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Microsoft 070-774

070-774

Exam Code: 070-774

Exam Name: Perform Cloud Data Science with Azure Machine Learning

Updated: Sep 30, 2026

Q&A Number: 65 Q&As

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About Microsoft 070-774 Exam dumps / Bootcamp

Benefits of having MICROSOFT 70-774 Certifications

MICROSOFT 70-774 Exam certification will not only help you learn some new skills, it can position you for a higher paying job or help you transform your current role into Data Scientist Professional. Get prepared to take this exam and become one of the very first Microsoft Azure Machine Learning Data Scientist Expert in the world by using our relevance quality of MICROSOFT 70-774 Exam study material and so we bring best-in-industry MICROSOFT 70-774 Exam online course and practice exams for you to help in your exam preparation.

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

For more info visit:

MICROSOFT 70-774 Exam Reference, Microsoft Documents

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Microsoft 070-774 Exam Syllabus Topics:

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

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