Microsoft AZ900 Exam Dumps|QAs Page -17

Microsoft AZ900 Exam Dumps post contains real and latest questions for Microsoft Azure Fundamentals.

Microsoft AZ900 Exam Dumps

Microsoft AZ900 Exam Dumps – QAs 81-85

Q81. You are creating a machine learning model. You have a dataset that contains null rows.
You need to use the Clean Missing Data module in Azure Machine Learning Studio to identify and resolve the null and missing data in the dataset.
Which parameter should you use?

  1. Replace with mean
  2. Remove entire column
  3. Remove entire row
  4. Hot Deck
  5. Custom substitution value
  6. Replace with mode
Correct Answer

3. Remove entire row

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

Q82. You plan to provision an Azure Machine Learning Basic edition workspace for a data science project.
You need to identify the tasks you will be able to perform in the workspace.
Which three tasks will you be able to perform? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

  1. Create a Compute Instance and use it to run code in Jupyter notebooks
  2. Create an Azure Kubernetes Service (AKS) inference cluster.
  3. Use the designer to train a model by dragging and dropping pre-defined modules.
  4. Create a tabular dataset that supports versioning.
  5. Use the Automated Machine Learning user interface to train a model.
Correct Answer

1. Create a Compute Instance and use it to run code in Jupyter notebooks
2. Create an Azure Kubernetes Service (AKS) inference cluster.
4. Create a tabular dataset that supports versioning.

Reference:
https://azure.microsoft.com/en-us/pricing/details/machine-learning/

Q83. A set of CSV files contains sales records. All the CSV files have the same data schema.
Each CSV file contains the sales record for a particular month and has the filename sales.csv. Each file is stored in a folder that indicates the month and year when the data was recorded. The folders are in an Azure blob container for which a datastore has been defined in an Azure Machine Learning workspace. The folders are organized in a parent folder named sales to create the following hierarchical structure:

az900_question 83

At the end of each month, a new folder with that month’s sales file is added to the sales folder.
You plan to use the sales data to train a machine learning model based on the following requirements:
✑ You must define a dataset that loads all of the sales data to date into a structure that can be easily converted to a dataframe.
✑ You must be able to create experiments that use only data that was created before a specific previous month, ignoring any data that was added after that month.
✑ You must register the minimum number of datasets possible.
You need to register the sales data as a dataset in Azure Machine Learning service workspace.
What should you do?

  1. Create a tabular dataset that references the datastore and explicitly specifies each ‘sales/mm-yyyy/sales.csv’ file every month. Register the dataset with the name sales_dataset each month, replacing the existing dataset and specifying a tag named month indicating the month and year it was registered. Use this dataset for all experiments.
  2. Create a tabular dataset that references the datastore and specifies the path ‘sales/*/sales.csv’, register the dataset with the name sales_dataset and a tag named month indicating the month and year it was registered, and use this dataset for all experiments.
  3. Create a new tabular dataset that references the datastore and explicitly specifies each ‘sales/mm-yyyy/sales.csv’ file every month. Register the dataset with the name sales_dataset_MM-YYYY each month with appropriate MM and YYYY values for the month and year. Use the appropriate month-specific dataset for experiments.
  4. Create a tabular dataset that references the datastore and explicitly specifies each ‘sales/mm-yyyy/sales.csv’ file. Register the dataset with the name sales_dataset each month as a new version and with a tag named month indicating the month and year it was registered. Use this dataset for all experiments, identifying the version to be used based on the month tag as necessary.
Correct Answer

2. Create a tabular dataset that references the datastore and specifies the path ‘sales/*/sales.csv’, register the dataset with the name sales_dataset and a tag named month indicating the month and year it was registered, and use this dataset for all experiments.

Q84. HOTSPOT –
You create an Azure Machine Learning compute target named ComputeOne by using the STANDARD_D1 virtual machine image.
ComputeOne is currently idle and has zero active nodes.
You define a Python variable named ws that references the Azure Machine Learning workspace. You run the following Python code:

az900_question 84-1

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:

az900_question 84-2
Correct Answer
az900_answer 84

Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.computetarget

Q85. DRAG DROP –
You are analyzing a raw dataset that requires cleaning.
You must perform transformations and manipulations by using Azure Machine Learning Studio.
You need to identify the correct modules to perform the transformations.
Which modules should you choose? To answer, drag the appropriate modules to the correct scenarios. Each module may be used once, more than once, or not at all.
You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Select and Place:

az900_question 85
Correct Answer
az900_answer 85

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/convert-to-indicator-values

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