Anomaly Detection and Forecasting in Cloud Application Question Set 3 ( Scenario 1 : Research and Development of Production-Grade Models for Cloud Applications Using Statistical and Machine Learning Techniques in Forecasting, Anomaly Detection, Optimization, and Clustering )

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What is the primary purpose of using anomaly detection in cloud applications?

To optimize cloud resource usage

To identify unusual patterns that could indicate security threats or system failures

To enhance user experience

To automate data entry

Which statistical method is often used for time series forecasting in cloud applications?

Logistic Regression

ARIMA

Decision Trees

K-means Clustering

What is the function of regularization in machine learning models?

To speed up training

To prevent overfitting by adding a penalty for complexity

To enhance data quality

To improve model interpretability

Which of the following is NOT a technique used in clustering algorithms?

Dimensionality Reduction

Hierarchical clustering

DBSCAN

K-means

What does "hyperparameter tuning" involve?

Training the model on a different dataset

Adjusting model weights during training

Selecting the best parameters before model training

Implementing automatic scaling in cloud resources

Which cloud service model is best suited for deploying machine learning applications?

Platform as a Service (PaaS)

Function as a Service (FaaS)

Software as a Service (SaaS)

Infrastructure as a Service (IaaS)

In the context of forecasting, what is meant by "seasonality"?

Regular patterns that repeat over specific intervals

Random fluctuations in data

Long-term trends in data

Irregular patterns without discernible order

Which of the following metrics is commonly used to evaluate clustering performance?

Mean Absolute Error (MAE)

F1 Score

Silhouette Score

Accuracy

What is the main advantage of using ensemble methods in model development?

They eliminate the need for feature selection

They reduce the risk of overfitting by combining multiple models

They are easier to implement

They require less computational power

10.

How does the cloud enhance the scalability of machine learning models?

By allowing dynamic allocation of resources based on demand

By reducing the complexity of algorithms

By eliminating the need for monitoring

By providing unlimited storage