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4. Models Deployment and Consumption (20-25%):

Model-as-a-Service deployment: This subtopic will measure the individuals’ expertise in configuring deployment settings, troubleshooting issues with deployment containers, and consuming deployed services.
Production computes targets creation: The test takers should perform their skills in compute options evaluation for deployment and consideration of security for deployed services.
Creation of pipelines for batch inferencing: This subject area covers your competence in running batch inferencing pipelines and obtaining outputs as well as publishing batch inferencing pipelines.
Designer pipeline publishing as a web service: The candidates should show their knowledge of target compute resources creation, inference pipelines configuration, and deployed endpoints consuming.

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Microsoft DP-100 certification exam covers a wide range of topics related to data science and machine learning. It requires candidates to have a deep understanding of Azure data services, including Azure Machine Learning, Azure Stream Analytics, and Azure Data Factory. DP-100 Exam also tests candidates' knowledge of statistical analysis, data visualization, and data exploration techniques.

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q268-Q273):

NEW QUESTION # 268
You manage an Azure Machine Learning workspace.
You must define the execution environments for your jobs and encapsulate the dependencies for your code.
You need to configure the environment from a Docker build context.
How should you complete the rode segment? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

NEW QUESTION # 269
You write five Python scripts that must be processed in the order specified in Exhibit A - which allows the same modules to run in parallel, but will wait for modules with dependencies.
You must create an Azure Machine Learning pipeline using the Python SDK, because you want to script to create the pipeline to be tracked in your version control system. You have created five PythonScriptSteps and have named the variables to match the module names.

You need to create the pipeline shown. Assume all relevant imports have been done.
Which Python code segment should you use?

A. Option B
B. Option A
C. Option C
D. Option D

Answer: B

Explanation:
The steps parameter is an array of steps. To build pipelines that have multiple steps, place the steps in order in this array.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-parallel-run-step

NEW QUESTION # 270
You create an Azure Machine Learning workspace. You are training a classification model with no-code AutoML in Azure Machine Learning studio.
The model must predict if a client of a financial institution will subscribe to a fixed-term deposit. You must preview the data profile in Azure Machine Learning studio once the dataset is created.
You need to train the model.
Which four 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:

1 - Create a file dataset.
2 - Create a compute cluster.
3 - Create an experiment.
4 - Create an automated ML job.

NEW QUESTION # 271
You create an Azure Machine Learning workspace and an Azure Synapse Analytics workspace with a Spark pool. The workspaces are contained within the same Azure subscription.
You must manage the Synapse Spark pool from the Azure Machine Learning workspace.
You need to attach the Synapse Spark pool in Azure Machine Learning by usinq the Python SDK v2.
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:

Explanation:

NEW QUESTION # 272
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. Always run the experiment with an Estimator by using the default packages.
B. Create 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.
C. Create and register an Environment that includes the required packages. Use this Environment for all experiment runs.
D. Create a config.yaml file defining the conda packages that are required and save the file in the experiment folder.
E. Do not specify an environment in the run configuration for the experiment. Run the experiment by using the default environment.

Answer: B

NEW QUESTION # 273
......

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