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Microsoft DP-100 Exam (page: 11)
Microsoft Designing and Implementing a Data Science Solution on Azure
Updated on: 15-Feb-2026

Viewing Page 11 of 102
DP-100 PDF 504 Questions

You are moving a large dataset from Azure Machine Learning Studio to a Weka environment.
You need to format the data for the Weka environment.
Which module should you use?

  1. Convert to CSV
  2. Convert to Dataset
  3. Convert to ARFF
  4. Convert to SVMLight

Answer(s): C

Explanation:

Use the Convert to ARFF module in Azure Machine Learning Studio, to convert datasets and results in Azure Machine Learning to the attribute-relation file format used by the Weka toolset. This format is known as ARFF.
The ARFF data specification for Weka supports multiple machine learning tasks, including data preprocessing, classification, and feature selection. In this format, data is organized by entites and their attributes, and is contained in a single text file.


Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/convert-to-arff



You plan to create a speech recognition deep learning model.
The model must support the latest version of Python.
You need to recommend a deep learning framework for speech recognition to include in the Data Science Virtual Machine (DSVM).
What should you recommend?

  1. Rattle
  2. TensorFlow
  3. Weka
  4. Scikit-learn

Answer(s): B

Explanation:

TensorFlow is an open-source library for numerical computation and large-scale machine learning. It uses Python to provide a convenient front-end API for building applications with the framework
TensorFlow can train and run deep neural networks for handwritten digit classification, image recognition, word embeddings, recurrent neural networks, sequence- to-sequence models for machine translation, natural language processing, and PDE (partial differential equation) based simulations.
Incorrect Answers:
A: Rattle is the R analytical tool that gets you started with data analytics and machine learning.
C: Weka is used for visual data mining and machine learning software in Java.
D: Scikit-learn is one of the most useful libraries for machine learning in Python. It is on NumPy, SciPy and matplotlib, this library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.


Reference:

https://www.infoworld.com/article/3278008/what-is-tensorflow-the-machine-learning-library-explained.html



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



Viewing Page 11 of 102



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