Microsoft 070-774 : Perform Cloud Data Science with Azure Machine Learning

Exam Code: 070-774
Exam Name: Perform Cloud Data Science with Azure Machine Learning
Updated: Sep 30, 2026
Q & A: 65 Questions and Answers

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MICROSOFT 70-774 Exam Reference, Microsoft Documents

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Overview about Overview about MICROSOFT 70-774 Exam

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

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Perform Cloud Data Science with Azure Machine learning 70-774 Dumps will include below mentioned topics with Exam focused percentage

Prepare Data for Analysis in Azure Machine Learning and Export from Azure Machine Learning: 20-25%
Operationalize and Manage Azure Machine Learning Services: 20-25%
Use Other Services for Machine Learning: 20-25%
Develop Machine Learning Models: 20-25%

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

Microsoft 070-774 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Use Other Services for Machine Learning	10%	- Integrate with HDInsight - Leverage Cortana Intelligence Gallery - Work with Microsoft Cognitive Toolkit - Use SQL Server R Services
Topic 2: Operationalize and Manage Azure Machine Learning Services	25%	- Consume models and APIs 1. Call deployed web services 2. Use Cognitive Services APIs - Deploy models as web services 1. Publish to Azure ML Studio 2. Configure endpoints and access - Manage workspaces and experiments 1. Create and organize projects 2. Share and collaborate
Topic 3: Develop Machine Learning Models	35%	- Validate and evaluate models 1. Perform cross-validation 2. Use accuracy, precision, recall, AUC - Train and tune models 1. Configure parameters 2. Split training and test sets - Select appropriate algorithms 1. Unsupervised learning methods 2. Supervised learning methods
Topic 4: Prepare Data for Analysis in Azure Machine Learning	30%	- Import and export data 1. Import from files and external sources 2. Connect to Azure storage and databases 3. Export results and datasets - Cleanse and transform data 1. Remove duplicates and inconsistent entries 2. Handle missing values and outliers - Perform feature engineering 1. Create derived features 2. Normalize and scale features - Explore and summarize data 1. Visualize data distributions 2. Generate statistical summaries