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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

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

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Analyzing Big Data with Microsoft R 70-773 Dumps Provided Study Notes

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  • Microsoft R Servers.
  • Data Science
  • Machine Learning models
  • SQL/T-SQL
  • Azure Machine Learning.
  • HDinsights
  • Algorithms for grouping unlabeled Data.
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  • Python Languages.

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Perform Cloud Data Science with Azure Machine learning 70-774 Dumps will include below mentioned topics with Exam focused percentage

  • Prepare Data for Analysis in Azure Machine Learning and Export from Azure Machine Learning: 20-25%
  • Develop Machine Learning Models: 20-25%
  • Use Other Services for Machine Learning: 20-25%
  • Operationalize and Manage Azure Machine Learning Services: 20-25%

Reference: https://www.microsoft.com/en-us/learning/exam-70-774.aspx

Microsoft 070-774 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Operationalize and Manage Azure Machine Learning Services25%- Manage workspaces and experiments
  • 1. Share and collaborate
    • 2. Create and organize projects
      - Deploy models as web services
      • 1. Publish to Azure ML Studio
        • 2. Configure endpoints and access
          - Consume models and APIs
          • 1. Use Cognitive Services APIs
            • 2. Call deployed web services
              Topic 2: Use Other Services for Machine Learning10%- Leverage Cortana Intelligence Gallery
              - Use SQL Server R Services
              - Work with Microsoft Cognitive Toolkit
              - Integrate with HDInsight
              Topic 3: Develop Machine Learning Models35%- Validate and evaluate models
              • 1. Use accuracy, precision, recall, AUC
                • 2. Perform cross-validation
                  - Train and tune models
                  • 1. Split training and test sets
                    • 2. Configure parameters
                      - Select appropriate algorithms
                      • 1. Unsupervised learning methods
                        • 2. Supervised learning methods
                          Topic 4: Prepare Data for Analysis in Azure Machine Learning30%- Import and export data
                          • 1. Import from files and external sources
                            • 2. Export results and datasets
                              • 3. Connect to Azure storage and databases
                                - Perform feature engineering
                                • 1. Normalize and scale features
                                  • 2. Create derived features
                                    - Explore and summarize data
                                    • 1. Visualize data distributions
                                      • 2. Generate statistical summaries
                                        - Cleanse and transform data
                                        • 1. Remove duplicates and inconsistent entries
                                          • 2. Handle missing values and outliers
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