nerdexam
Microsoft

DP-100 · Question #134

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 in stor

The correct answer is B. Create a tabular dataset that references the datastore and specifies the path 'sales/*/sales.csv',. Option B is correct because creating a tabular dataset with the wildcard path 'sales//sales.csv' dynamically includes all current and future monthly folders without modification. The '' wildcard matches any folder name under 'sales/', so when a new month's folder is added (e.g.,

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Question

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 in 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: 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?

Options

  • ACreate a tabular dataset that references the datastore and explicitly specifies each 'sales/mm-
  • BCreate a tabular dataset that references the datastore and specifies the path 'sales/*/sales.csv',
  • CCreate a new tabular dataset that references the datastore and explicitly specifies each
  • DCreate a tabular dataset that references the datastore and explicitly specifies each 'sales/mm-

How the community answered

(61 responses)
  • A
    8% (5)
  • B
    72% (44)
  • C
    3% (2)
  • D
    16% (10)

Explanation

Option B is correct because creating a tabular dataset with the wildcard path 'sales//sales.csv' dynamically includes all current and future monthly folders without modification. The '' wildcard matches any folder name under 'sales/', so when a new month's folder is added (e.g., sales/2026-05/), it is automatically included in the dataset on the next training run. Options A, C, and D all explicitly enumerate each 'sales/mm-yyyy/' path, which means the dataset definition must be manually updated each month - violating the requirement that new data is automatically incorporated. The wildcard path approach is the recommended pattern for time-partitioned data in Azure ML tabular datasets.

Topics

#Azure ML Datasets#Tabular Dataset#Wildcard Paths#Data Ingestion

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