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To prepare for the exam, candidates should have a solid understanding of data science and machine learning concepts, as well as experience working with Azure technologies. Microsoft offers a range of training courses and resources to help candidates prepare for the exam, including online courses, practice exams, and study guides. Additionally, candidates can take advantage of the Microsoft Azure free trial to gain hands-on experience with Azure Machine Learning before taking the exam.
Microsoft Azure Machine Learning is a cloud-based service that provides a platform for performing data science tasks such as data preparation, predictive modeling, and deployment. The Microsoft 070-774 (Perform Cloud Data Science with Azure Machine Learning) Certification Exam is designed for professionals who want to demonstrate their expertise in using Azure Machine Learning for cloud-based data science solutions.
Reference: https://www.microsoft.com/en-us/learning/exam-70-774.aspx
The Microsoft 070-774 exam covers topics such as designing and implementing machine learning models, deploying and managing machine learning models, and working with data in Azure. It is intended for professionals who have experience using Azure Machine Learning and are proficient in programming languages such as Python and R.
Preparing for this certification exam requires a comprehensive understanding of Azure Machine Learning and the cloud data science workflow. There are various resources available for preparing, including online courses, Microsoft documentation, and practice exams. By passing the Microsoft 070-774 certification exam, you can demonstrate your proficiency in cloud data science and machine learning, positioning yourself as a top candidate for cloud-based data science roles.
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Microsoft 070-774 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Prepare Data for Analysis in Azure Machine Learning | - Perform feature engineering - Explore and summarize data - Cleanse data for Azure ML - Import and export data to/from Azure ML |
| Topic 2: Use Other Services for Machine Learning | - Perform data sciences at scale via HDInsight - Build/use neural networks (Microsoft Cognitive Toolkit) - Streamline development with existing resources - Perform database analytics using SQL Server R Services |
| Topic 3: Operationalize and Manage Azure Machine Learning Services | - Consume related Cognitive Services APIs - Consume ML models - Deploy models using Azure ML - Manage ML projects and workspaces |
| Topic 4: Develop Machine Learning Models | - Initialize and train models - Validate models - Select appropriate algorithm/method |






