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Overview about Overview about MICROSOFT 70-774 Exam

  • Format: Multiple choice, multiple answer
  • Registration Fee: 165 USD
  • Number of Questions: 40-60
  • Length of Examination: 120 minutes
  • Passing Score: 70-80%

The Microsoft 070-774 exam consists of multiple-choice questions and performance-based tasks that require you to apply your knowledge and skills to real-world scenarios. You will need to demonstrate your ability to create and configure Azure Machine Learning workspaces, data stores, and compute resources, as well as your ability to use Azure Machine Learning Studio to create and deploy machine learning models. You will also be tested on your knowledge of Azure Machine Learning packages and APIs, as well as your ability to use Python and R programming languages to create and evaluate machine learning models.

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Microsoft 070-774 certification exam is a valuable credential for data scientists looking to advance their careers in cloud data science and machine learning. Candidates who wish to take the exam should have a solid understanding of the Azure environment, machine learning algorithms, and programming languages such as Python and R. Obtaining this certification can lead to better job prospects, higher salaries, and increased recognition in the industry.

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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:

SectionObjectives
Topic 1: Deploy and manage models- Operationalization
  • 1. Update and retrain models
    • 2. Monitor deployed models
      - Model deployment in Azure
      • 1. Deploy to Azure web services
        • 2. Batch inference pipelines
          Topic 2: Evaluate and optimize models- Model evaluation techniques
          • 1. ROC and AUC analysis
            • 2. Accuracy, precision, recall, F1 score
              - Model tuning and improvement
              • 1. Feature selection and engineering
                • 2. Hyperparameter tuning
                  Topic 3: Develop machine learning models- Training models in Azure Machine Learning
                  • 1. Use Azure ML Studio experiments
                    • 2. Train models using Python or R scripts
                      - Select and implement algorithms
                      • 1. Unsupervised learning models
                        • 2. Supervised learning models
                          Topic 4: Prepare and process data for Azure Machine Learning- Data preparation and transformation
                          • 1. Clean missing or inconsistent data
                            • 2. Transform features for modeling
                              - Data ingestion and storage
                              • 1. Connect to Azure storage services
                                • 2. Import data into Azure Machine Learning datasets

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