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Free DP-100 Exam Braindumps (page: 22)

Page 21 of 127
PDF with 512 Questions

You are creating a new experiment in Azure Machine Learning Studio. You have a small dataset that has missing values in many columns. The data does not require the application of predictors for each column. You plan to use the Clean Missing Data.
You need to select a data cleaning method.
Which method should you use?

  1. Replace using Probabilistic PCA
  2. Normalization
  3. Synthetic Minority Oversampling Technique (SMOTE)
  4. Replace using MICE

Answer(s): A

Explanation:

Replace using Probabilistic PCA: Compared to other options, such as Multiple Imputation using Chained Equations (MICE), this option has the advantage of not requiring the application of predictors for each column. Instead, it approximates the covariance for the full dataset. Therefore, it might offer better performance for datasets that have missing values in many columns.


Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data



You use Azure Machine Learning Studio to build a machine learning experiment.
You need to divide data into two distinct datasets.
Which module should you use?

  1. Split Data
  2. Load Trained Model
  3. Assign Data to Clusters
  4. Group Data into Bins

Answer(s): A

Explanation:

The Group Data into Bins module supports multiple options for binning data. You can customize how the bin edges are set and how values are apportioned into the bins.


Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins



You are a lead data scientist for a project that tracks the health and migration of birds. You create a multi-class image classification deep learning model that uses a set of labeled bird photographs collected by experts.
You have 100,000 photographs of birds. All photographs use the JPG format and are stored in an Azure blob container in an Azure subscription.
You need to access the bird photograph files in the Azure blob container from the Azure Machine Learning service workspace that will be used for deep learning model training. You must minimize data movement.
What should you do?

  1. Create an Azure Data Lake store and move the bird photographs to the store.
  2. Create an Azure Cosmos DB database and attach the Azure Blob containing bird photographs storage to the database.
  3. Create and register a dataset by using TabularDataset class that references the Azure blob storage containing bird photographs.
  4. Register the Azure blob storage containing the bird photographs as a datastore in Azure Machine Learning service.
  5. Copy the bird photographs to the blob datastore that was created with your Azure Machine Learning service workspace.

Answer(s): D

Explanation:

We recommend creating a datastore for an Azure Blob container. When you create a workspace, an Azure blob container and an Azure file share are automatically registered to the workspace.


Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-access-data



Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are analyzing a numerical dataset which contains missing values in several columns.
You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set.
You need to analyze a full dataset to include all values.
Solution: Calculate the column median value and use the median value as the replacement for any missing value in the column.
Does the solution meet the goal?

  1. Yes
  2. No

Answer(s): A

Explanation:

Use the Multiple Imputation by Chained Equations (MICE) method.


Reference:

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074241/
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data






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