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Microsoft 070-774 exam is an excellent way for aspiring data scientists and cloud technologists to show their skills and knowledge of Azure Machine Learning services. It validates your expertise in building, deploying, and managing machine learning models in a cloud environment. Achieving this certification demonstrates your ability to meet the needs of this fast-paced and innovative industry, and it can significantly improve career prospects in data science.

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Another important area of focus in the 070-774 exam is data exploration and transformation. Candidates must demonstrate their ability to use Azure Machine Learning to clean and preprocess data, as well as to perform feature engineering to create new variables that can improve model performance. They must also be able to work with large datasets and use distributed computing techniques to speed up data processing.

The Microsoft 070-774 exam covers a variety of topics, including data preparation, model training, model deployment, and experimentation. Candidates are expected to have a solid understanding of machine learning principles and techniques, as well as experience with Azure Machine Learning tools and platforms. Perform Cloud Data Science with Azure Machine Learning certification is intended for data scientists, IT professionals, and developers who work with machine learning models and algorithms. Successful completion of the exam demonstrates proficiency in cloud data science, which can increase career opportunities and earning potential.

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%- Validate and evaluate models
  • 1. Use accuracy, precision, recall, AUC
    • 2. Perform cross-validation
      - 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 3: Prepare Data for Analysis in Azure Machine Learning30%- Import and export data
              • 1. Import from files and external sources
                • 2. Export results and datasets
                  • 3. Connect to Azure storage and databases
                    - 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. Visualize data distributions
                              • 2. Generate statistical summaries
                                Topic 4: 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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