Free Microsoft DP-100 Exam Questions

Question 1

You are implementing a machine learning model to predict stock prices. The model uses a PostgreSQL database and requires GPU processing. You need to create a virtual machine that is pre-configured with the required tools. What should you do?

A : Create a Data Science Virtual Machine (DSVM) Windows edition.

B : Create a Geo Al Data Science Virtual Machine (Geo-DSVM) Windows edition.

C : Create a Deep Learning Virtual Machine (DLVM) Linux edition.

D : Create a Deep Learning Virtual Machine (DLVM) Windows edition.

E : Create a Data Science Virtual Machine (DSVM) Linux edition.

Answer: E

Question 2

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 have a Python script named train.py in a local folder named scripts. The script trains a regression model

by using scikit-learn. The script includes code to load a training data file which is also located in the scripts

folder.

You must run the script as an Azure ML experiment on a compute cluster named aml-compute.

You need to configure the run to ensure that the environment includes the required packages for model

training. You have instantiated a variable named aml-compute that references the target compute cluster.

Solution: Run the following code:

Does the solution meet the goal?

A :

B :

Answer: B

Question 3

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?

A : 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 theexisting dataset and specifying a tag named month indicating the month and year it was registered. Usethis dataset for all experiments.

B : Create a tabular dataset that references the datastore and specifies the path 'sales/*/sales.csv', register thedataset with the name sales_dataset and a tag named month indicating the month and year it wasregistered, and use this dataset for all experiments.

C : 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 monthwith appropriate MM and YYYY values for the month and year. Use the appropriate month-specificdataset for experiments.

D : 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 atag 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.

Answer: B

Question 4

You are developing deep learning models to analyze semi-structured, unstructured, and structured data types. You have the following data available for model building: Video recordings of sporting events Transcripts of radio commentary about events Logs from related social media feeds captured during sporting events You need to select an environment for creating the model. Which environment should you use?

A : Azure Cognitive Services

B : Azure Data Lake Analytics

C : Azure HDInsight with Spark MLib

D : Azure Machine Learning Studio

Answer: A

Question 5

You create an Azure Machine Learning workspace. You must configure an event handler to send an email notification when data drift is detected in the workspace datasets. You must minimize development efforts. You need to configure an Azure service to send the notification. Which Azure service should you use?

A : Azure Function apps

B : Azure DevOps pipeline

C : Azure Automation runbook

D : Azure Logic Apps

Answer: A