Lesson 3 - Model Training

Which of the following are keys to model training?

• Data Preparation

• Data Management

• Data Handling

• Data Diversion

Multiple machine learning models can together improve the results. What kind of learning is this?

• Collective Learning

• Symbiotic Learning

• Ensemble Learning

• More Learning

Who contains resources for exploring, transforming, and managing data in Azure ML.

• Datasets

• Datastores

The data management process, includes...

• The use of datastores and datasets

• Data importing

• Versioning

• How to monitor for data drift

Which of the following could not be a feature of a T-Shirt

• Medium

• Colour

• Size

• None of the above

Which of these could be steps of feature engineering?

• Derive new features on the basis of existing features

• Applying a mathematical function to a pre-existing feature

• Training a separate ML model to create new features

• Fetching more data with wider set of features

Which dataset is supported in Microsoft Azure?

• Tabular Dataset

• Web URL (File Dataset)

• Graph Based Data set

• Folder Based Data set

Data Wrangling is ___________ process.

• Done according to performance, An iterative

• Done only once, non-iterative

Datasets helps in seamless integration with numerous machine learning task, such as:

• Consummation of Datastores in automated processes

• Data labelling

• Monitoring issues like data drift

• Access of datastore through inference process

Identify the correct sequence.

• Dataset Monitor -> Datastore -> Dataset

• Datastore -> Dataset Monitor -> Dataset

• Data Store -> Dataset -> Dataset Monitor