goal of MLOps?
A. Optimizing machine learning algorithms
B. Enhancing data visualization
C. Streamlining and automating the machine learning lifecycle
D. Improving model interpretability
Answer: C. Streamlining and automating the machine learning lifecycle
4.What is the role of DevOps in MLOps?
A. Developing machine learning models
B. Deploying machine learning models
C. Managing machine learning datasets
D. Integrating machine learning with software development
Answer: D. Integrating machine learning with software development
5. What does CI/CD stand for in the context of MLOps?
A. Continuous Integration/Continuous Deployment
B. Continuous Iteration/Continuous Deployment
C. Continuous Inspection/Continuous Deployment
D. Continuous Integration/Continuous Delivery
Answer: A. Continuous Integration/Continuous Deployment
6. Which of the following is a key challenge in MLOps?
A. Model training
B. Model evaluation
C. Model interpretation
D. Model reproducibility
Answer: D. Model reproducibility
7. Which tool is commonly used for version control in MLOps?
A. Jenkins
B. Git
C. Docker
D. Kubernetes
Answer: B. Git
8. What is A/B testing in the context of MLOps?
A. Comparing machine learning models based on accuracy
B. Testing the performance of models using different datasets
C. Experimenting with model variations and selecting the best-performing one
D. Validating the correctness of machine learning algorithms
Answer: C. Experimenting with model variations and selecting the best-performing one
9. Which technique is used for automatically scaling machine learning models based on demand?
A. AutoML
B. Hyperparameter tuning
C. Reinforcement learning
D. Ensemble learning
Answer: A. AutoML
10. Which technology is commonly used for containerization in MLOps?
A. Docker
B. Kubernetes
C. Ansible
D. Vagrant
Answer: A. Docker
11. What is the purpose of a model registry in MLOps?
A. Storing training data
B. Storing machine learning models and their versions
C. Managing feature engineering
D. Optimizing model performance
Answer: B. Storing machine learning models and their versions
12. What does the acronym "MLCI" stand for in MLOps?
A. Machine Learning Continuous Integration
B. Model Lifecycle Integration
C. Machine Learning Continuous Interpretation
D. Model Lifecycle Continuation
Answer: A. Machine Learning Continuous Integration
13. What is the main advantage of using Kubernetes in MLOps?
A. Model training optimization
B. Model versioning
C. Efficient model deployment and scaling
D. Data preprocessing automation
Answer: C. Efficient model deployment and scaling
14. What is the purpose of monitoring and logging in MLOps?
A. Tracking model accuracy
B. Tracking model training time
C. Identifying and troubleshooting model issues
D. Optimizing hyperparameters
Answer: C. Identifying and troubleshooting model issues
15. Which of the following is not a key component of MLOps infrastructure?
A. Data preprocessing tools
B. Model evaluation frameworks
C. Model serving systems
D. Model visualization libraries
Answer: D. Model visualization libraries
16. What is the role of metadata in MLOps?
A. Storing model predictions
B. Storing information about model versions, configurations, and training data
C. Storing training datasets
D. Storing evaluation metrics
Answer: B. Storing information about model versions, configurations, and training data
17. What is the purpose of feature engineering in MLOps?
A. Selecting the best machine learning algorithm
B. Preprocessing and transforming raw data into meaningful features for model training
C. Fine-tuning hyperparameters
D. Validating the model's performance
Answer: B. Preprocessing and transforming raw data into meaningful features for model training
18. Which of the following is an important aspect of MLOps regarding compliance and ethics?
A. Model accuracy
B. Model interpretability and fairness
C. Model speed
D. Model hyperparameters
Answer: B. Model interpretability and fairness
19. Which method is used for deploying machine learning models as RESTful APIs?
A. Flask
B. Django
C. Node.js
D. Ruby on Rails
Answer: A. Flask
20. Which programming language is commonly used for implementing machine learning models in MLOps?
A. Java
B. Python
C. C++
D. Ruby
Answer: B. Python
21. What is the main purpose of a model catalog in MLOps?
A. Storing machine learning libraries
B. Storing information about available models and their versions
C. Storing training data
D. Storing model hyperparameters
Answer: B. Storing information about available models and their versions
22. What is the main goal of model versioning in MLOps?
A. Tracking changes in model code
B. Tracking changes in model hyperparameters
C. Tracking changes in model performance over time
D. Tracking changes in training data
Answer: C. Tracking changes in model performance over time
23. Which technique is used for automating the selection of the best machine learning model and hyperparameters?
A. Grid search
B. Random search
C. K-nearest neighbors
D. Linear regression
Answer: A. Grid search
24. Which of the following is a key consideration for deploying machine learning models in a cloud environment?
A. Model interpretability
B. Model visualization
C. Scalability and elasticity
D. Model hyperparameters
Answer: C. Scalability and elasticity
25. What is the purpose of model orchestration in MLOps?
A. Coordinating model training and evaluation
B. Storing model versions
C. Automating model deployment
D. Managing feature engineering
Answer: A. Coordinating model training and evaluation
26. Which technique is used for ensuring data quality and consistency in MLOps?
A. Data augmentation
B. Data validation
C. Data preprocessing
D. Data visualization
Answer: B. Data validation
27. What is the main goal of a canary release in MLOps?
A. Comparing different machine learning algorithms
B. Evaluating model performance in a controlled environment
C. Optimizing model hyperparameters
D. Automatically selecting the best-performing model
Answer: B. Evaluating model performance in a controlled environment
28. What is the purpose of a model cache in MLOps?
A. Storing training data
B. Storing intermediate model results for faster access
C. Storing evaluation metrics
D. Storing model hyperparameters
Answer: B. Storing intermediate model results for faster access
29. Which tool is commonly used for continuous integration in MLOps?
A. Jenkins
B. GitLab
C. Ansible
D. CircleCI
Answer: A. Jenkins
30. What is the primary objective of feedback loops in MLOps?
A. Continuously updating model hyperparameters
B. Gathering feedback from users on model predictions
C. Continuously retraining models based on new data
D. Fine-tuning model visualization
Answer: C. Continuously retraining models based on new data
Top comments (0)