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Microsoft Azure AI Fundamentals (Retired and replaced with AI-901) Questions 96–100 (page: 21)

HOTSPOT (Drag and Drop is not supported)

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

Note: Each correct selection is worth one point.

Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:


Reference:

https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer/5- create-training-pipeline https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/ introduction https://docs.microsoft.com/en-us/learn/modules/create-clustering-model-azure-machine-learning-designer/1- introduction



Which two actions are performed during the data ingestion and data preparation stage of an Azure Machine Learning process? Each correct answer presents part of the solution.

Note: Each correct selection is worth one point.

  1. Calculate the accuracy of the model.
  2. Score test data by using the model.
  3. Combine multiple datasets.
  4. Use the model for real-time predictions.
  5. Remove records that have missing values.

Answer(s): C,E


Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/concept-data-ingestion https://docs.microsoft.com/en-us/azure/architecture/data-science-process/prepare-data



You need to predict the animal population of an area.

Which Azure Machine Learning type should you use?

  1. regression
  2. clustering
  3. classification

Answer(s): A

Explanation:

Regression is a supervised machine learning technique used to predict numeric values.


Reference:

https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning- designer/1-introduction



HOTSPOT (Drag and Drop is not supported)

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

Note: Each correct selection is worth one point.

Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:

Box 1: Yes
For regression problems, the label column must contain numeric data that represents the response variable.
Ideally the numeric data represents a continuous scale.

Box 2: No
K-Means Clustering
Because the K-means algorithm is an unsupervised learning method, a label column is optional.

If your data includes a label, you can use the label values to guide selection of the clusters and optimize the model.

If your data has no label, the algorithm creates clusters representing possible categories, based solely on the data.

Box 3: No
For classification problems, the label column must contain either categorical values or discrete values. Some examples might be a yes/no rating, a disease classification code or name, or an income group. If you pick a noncategorical column, the component will return an error during training.


Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/component-reference/train-model https://docs.microsoft.com/en-us/azure/machine-learning/component-reference/k-means-clustering



You use Azure Machine Learning designer to build a model pipeline.

What should you create before you can run the pipeline?

  1. a registered model
  2. a compute resource
  3. a Jupyter notebook

Answer(s): B

Explanation:

To train the model, we will create Azure Machine Learning Compute resource.


Reference:

https://github.com/solliancenet/azure-machine-learning-quickstarts/blob/master/aml-visual-interface/ README.md



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