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Free DP-100 Exam Questions - Microsoft DP-100 Exam

Microsoft DP-100 Exam

Designing and Implementing a Data Science Solution on Azure

Total Questions: 265

Based on Official Syllabus Topics of Actual Microsoft DP-100 Exam

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Understand the Actual DP-100 Exam Syllabus, Format, and Question Types

Get official information about the syllabus and format of the exam to set an effective study plan. This information helps you to know what type of questions and topics will appear in the Microsoft DP-100 exam. Don’t waste your time and concentrate on such learning content which is expected in the actual exam.

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Manage your daily routine to ensure that you have the proper time to study for the Microsoft Designing and Implementing a Data Science Solution on Azure exam every day. Sit in a calm environment and do hard work daily to cover the entire DP-100 exam syllabus. It is not possible to study one morning and pass the DP-100 exam the next day. If you want to get passing marks on the first attempt, prepare for the Microsoft DP-100 exam daily.

Microsoft DP-100 Questions


You are creating a new Azure Machine Learning pipeline using the designer.

The pipeline must train a model using data in a comma-separated values (CSV) file that is published on a

website. You have not created a dataset for this file.

You need to ingest the data from the CSV file into the designer pipeline using the minimal administrative effort.

Which module should you add to the pipeline in Designer?


You train and register a model in your Azure Machine Learning workspace.

You must publish a pipeline that enables client applications to use the model for batch inferencing. You must use a pipeline with a single ParallelRunStep step that runs a Python inferencing script to get predictions from the input data.

You need to create the inferencing script for the ParallelRunStep pipeline step.

Which two functions should you include? Each correct answer presents part of the solution.

NOTE: Each correct selection is worth one point.


You create a batch inference pipeline by using the Azure ML SDK. You run the pipeline by using the following code:

from azureml.pipeline.core import Pipeline

from azureml.core.experiment import Experiment

pipeline = Pipeline(workspace=ws, steps=[parallelrun_step])

pipeline_run = Experiment(ws, 'batch_pipeline').submit(pipeline)

You need to monitor the progress of the pipeline execution.

What are two possible ways to achieve this goal? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.


You create a deep learning model for image recognition on Azure Machine Learning service using GPU-based training.

You must deploy the model to a context that allows for real-time GPU-based inferencing.

You need to configure compute resources for model inferencing.

Which compute type should you use?


You create a datastore named training_data that references a blob container in an Azure Storage account. The blob container contains a folder named csv_files in which multiple comma-separated values (CSV) files are stored.

You have a script named train.py in a local folder named ./script that you plan to run as an experiment using an estimator. The script includes the following code to read data from the csv_files folder:

You have the following script.

You need to configure the estimator for the experiment so that the script can read the data from a data reference named data_ref that references the csv_files folder in the training_data datastore.

Which code should you use to configure the estimator?

Question: 1 Answer: D
Question: 2 Answer: A, D
Question: 3 Answer: D, E
Question: 4 Answer: B
Question: 5 Answer: B

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