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Perform Cloud Data Science with Azure Machine Learning 70-774 Exam

Perform Cloud Data Science with Azure Machine Learning 70-774 Exam is centered around Azure Machine Learning, Bot Framework, and Cognitive Services. The exam is designed to target candidates who are Data Scientists or Analysts using the Microsoft Azure cloud services to build and deploy intelligent solutions. This exam will test your understanding and familiarity with common data science processes such as filtering and transforming data sets, model estimation, and model evaluation. Machine Learning is one of the hottest topics and technologies in the industry right now.

Machine Learning is being used to combine with Big Data to automate the processes of gaining insights and predicting future behavior of systems, devices, and people. In an effort to expand out the breadth of certifications and exams offered around Microsoft Azure and the cloud, Microsoft has added the Perform Cloud Data Science with Azure Machine Learning Microsoft 70-774 Exam. This exam only available for the candidates till 30th June 2019 after that this may get replace with some other exam so you can plan get it completed before it get expire.

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This is the list of the contents in our Perform Cloud Data Science with Azure Machine Learning 70-774

Perform data sciences at scale by using HD Insights
Explore and summarize data
Import and Export Data to and from ML.
Cleanse data for Azure Machine Learning
Validate models
Azure Machine Learning models

Overview about Overview about MICROSOFT 70-774 Exam

Format: Multiple choice, multiple answer
Passing Score: 70-80%
Number of Questions: 40-60
Length of Examination: 120 minutes
Registration Fee: 165 USD

Microsoft 070-774 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Use Other Services for Machine Learning	10%	- Integrate with HDInsight - Leverage Cortana Intelligence Gallery - Work with Microsoft Cognitive Toolkit - Use SQL Server R Services
Topic 2: Operationalize and Manage Azure Machine Learning Services	25%	- Manage workspaces and experiments 1. Share and collaborate 2. Create and organize projects - Consume models and APIs 1. Call deployed web services 2. Use Cognitive Services APIs - Deploy models as web services 1. Publish to Azure ML Studio 2. Configure endpoints and access
Topic 3: Prepare Data for Analysis in Azure Machine Learning	30%	- Perform feature engineering 1. Create derived features 2. Normalize and scale features - Import and export data 1. Connect to Azure storage and databases 2. Import from files and external sources 3. Export results and datasets - Cleanse and transform data 1. Handle missing values and outliers 2. Remove duplicates and inconsistent entries - Explore and summarize data 1. Generate statistical summaries 2. Visualize data distributions
Topic 4: Develop Machine Learning Models	35%	- Select appropriate algorithms 1. Unsupervised learning methods 2. Supervised learning methods - Validate and evaluate models 1. Perform cross-validation 2. Use accuracy, precision, recall, AUC - Train and tune models 1. Configure parameters 2. Split training and test sets

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