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Latest updated Free DP-100 Dumps | Amazing Pass Rate For DP-100 Exam | Top DP-100: Designing and Implementing a Data Science Solution on Azure

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q304-Q309):

NEW QUESTION # 304
You plan to implement an Azure Machine Learning solution. You have the following requirements:
* Run a Jupyter notebook to interactively tram a machine learning model.
* Deploy assets and workflows for machine learning proof of concept by using scripting rather than custom programming.
You need to select a development technique for each requirement
Which development technique should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 305
You create a workspace to include a compute instance by using Azure Machine Learning Studio.
You are developing a Python SDK v2 notebook in the workspace.
You need to use Intellisense in the notebook.
What should you do?

A. Run a %pip magic function on the compute instance.
B. Stop the compute instance.
C. Start the compute instance.
D. Run a !pip magic function on the compute instance.

Answer: C

Explanation:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-run-jupyter- notebooks?view=azureml-api-2

NEW QUESTION # 306
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. Yes
B. No

Answer: B

Explanation:
Explanation
Need to attach the mlvm virtual machine as a compute target in the Azure Machine Learning workspace.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target

NEW QUESTION # 307
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 train and register an Azure Machine Learning model.
You plan to deploy the model to an online endpoint.
You need to ensure that applications will be able to use the authentication method with a non- expiring artifact to access the model.
Solution: Create a managed online endpoint and set the value of its auth_mode parameter to key.
Deploy the model to the online endpoint.
Does the solution meet the goal?

A. No
B. Yes

Answer: B

Explanation:
When consuming an online endpoint from a client, you can use either a key or a token. Keys don't expire, tokens do.
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-authenticate-online- endpoint?view=azureml-api-2&tabs=python

NEW QUESTION # 308
You have an Azure Machine Learning workspace that contains a CPU-based compute cluster and an Azure Kubernetes Services (AKS) inference cluster. You create a tabular dataset containing data that you plan to use to create a classification model.
You need to use the Azure Machine Learning designer to create a web service through which client applications can consume the classification model by submitting new data and getting an immediate prediction as a response.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

Explanation:

Step 1: Create and start a Compute Instance
To train and deploy models using Azure Machine Learning designer, you need compute on which to run the training process, test the model, and host the model in a deployed service.
There are four kinds of compute resource you can create:
Compute Instances: Development workstations that data scientists can use to work with data and models.
Compute Clusters: Scalable clusters of virtual machines for on-demand processing of experiment code.
Inference Clusters: Deployment targets for predictive services that use your trained models.
Attached Compute: Links to existing Azure compute resources, such as Virtual Machines or Azure Databricks clusters.
Step 2: Create and run a training pipeline..
After you've used data transformations to prepare the data, you can use it to train a machine learning model.
Create and run a training pipeline
Step 3: Create and run a real-time inference pipeline
After creating and running a pipeline to train the model, you need a second pipeline that performs the same data transformations for new data, and then uses the trained model to inference (in other words, predict) label values based on its features. This pipeline will form the basis for a predictive service that you can publish for applications to use.
Reference:
https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/

NEW QUESTION # 309
......

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