Introduction
This is top class certification exam of Microsoft. It is designed by group of Microsoft advanced level official certification experts. It has covered all the aspects of depth in the technical questions and delivery methodology of official Microsoft certification by following the complete exam syllabus. This certification questions will give you the most realistic experience in the real world exam.
Exam Details:
This Exam contains multiple choices, multi selection and true or false questions.
Pass Percentage: 70
Exam Time Limit: 90 Minutes
Exam Syllabus:
Prepare
Data for Analysis in Azure Machine Learning and Export from Azure Machine
Learning
- Import
and export data to and from Azure Machine Learning
·
Import and export data to and from Azure Blob storage, import and
export data to and from Azure SQL Database, import and export data via Hive
Queries, import data from a website, import data from on-premises SQL
- Explore
and summarize data
·
Create univariate summaries, create multivariate summaries,
visualize univariate distributions, use existing Microsoft R or Python
notebooks for custom summaries and custom visualizations, use zip archives to
import external packages for R or Python
- Cleanse
data for Azure Machine Learning
·
Apply filters to limit a dataset to the desired rows, identify and
address missing data, identify and address outliers, remove columns and rows of
datasets
- Perform
feature engineering
·
Merge multiple datasets by rows or columns into a single dataset by
columns, merge multiple datasets by rows or columns into a single dataset by
rows, add columns that are combinations of other columns, manually select and
construct features for model estimation, automatically select and construct
features for model estimation, reduce dimensions of data through principal
component analysis (PCA), manage variable metadata, select standardized
variables based on planned analysis
Develop Machine Learning Models
- Select
an appropriate algorithm or method
·
Select an appropriate algorithm for predicting continuous label
data, select an appropriate algorithm for supervised versus unsupervised
scenarios, identify when to select R versus Python notebooks, identify an
appropriate algorithm for grouping unlabeled data, identify an appropriate
algorithm for classifying label data, select an appropriate ensemble
- Initialize
and train appropriate models
·
Tune hyperparameters manually; tune hyperparameters automatically;
split data into training and testing datasets, including using routines for
cross-validation; build an ensemble using the stacking method
- Validate
models
·
Score and evaluate models, select appropriate evaluation metrics
for clustering, select appropriate evaluation metrics for classification,
select appropriate evaluation metrics for regression, use evaluation metrics to
choose between Machine Learning models, compare ensemble metrics against base
models
Operationalize and Manage Azure
Machine Learning Services
- Deploy
models using Azure Machine Learning
·
Publish a model developed inside Azure Machine Learning, publish
an externally developed scoring function using an Azure Machine Learning
package, use web service parameters, create and publish a recommendation model,
create and publish a language understanding model
- Manage
Azure Machine Learning projects and workspaces
·
Create projects and experiments, add assets to a project, create
new workspaces, invite users to a workspace, switch between different
workspaces, create a Jupyter notebook that references an intermediate dataset
- Consume
Azure Machine Learning models
·
Connect to a published Machine Learning web service, consume a
published Machine Learning model programmatically using a batch execution
service, consume a published Machine Learning model programmatically using a
request response service, interact with a published Machine Learning model
using Microsoft Excel, publish models to the marketplace
- Consume
exemplar Cognitive Services APIs
·
Consume Vision APIs to process images, consume Language APIs to
process text, consume Knowledge APIs to create recommendations
Use Other Services for Machine
Learning
- Build
and use neural networks with the Microsoft Cognitive Toolkit
·
Use N-series VMs for GPU acceleration, build and train a
three-layer feed forward neural network, determine when to implement a neural
network
- Streamline
development by using existing resources
·
Clone template experiments from Cortana Intelligence Gallery, use
Cortana Intelligence Quick Start to deploy resources, use a data science VM for
streamlined development
- Perform
data sciences at scale by using HDInsights
·
Deploy the appropriate type of HDI cluster, perform exploratory
data analysis by using Spark SQL, build and use Machine Learning models with
Spark on HDI, build and use Machine Learning models using MapReduce, build and
use Machine Learning models using Microsoft R Server
- Perform
database analytics by using SQL Server R Services on Azure
·
Deploy a SQL Server 2016 Azure VM, configure SQL Server to allow
execution of R scripts, execute R scripts inside T-SQL statements
How this exam is useful to you?
This exam is intended for the people who are preparing for the 70-774 Perform Cloud Data Science with Azure Machine Learning Certification. This exam is prepared exclusively as per the real certification exam. This will definitely help to check your skills before attempting the main exam. Also it will give the complete idea how main exam will be.
This exam will evaluate your skills and will give you report on how far you are skillful in 70-774 Perform Cloud Data Science with Azure Machine Learning certification exam.
In case you face any issues during the exam or you have any queries, email us at [email protected]
Wish you all the best for your certification exam and we are always ready to help you to get certified and grow in your career.
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Exam Details:
This Exam contains multiple choices, multi selection and true or false questions.
Pass Percentage: 70
Exam Time Limit: 90 Minutes
Check the instructions before starting the exam:
Read the exam instructions carefully and follow them to avoid any issues while writing the exam.
· Check the exam time limit before you start the exam.
· It is highly recommended to have faster internet access.
· No power cut shall happen during the exam. Hence the power backup for your system/laptop.
· In case your system/laptop gets shut down abruptly due to power cut, internet connectivity issues or any other reasons, your exam will be ended and will be considered as one attempt completed.
· Exam browser window shall not be closed once exam starts otherwise exam will be ended abruptly and same with losing your attempt as well.
· While writing the exam, if you click on back button of the browser tab then your exam will be ended abruptly and attempt will be lost. Hence it is highly recommended not use to back button of the browser button while/after writing the exam.
· Once exam time is completed, exam will be submitted automatically. Hence it is recommended to keep checking the timer which will be available at the exam window.
In case you face any issues during the exam, please email us at [email protected]
Copy Rights:
These are exams are prepared exclusively by Vullam experts team. This is copy right content. Copying or distribution of any of the exam content will considered as the violation of the terms and conditions and strict action will be taken against him/her as per the law.
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Once after exam is submitted:
· Once exam is submitted, your result will be shown in the screen immediately.
· Result shows Pass/Fail along with the achieved score.
· In the result user can find the questions which were answered correct and wrong.
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· If the result is Pass then user will get the certificate of the exam to their registered email id.
In case you face any issues during the exam or you have any queries, email us at [email protected]
Wish you all the best for your certification exam and we are always ready to help you to get certified and grow in your career.
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