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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

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

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

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Microsoft 070-774 Exam Syllabus Topics:

SectionObjectives
Topic 1: Evaluate and optimize models- Model tuning and improvement
  • 1. Hyperparameter tuning
    • 2. Feature selection and engineering
      - Model evaluation techniques
      • 1. ROC and AUC analysis
        • 2. Accuracy, precision, recall, F1 score
          Topic 2: Develop machine learning models- Select and implement algorithms
          • 1. Supervised learning models
            • 2. Unsupervised learning models
              - Training models in Azure Machine Learning
              • 1. Use Azure ML Studio experiments
                • 2. Train models using Python or R scripts
                  Topic 3: Prepare and process data for Azure Machine Learning- Data ingestion and storage
                  • 1. Connect to Azure storage services
                    • 2. Import data into Azure Machine Learning datasets
                      - Data preparation and transformation
                      • 1. Clean missing or inconsistent data
                        • 2. Transform features for modeling
                          Topic 4: Deploy and manage models- Model deployment in Azure
                          • 1. Batch inference pipelines
                            • 2. Deploy to Azure web services
                              - Operationalization
                              • 1. Monitor deployed models
                                • 2. Update and retrain models

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