Designing and Implementing a Data Science Solution on Azure (beta)

Here you have the best Microsoft DP-100 practice exam questions

  • You have 477 total questions to study from
  • Each page has 5 questions, making a total of 96 pages
  • You can navigate through the pages using the buttons at the bottom
  • This questions were last updated on December 2, 2024
Question 1 of 477

DRAG DROP -

You are planning to host practical training to acquaint staff with Docker for Windows.

Staff devices must support the installation of Docker.

Which of the following are requirements for this installation? Answer by dragging the correct options from the list to the answer area.

Select and Place:

    Correct Answer:

    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/ https://docs.docker.com/docker-for-windows/install/

Question 2 of 477

HOTSPOT -

Complete the sentence by selecting the correct option in the answer area.

Hot Area:

    Correct Answer:

    A Deep Learning Virtual Machine is a pre-configured environment for deep learning using GPU instances.

Question 3 of 477

You need to implement a Data Science Virtual Machine (DSVM) that supports the Caffe2 deep learning framework.

Which of the following DSVM should you create?

    Correct Answer: C

    C

    Caffe2 is supported by Data Science Virtual Machine for Linux.

    Microsoft offers Linux editions of the DSVM on Ubuntu 16.04 LTS and CentOS 7.4.

    However, only the DSVM on Ubuntu is preconfigured for Caffe2.

    Reference:

    https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview

Question 4 of 477

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 have been tasked with employing a machine learning model, which makes use of a PostgreSQL database and needs GPU processing, to forecast prices.

You are preparing to create a virtual machine that has the necessary tools built into it.

You need to make use of the correct virtual machine type.

Recommendation: You make use of a Geo AI Data Science Virtual Machine (Geo-DSVM) Windows edition.

Will the requirements be satisfied?

    Correct Answer: B

    The Geo AI Data Science Virtual Machine (Geo-DSVM) is designed specifically for geospatial analytics and includes tools such as ESRI's ArcGIS Pro. However, for the given task, the requirements involve employing a machine learning model that requires GPU processing and a PostgreSQL database. The Geo-DSVM may not necessarily have GPU support or the necessary configurations for PostgreSQL database management built-in. A more suitable choice would be a general Data Science Virtual Machine (DSVM) with GPU support and PostgreSQL installation capabilities. Therefore, using a Geo-DSVM Windows edition would not satisfy the given requirements.

Question 5 of 477

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 have been tasked with employing a machine learning model, which makes use of a PostgreSQL database and needs GPU processing, to forecast prices.

You are preparing to create a virtual machine that has the necessary tools built into it.

You need to make use of the correct virtual machine type.

Recommendation: You make use of a Deep Learning Virtual Machine (DLVM) Windows edition.

Will the requirements be satisfied?

    Correct Answer: B

    The recommendation to use a Deep Learning Virtual Machine (DLVM) Windows edition for the task does not satisfy the requirements. While the DLVM is optimized for deep learning tasks and includes GPU support, it is primarily configured for Windows-based environments. The PostgreSQL database is typically better supported on Linux-based virtual machines, as many of the tools and libraries for PostgreSQL are more readily available and better integrated on these platforms. Therefore, a more suitable option would be to use a virtual machine that runs on a Linux operating system, which would provide better support for PostgreSQL alongside the necessary GPU processing capabilities for the machine learning model.