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Microsoft 070-775 certification exam is designed for data engineers who want to demonstrate their expertise in performing data engineering on Microsoft Azure HDInsight. 070-775 exam measures the candidate's ability to create and implement big data workflows, design and implement data processing solutions, and manage and monitor HDInsight clusters. Candidates who pass the exam earn the Microsoft Certified: Azure Data Engineer Associate certification, which is a globally recognized industry credential.

Reference: https://www.microsoft.com/en-us/learning/exam-70-775.aspx

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Earning the Microsoft 070-775 certification demonstrates a candidate's proficiency in using Azure HDInsight for data engineering tasks. Perform Data Engineering on Microsoft Azure HDInsight certification can help professionals advance their careers and gain recognition as experts in the field of big data. Additionally, this certification can help organizations identify skilled professionals who can effectively implement data engineering solutions using Azure HDInsight.

Microsoft 070-775 certification exam is an important milestone for IT professionals in the big data industry. It tests candidates on their ability to design and implement batch and real-time processing solutions using Azure HDInsight, as well as monitor and optimize cluster performance. With proper preparation and study, candidates can earn this valuable certification and advance their careers in data engineering and architecture.

Microsoft 070-775 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Implement Big Data Real-Time Processing Solutions	25%	- Create Spark Streaming applications 1. Process streaming data in real time 2. Implement Structured Streaming 3. Use DStreams - Implement event processing solutions 1. Store and process streaming data with HBase 2. Use Apache Storm 3. Integrate Apache Kafka
Topic 2: Implement Big Data Batch Processing Solutions	25%	- Operationalize Hadoop and Spark workloads 1. Automate workflows with Apache Oozie 2. Use Azure Data Factory orchestration - Design ETL solutions with Spark 1. Optimize Spark performance 2. Process large-scale datasets 3. Create Spark ETL processes - Implement batch processing with Hive and Pig 1. Execute Pig jobs 2. Design Hive-based solutions 3. Manage data processing workflows
Topic 3: Implement Big Data Interactive Processing Solutions	25%	- Create interactive query solutions 1. Use Interactive Hive 2. Use Spark SQL - Perform exploratory data analysis 1. Use Apache Zeppelin 2. Analyze data with Spark SQL 3. Work with Jupyter notebooks 4. Use Hive for exploration - Implement interactive processing on HBase 1. Query HBase data efficiently 2. Use Apache Phoenix
Topic 4: Administer and Provision HDInsight Clusters	25%	- Ingest and prepare data 1. Optimize data storage formats 2. Load data into Azure Data Lake Store 3. Load data into Azure Blob Storage - Secure and manage multi-user clusters 1. Manage user authorization and permissions 2. Configure domain-joined clusters 3. Use Ambari and Ranger administration tools - Deploy and configure HDInsight clusters 1. Customize clusters with script actions 2. Configure storage and networking 3. Create and manage HDInsight cluster types - Monitor and troubleshoot clusters 1. Analyze HDInsight logs 2. Monitor resources with Operations Management Suite 3. Use YARN logs and heap dumps