Free Microsoft DP-100 Exam Questions

Question 1

You create an Azure Machine Learning workspace. The workspace contains a dataset named sample.dataset, a compute instance, and a compute cluster. You must create a two-stage pipeline that will prepare data in the dataset and then train and register a model based on the prepared data. The first stage of the pipeline contains the following code:You need to identify the location containing the output of the first stage of the script that you can use as input for the second stage. Which storage location should you use?

A : workspaceblobstore datastore

B : workspacefi lest ore datastore

C : compute instance

Answer: C

Question 2

You plan to run a script as an experiment using a Script Run Configuration. The script uses modules from the

scipy library as well as several Python packages that are not typically installed in a default conda environment

You plan to run the experiment on your local workstation for small datasets and scale out the experiment by

running it on more powerful remote compute clusters for larger datasets.

You need to ensure that the experiment runs successfully on local and remote compute with the least

administrative effort.

What should you do?

A : Create and register an Environment that includes the required packages. Use this Environment for all experiment runs.

B : ways run the experiment with an Estimator by using the default packages.

C : not specify an environment in the run configuration for the experiment. Run the experiment by using the default environment.

D : eate a config.yaml file defining the conda packages that are required and save the file in the experiment folder.

E : eate a virtual machine (VM) with the required Python configuration and attach the VM as a compute target. Use this compute target for all experiment runs.

Answer: A

Question 3

An organization creates and deploys a multi-class image classification deep learning model that uses a set of labeled photographs. The software engineering team reports there is a heavy inferencing load for the prediction web services during the summer. The production web service for the model fails to meet demand despite having a fully-utilized compute cluster where the web service is deployed. You need to improve performance of the image classification web service with minimal downtime and minimal administrative effort. What should you advise the IT Operations team to do?

A : Increase the minimum node count of the compute cluster where the web service is deployed.

B : Create a new compute cluster by using larger VM sizes for the nodes, redeploy the web service to that cluster, and update the DNS registration for the service endpoint to point to the new cluster.

C : Increase the VM size of nodes in the compute cluster where the web service is deployed.

D : Increase the node count of the compute cluster where the web service is deployed.

Answer: D

Question 4

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.

An IT department creates the following Azure resource groups and resources:

The IT department creates an Azure Kubernetes Service (AKS)-based inference compute target named

aks-cluster in the Azure Machine Learning workspace.

You have a Microsoft Surface Book computer with a GPU. Python 3.6 and Visual Studio Code are installed.

You need to run a script that trains a deep neural network (DNN) model and logs the loss and accuracy

metrics.

Solution: Install the Azure ML SDK on the Surface Book. Run Python code to connect to the workspace and

then run the training script as an experiment on local compute.

A :

B :

Answer: B

Question 5

You create a multi-class image classification deep learning model that uses a set of labeled images. You

create a script file named train.py that uses the PyTorch 1.3 framework to train the model.

You must run the script by using an estimator. The code must not require any additional Python libraries to be

installed in the environment for the estimator. The time required for model training must be minimized.

You need to define the estimator that will be used to run the script.

Which estimator type should you use?

A : nsorFlow

B : Torch

C : Learn

D : timator

Answer: B