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Free DP-100 Exam Braindumps (page: 15)

Page 14 of 127
PDF with 512 Questions

You plan to use a Deep Learning Virtual Machine (DLVM) to train deep learning models using Compute Unified Device Architecture (CUDA) computations.
You need to configure the DLVM to support CUDA.
What should you implement?

  1. Solid State Drives (SSD)
  2. Computer Processing Unit (CPU) speed increase by using overclocking
  3. Graphic Processing Unit (GPU)
  4. High Random Access Memory (RAM) configuration
  5. Intel Software Guard Extensions (Intel SGX) technology

Answer(s): C

Explanation:

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


Reference:

https://azuremarketplace.microsoft.com/en-au/marketplace/apps/microsoft-ads.dsvm-deep-learning



You plan to use a Data Science Virtual Machine (DSVM) with the open source deep learning frameworks Caffe2 and PyTorch.
You need to select a pre-configured DSVM to support the frameworks.
What should you create?

  1. Data Science Virtual Machine for Windows 2012
  2. Data Science Virtual Machine for Linux (CentOS)
  3. Geo AI Data Science Virtual Machine with ArcGIS
  4. Data Science Virtual Machine for Windows 2016
  5. Data Science Virtual Machine for Linux (Ubuntu)

Answer(s): E

Explanation:

Caffe2 and PyTorch 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.
Only the DSVM on Ubuntu is preconfigured for Caffe2 and PyTorch.
Incorrect Answers:
D: Caffe2 and PytOCH are only supported in the Data Science Virtual Machine for Linux.


Reference:

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



HOTSPOT (Drag and Drop is not supported)
You are performing sentiment analysis using a CSV file that includes 12,000 customer reviews written in a short sentence format. You add the CSV file to Azure
Machine Learning Studio and configure it as the starting point dataset of an experiment. You add the Extract N-Gram Features from Text module to the experiment to extract key phrases from the customer review column in the dataset.
You must create a new n-gram dictionary from the customer review text and set the maximum n-gram size to trigrams.
What should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:



Vocabulary mode: Create
For Vocabulary mode, select Create to indicate that you are creating a new list of n-gram features.
N-Grams size: 3
For N-Grams size, type a number that indicates the maximum size of the n-grams to extract and store. For example, if you type 3, unigrams, bigrams, and trigrams will be created.
Weighting function: Leave blank
The option, Weighting function, is required only if you merge or update vocabularies. It specifies how terms in the two vocabularies and their scores should be weighted against each other.


Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/extract-n-gram-features-from-text



You are developing a data science workspace that uses an Azure Machine Learning service.
You need to select a compute target to deploy the workspace.
What should you use?

  1. Azure Data Lake Analytics
  2. Azure Databricks
  3. Azure Container Service
  4. Apache Spark for HDInsight

Answer(s): B

Explanation:

Azure Container Instances can be used as compute target for testing or development. Use for low-scale CPU-based workloads that require less than 48 GB of
RAM.


Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-deploy-and-where






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