Mastering Machine Learning in Cloud Applications: Key Concepts and Techniques - Question Set 4 ( 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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Total Questions: The quiz consists of 10 questions.

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What is the purpose of data normalization in machine learning?

To eliminate missing values

To transform features to a common scale without distorting differences in ranges

To enhance the performance of clustering algorithms only

To increase the number of features

Which of the following is a common challenge when implementing machine learning models in the cloud?

Managing resource costs effectively

Difficulty in feature extraction

Inability to access data

Lack of available algorithms

What is the function of a confusion matrix in machine learning?

To visualize the distribution of features

To assess the performance of a classification model

To summarize clustering results

To calculate resource usage in the cloud

In the context of anomaly detection, what is a “false positive”?

Correctly identifying an anomaly

Detecting an anomaly that does not exist

Failing to detect an actual anomaly

Incorrectly identifying a normal instance as an anomaly

What is the main advantage of using cloud-based data storage for machine learning?

Fixed storage capacity

Limited access to data

Increased complexity of data retrieval

Scalability and accessibility from anywhere

What does "feature selection" aim to achieve in model development?

To simplify data preprocessing

To remove irrelevant or redundant features to improve model performance

To increase the number of features

To cluster similar features together

Which method is used to handle imbalanced datasets in classification problems?

Feature scaling

Data normalization

Cross-validation

Under-sampling or over-sampling techniques

How do cloud environments facilitate collaborative model development?

By requiring local installations

By providing shared resources and tools accessible from anywhere

By enforcing strict security protocols

By limiting access to data

What role does monitoring play in production-grade machine learning models?

It complicates model deployment

It helps ensure models are performing as expected and allows for timely adjustments

It increases model complexity

It eliminates the need for regular updates

10.

In the context of optimization, what is “gradient descent”?

A clustering technique

A method for scaling data

A technique for feature engineering

An algorithm for minimizing the loss function in machine learning

Question Set 4 ( 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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