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Microsoft 070-774 certification exam is designed for individuals who are looking to validate their skills in performing cloud data science using Azure Machine Learning. Perform Cloud Data Science with Azure Machine Learning certification is ideal for data scientists, data engineers, and other professionals who are looking to enhance their knowledge and skills in cloud-based machine learning.

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Microsoft 070-774 exam is intended for professionals who work with data science, machine learning, or artificial intelligence in organizations that use Azure. This includes data scientists, machine learning engineers, artificial intelligence developers, and data analysts. 070-774 exam is also suitable for professionals who want to demonstrate their knowledge of Azure Machine Learning to potential employers. Regardless of your role, passing 070-774 exam will provide you with a strong foundation in Azure Machine Learning and give you a competitive edge in the job market.

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Microsoft 070-774 Certification Exam is a valuable credential for professionals who want to demonstrate their expertise in using Azure Machine Learning for cloud-based data science solutions. It covers essential topics such as machine learning model design and implementation, deployment and management, and data manipulation in Azure. With this certification, professionals can enhance their career prospects and demonstrate their commitment to staying up-to-date with the latest advancements in data science and machine learning.

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: Use Other Services for Machine Learning10%- Use SQL Server R Services
- Work with Microsoft Cognitive Toolkit
- Leverage Cortana Intelligence Gallery
- Integrate with HDInsight
Topic 2: Develop Machine Learning Models35%- Train and tune models
  • 1. Split training and test sets
    • 2. Configure parameters
      - Validate and evaluate models
      • 1. Perform cross-validation
        • 2. Use accuracy, precision, recall, AUC
          - Select appropriate algorithms
          • 1. Supervised learning methods
            • 2. Unsupervised learning methods
              Topic 3: Operationalize and Manage Azure Machine Learning Services25%- Deploy models as web services
              • 1. Configure endpoints and access
                • 2. Publish to Azure ML Studio
                  - Consume models and APIs
                  • 1. Use Cognitive Services APIs
                    • 2. Call deployed web services
                      - Manage workspaces and experiments
                      • 1. Share and collaborate
                        • 2. Create and organize projects
                          Topic 4: Prepare Data for Analysis in Azure Machine Learning30%- Import and export data
                          • 1. Import from files and external sources
                            • 2. Connect to Azure storage and databases
                              • 3. Export results and datasets
                                - Perform feature engineering
                                • 1. Normalize and scale features
                                  • 2. Create derived features
                                    - Cleanse and transform data
                                    • 1. Remove duplicates and inconsistent entries
                                      • 2. Handle missing values and outliers
                                        - Explore and summarize data
                                        • 1. Generate statistical summaries
                                          • 2. Visualize data distributions

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