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Microsoft DP-100 Exam (page: 9)
Microsoft Designing and Implementing a Data Science Solution on Azure
Updated on: 15-Feb-2026

Viewing Page 9 of 102
DP-100 PDF 504 Questions

This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.
You are planning to make use of Azure Machine Learning designer to train models.
You need choose a suitable compute type.
Recommendation: You choose Compute cluster.
Will the requirements be satisfied?

  1. Yes
  2. No

Answer(s): A

Explanation:


Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-studio



You are making use of the Azure Machine Learning to designer construct an experiment.
After dividing a dataset into training and testing sets, you configure the algorithm to be Two-Class Boosted Decision Tree.
You are preparing to ascertain the Area Under the Curve (AUC).
Which of the following is a sequential combination of the models required to achieve your goal?

  1. Train, Score, Evaluate.
  2. Score, Evaluate, Train.
  3. Evaluate, Export Data, Train.
  4. Train, Score, Export Data.

Answer(s): A



DRAG DROP (Drag and Drop is not supported)

You create an Azure Machine Learning workspace.
You must implement dedicated compute for model training in the workspace by using Azure Synapse compute resources. The solution must attach the dedicated compute and start an Azure Synapse session.
You need to implement the computer resources.
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.

  1. See Explanation section for answer.

Answer(s): A

Explanation:



You are developing a hands-on workshop to introduce Docker for Windows to attendees.
You need to ensure that workshop attendees can install Docker on their devices.
Which two prerequisite components should attendees install on the devices? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

  1. Microsoft Hardware-Assisted Virtualization Detection Tool
  2. Kitematic
  3. BIOS-enabled virtualization
  4. VirtualBox
  5. Windows 10 64-bit Professional

Answer(s): C,E

Explanation:

C: Make sure your Windows system supports Hardware Virtualization Technology and that virtualization is enabled.
Ensure that hardware virtualization support is turned on in the BIOS settings. For example:

E: To run Docker, your machine must have a 64-bit operating system running Windows 7 or higher.


Reference:

https://docs.docker.com/toolbox/toolbox_install_windows/
https://blogs.technet.microsoft.com/canitpro/2015/09/08/step-by-step-enabling-hyper-v-for-use-on-windows-10/



Your team is building a data engineering and data science development environment.
The environment must support the following requirements:
-support Python and Scala
-compose data storage, movement, and processing services into automated data pipelines
-the same tool should be used for the orchestration of both data engineering and data science
-support workload isolation and interactive workloads
-enable scaling across a cluster of machines
You need to create the environment.
What should you do?

  1. Build the environment in Apache Hive for HDInsight and use Azure Data Factory for orchestration.
  2. Build the environment in Azure Databricks and use Azure Data Factory for orchestration.
  3. Build the environment in Apache Spark for HDInsight and use Azure Container Instances for orchestration.
  4. Build the environment in Azure Databricks and use Azure Container Instances for orchestration.

Answer(s): B

Explanation:

In Azure Databricks, we can create two different types of clusters.
- Standard, these are the default clusters and can be used with Python, R, Scala and SQL
- High-concurrency
Azure Databricks is fully integrated with Azure Data Factory.
Incorrect Answers:
D: Azure Container Instances is good for development or testing. Not suitable for production workloads.


Reference:

https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/data-science-and-machine-learning



Viewing Page 9 of 102



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