QuizMania Question Set 2 ( 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 goal of using forecasting models in cloud applications?

To predict future trends based on historical data

To detect anomalies in real-time

To cluster similar data points

To visualize historical data

Which of the following techniques is most commonly used for detecting anomalies in time-series data?

Decision Trees

Linear Regression

K-means Clustering

ARIMA

What does the term "model drift" refer to?

The process of optimizing a model’s hyperparameters

The change in model parameters during training

The gradual degradation of model performance due to changes in underlying data distribution

The integration of new features into an existing model

Which of the following is a common method for optimizing hyperparameters?

Random Search

Naive Bayes

Linear Programming

Clustering

What is the purpose of cross-validation in machine learning?

To improve the training speed

To cluster similar data points

To assess how the results of a statistical analysis will generalize to an independent dataset

To eliminate the need for feature selection

In cloud-based anomaly detection, what role do thresholds play?

They define limits for identifying anomalies

They complicate the anomaly detection process

They are not necessary in machine learning models

They eliminate the need for monitoring

Which machine learning algorithm is often used for optimizing resource allocation in cloud applications?

K-means

Reinforcement Learning

Linear Regression

Naive Bayes

How can clustering algorithms assist in customer segmentation?

By eliminating outliers

By predicting future sales

By reducing model complexity

By identifying distinct customer groups based on similar attributes

Which of the following techniques can help improve the accuracy of forecasting models?

Limiting data to the most recent observations

Using ensemble methods

Reducing the number of features

Ignoring outliers

10.

What is a common challenge when deploying machine learning models in cloud environments?

Ensuring data privacy and compliance

Overfitting on training data

Lack of available algorithms

Limited data storage