Machine Learning, Cloud Applications, Anomaly Detection - Question Set 1- 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 a best practice for deploying machine learning models in cloud environments?

Avoiding performance monitoring

Making changes to the model without testing

Using CI/CD pipelines for deployment

Ignoring version control

In the context of clustering, what distinguishes supervised from unsupervised learning?

Supervised learning is faster than unsupervised learning

There is no difference between the two

Unsupervised learning requires labeled data

Supervised learning uses labeled data, unsupervised does not

Why is model monitoring important in cloud applications?

To ensure data is securely stored

To keep models running indefinitely

To track performance and identify model drift

To analyze user feedback

How can cloud resources be optimized for model training?

By utilizing auto-scaling and load balancing

By manually adjusting resources

By avoiding GPU usage

By using the same instance type for all tasks

What is the purpose of feature engineering in machine learning?

To create, transform, or select variables for better model performance

To visualize data trends

To deploy models in production

To select models for training

How does clustering help businesses understand customer behavior?

By generating random customer segments

By grouping similar customers for targeted strategies

By analyzing historical sales data only

By predicting future sales

Which of the following is a challenge in optimizing machine learning models for cloud deployment?

Finding enough data for training

Ensuring data privacy and security

Simplifying models to reduce accuracy

Creating overly complex models

What is a key benefit of using machine learning for anomaly detection?

It is faster than traditional methods

It can adapt to changes in data patterns

It only works with small datasets

It requires manual threshold setting

Which of the following statistical techniques is commonly used for forecasting?

Reinforcement learning

Regression analysis

Support Vector Machines

K-means clustering

10.

What defines a production-grade model?

A model that is simple and easy to implement

A model that is robust, scalable, and reliable for real-world applications

A model that requires constant human intervention

A model that is only used in research

Question Set 1- 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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