DP-100 Exam Dumps - Designing and Implementing a Data Science Solution on Azure

You are preparing to build a deep learning convolutional neural network model for image classification. You create a script to train the model using CUDA devices.

You must submit an experiment that runs this script in the Azure Machine Learning workspace.

The following compute resources are available:

a Microsoft Surface device on which Microsoft Office has been installed. Corporate IT policies prevent the installation of additional software

a Compute Instance named ds-workstation in the workspace with 2 CPUs and 8 GB of memory

an Azure Machine Learning compute target named cpu-cluster with eight CPU-based nodes

an Azure Machine Learning compute target named gpu-cluster with four CPU and GPU-based nodes

You need to specify the compute resources to be used for running the code to submit the experiment, and for running the script in order to minimize model training time.

Which resources should the data scientist use? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

You have an Azure Machine Learning workspace. You plan to tune model hyperparameters by using a sweep job.

You need to find a sampling method that supports early termination of low-performance jobs and continuous hyperpara meters.

Solution: Use the Sobol sampling method over the hyperpara meter space.

Does the solution meet the goal?

You have an Azure Machine Learning workspace named WS1.

You plan to use the Responsible Al dashboard to assess MLflow models that you will register in WS1.

You need to identify the library you should use to register the MLflow models.

Which library should you use?

You are determining if two sets of data are significantly different from one another by using Azure Machine Learning Studio.

Estimated values in one set of data may be more than or less than reference values in the other set of data. You must produce a distribution that has a constant Type I error as a function of the correlation.

You need to produce the distribution.

Which type of distribution should you produce?

You plan to build a team data science environment. Data for training models in machine learning pipelines will

be over 20 GB in size.

You have the following requirements:

Models must be built using Caffe2 or Chainer frameworks.

Data scientists must be able to use a data science environment to build the machine learning pipelines and train models on their personal devices in both connected and disconnected network environments.

Personal devices must support updating machine learning pipelines when connected to a network.

You need to select a data science environment.

Which environment should you use?

You have a multi-class image classification deep learning model that uses a set of labeled photographs. You create the following code to select hyperparameter values when training the model.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

NOTE: Each correct selection is worth one point.

You create a binary classification model. The model is registered in an Azure Machine Learning workspace. You use the Azure Machine Learning Fairness SDK to assess the model fairness.

You develop a training script for the model on a local machine.

You need to load the model fairness metrics into Azure Machine Learning studio.

What should you do?

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.