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Microsoft 070-774 valid exam - in .pdf Free Demo

  • Exam Code: 070-774
  • Exam Name: Perform Cloud Data Science with Azure Machine Learning
  • Last Updated: Jul 31, 2026
  • Q & A: 65 Questions and Answers
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  • PDF Price: $49.98    

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  • Exam Code: 070-774
  • Exam Name: Perform Cloud Data Science with Azure Machine Learning
  • Last Updated: Jul 31, 2026
  • Q & A: 65 Questions and Answers
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ValidExam expert team recommend you to prepare some notes on these topics along with it don't forget to practice Perform Cloud Data Science with Azure Machine Learning 70-774 Dumps which been written by our expert team, Both these will help you a lot to clear this exam with good marks.

  • SQL/T-SQL
  • Microsoft R Servers.
  • Data Science
  • Azure Machine Learning.
  • Algorithms for grouping unlabeled Data.
  • Cognitive Services/API
  • Machine Learning Projects and workspace
  • HDinsights
  • Python Languages.
  • Machine Learning models

How to book MICROSOFT 70-774 Certifications Exam?

  • Select Date and Center of examination and confirm with payment value of 165$
  • Signup/Login to MICROSOFT account
  • Visit to Microsoft Exam Registration
  • Search for MICROSOFT 70-774 Certifications Exam

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: Prepare and process data for Azure Machine Learning- Data preparation and transformation
  • 1. Clean missing or inconsistent data
    • 2. Transform features for modeling
      - Data ingestion and storage
      • 1. Import data into Azure Machine Learning datasets
        • 2. Connect to Azure storage services
          Topic 2: Deploy and manage models- Operationalization
          • 1. Monitor deployed models
            • 2. Update and retrain models
              - Model deployment in Azure
              • 1. Deploy to Azure web services
                • 2. Batch inference pipelines
                  Topic 3: Develop machine learning models- Training models in Azure Machine Learning
                  • 1. Use Azure ML Studio experiments
                    • 2. Train models using Python or R scripts
                      - Select and implement algorithms
                      • 1. Supervised learning models
                        • 2. Unsupervised learning models
                          Topic 4: 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

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