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Microsoft 070-774 Exam : Perform Cloud Data Science with Azure Machine Learning

070-774 actual test
  • Exam Code: 070-774
  • Exam Name: Perform Cloud Data Science with Azure Machine Learning
  • Updated: Sep 28, 2026
  • Q & A: 65 Questions and Answers
  • PDF Demo
  • PC Test Engine
  • Online Test Engine
  • Total Price: $49.99  

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

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Microsoft 070-774 Exam Syllabus Topics:

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

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