Q. What does CI/CD stand for in the context of MLOps?
A.
Continuous Integration/Continuous Deployment
B.
Cyclic Integration/Cyclic Deployment
C.
Constant Improvement/Constant Development
D.
Collaborative Integration/Collaborative Deployment
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Solution
CI/CD stands for Continuous Integration and Continuous Deployment, which are practices used to automate the deployment of machine learning models.
Correct Answer:
A
— Continuous Integration/Continuous Deployment
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A.
A methodology for managing machine learning lifecycle
B.
A type of machine learning algorithm
C.
A programming language for AI
D.
A data preprocessing technique
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Solution
MLOps is a methodology that combines machine learning, DevOps, and data engineering to manage the lifecycle of machine learning models.
Correct Answer:
A
— A methodology for managing machine learning lifecycle
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Q. What is the primary goal of model monitoring in MLOps?
A.
To improve model accuracy
B.
To ensure model performance over time
C.
To reduce training time
D.
To automate data collection
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Solution
The primary goal of model monitoring is to ensure that the model continues to perform well over time and to detect any degradation in performance.
Correct Answer:
B
— To ensure model performance over time
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Q. What is the purpose of A/B testing in MLOps?
A.
To compare two versions of a model
B.
To train models faster
C.
To clean data
D.
To visualize model performance
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Solution
A/B testing is used to compare two versions of a model to determine which one performs better in a real-world scenario.
Correct Answer:
A
— To compare two versions of a model
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Q. What is the purpose of A/B testing in model deployment?
A.
To compare two versions of a model
B.
To train models faster
C.
To clean data
D.
To visualize model performance
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Solution
A/B testing is used to compare two versions of a model to determine which one performs better in a real-world scenario.
Correct Answer:
A
— To compare two versions of a model
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Q. What is the role of a feature store in MLOps?
A.
To store raw data
B.
To manage and serve features for ML models
C.
To deploy models
D.
To monitor model performance
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Solution
A feature store is used to manage and serve features for machine learning models, ensuring consistency and reusability.
Correct Answer:
B
— To manage and serve features for ML models
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Q. What is the role of feature engineering in MLOps?
A.
To improve model interpretability
B.
To enhance model performance
C.
To automate model training
D.
To reduce data size
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Solution
Feature engineering plays a crucial role in enhancing model performance by creating new features or modifying existing ones to better represent the underlying data.
Correct Answer:
B
— To enhance model performance
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Q. Which metric is commonly used to evaluate model performance in MLOps?
A.
Accuracy
B.
Mean Squared Error
C.
F1 Score
D.
All of the above
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Solution
All of the above metrics (Accuracy, Mean Squared Error, F1 Score) are commonly used to evaluate model performance in MLOps.
Correct Answer:
D
— All of the above
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Q. Which metric is commonly used to evaluate the performance of classification models?
A.
Mean Squared Error
B.
Accuracy
C.
Silhouette Score
D.
R-squared
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Solution
Accuracy is a common metric used to evaluate the performance of classification models, indicating the proportion of correct predictions.
Correct Answer:
B
— Accuracy
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Q. Which of the following best describes 'model drift'?
A.
A decrease in model accuracy over time
B.
The process of retraining a model
C.
The introduction of new features
D.
A method for optimizing model performance
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Solution
Model drift refers to a decrease in model accuracy over time, often due to changes in the underlying data distribution.
Correct Answer:
A
— A decrease in model accuracy over time
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Q. Which of the following is a challenge in MLOps?
A.
Data privacy and security
B.
Lack of data
C.
Overfitting models
D.
High computational cost
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Solution
Data privacy and security is a significant challenge in MLOps, especially when deploying models that handle sensitive information.
Correct Answer:
A
— Data privacy and security
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Q. Which of the following is a common challenge in MLOps?
A.
Data privacy regulations
B.
Lack of data
C.
Overfitting models
D.
All of the above
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Solution
All of the above are common challenges faced in MLOps, including data privacy regulations, lack of data, and overfitting models.
Correct Answer:
D
— All of the above
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Q. Which of the following is NOT a key component of MLOps?
A.
Continuous integration
B.
Model monitoring
C.
Data labeling
D.
Version control
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Solution
Data labeling is a part of the data preparation process, not a key component of MLOps.
Correct Answer:
C
— Data labeling
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Q. Which tool is commonly used for model deployment in MLOps?
A.
TensorFlow Serving
B.
Pandas
C.
NumPy
D.
Matplotlib
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Solution
TensorFlow Serving is a popular tool used for deploying machine learning models in production environments.
Correct Answer:
A
— TensorFlow Serving
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Q. Which tool is commonly used for version control in MLOps?
A.
Git
B.
Jupyter Notebook
C.
TensorFlow
D.
Pandas
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Solution
Git is widely used for version control in MLOps to track changes in code and model versions.
Correct Answer:
A
— Git
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