The document provides an overview of the Exam DP 100: Designing and Implementing a Data Science Solution on Azure certification exam. It outlines that the exam contains 51 questions including 10 that cannot be skipped. Questions are scenario-based and change with each attempt. The passing score is 700 but can change. Question types include single choice, multiple choice, arrange in sequence, code/blank filling, and case studies. The exam starts with a case study that cannot be revisited. Prerequisites include intermediate Python skills in libraries like Pandas and experience in data science, machine learning, and deep learning topics. Useful sources and an example question are also provided.

1. Exam DP 100
Designing and Implementing a Data Science Solution
on Azure

2. Overview of Exam Pattern
● Total number of questions are 51. Including 10 questions that can not be
skipped.
● Also, It’s a tough certification and questions change. Kindly prepare well
before attempting.
● Scenario based questions which change every time. There is no specific
pattern, Microsoft changes the pattern in each and every attempt.
● There is Passing Score is 700 which can be change by proctor.

3. Type of Questions
● Single choice based on the scenario
● Multiple choice questions
● Arrange in right sequence type questions
● Complete the code fill in the blanks
● Case studies with multiple questions.
● Drag and drop
● Sequential answer based (Flow based) questions
● The exam started with the Case Study and once you complete answering all
the questions in this section and exit, you cannot go back and review them.

4. Prerequisite
● Python Intermediate Level (Pandas, Numpy, Matplotlib, Sklearn, Seaborn,
Pytorch, Tensorflow)
● Data Science, Machine Learning and Deep Learning With At Least Some
hands on Knowledge.
● Most Useful Topics Classification, Regression, Forecasting, Image
Classification and NLP.
● Focused Topics Model Selection/Train/Evaluation, EDA/Data Transformation,
Feature Engineering and Development Environment/Deployment.

5. Useful Sources and Links
● Azure ML Python sdk - https://github.com/MicrosoftDocs/mslearn-aml-labs/blob/master/labdocs/README.md
● Azure Kubernetes Service (AKS) - https://azure.microsoft.com/en-in/services/kubernetes-service/
● What is Kubernetes - https://azure.microsoft.com/en-in/topic/what-is-kubernetes/
● Azure Machine Learning SDK for Python - https://docs.microsoft.com/en-
us/python/api/overview/azure/ml/intro?view=azure-ml-py
● scikit-learn Tutorials - https://scikit-learn.org/stable/tutorial/index.html
● What are compute targets in Azure Machine Learning service -
https://docs.microsoft.com/en-gb/azure/machine-learning/concept-compute-target
● Exam Type questions : https://www.itexams.com/exam/DP-100

6. Which environment should you
use?
A. Azure Machine Learning
Service
B. Azure Machine Learning
Studio
C. Azure Databricks
D. Azure Kubernetes Service
(AKS) A
You plan to build a team data science environment. Data for
training models in machine learning
pipelines will be over 20 GB in size.
You have the following requirements:
-> Models must be built using Caffe2 or Chainer frameworks.
-> Data scientists must be able to use a data science
environment to build the machine learning pipelines and train
models on their personal devices in both connected and
disconnected network environments.
Personal devices must support updating machine learning
pipelines when connected to a network.
You need to select a data science environment.
Sample Question :Options:

7. Thank You !
Any Question ?
You Can Find me on @Gmail
gaurav.dubey@decisionpoint.in
@LinkedIn: https://www.linkedin.com/in/gaurav-dubey-0703/
Certificate Link:
https://www.youracclaim.com/badges/7e07d05c-2a0a-4020-90d8-
e384cfd60702/linked_in