070-774 exam dumps

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  • Exam Code: 070-774
  • Exam Name: Perform Cloud Data Science with Azure Machine Learning
  • No. of Questions: 65 Questions and Answers
  • Updated: Sep 06, 2026

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

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

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

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Difficulty in writing MICROSOFT 70-774 Exam

It is one of the toughest exam and it requires you a lot of hands-on experience. It is advisable to have prior knowledge of Data analytics on Microsoft. The more experience you have, the more it is beneficial for you, Major challenging things which you can find in exam is about different scenario based questions, you must have strong understanding of Big Data, Machine Learning, Data Science, Data Analytics.

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Microsoft 070-774 Exam Syllabus Topics:

SectionObjectives
Topic 1: Develop machine learning models- Select and implement algorithms
  • 1. Unsupervised learning models
    • 2. Supervised learning models
      - Training models in Azure Machine Learning
      • 1. Use Azure ML Studio experiments
        • 2. Train models using Python or R scripts
          Topic 2: 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 3: 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 4: Evaluate and optimize models- Model evaluation techniques
                          • 1. ROC and AUC analysis
                            • 2. Accuracy, precision, recall, F1 score
                              - Model tuning and improvement
                              • 1. Hyperparameter tuning
                                • 2. Feature selection and engineering

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