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The ACA-BigData1 certification exam covers a wide range of topics, including big data concepts, distributed systems, data processing, storage, and analysis. ACA-BigData1 exam is designed to test the candidate's theoretical understanding and practical application of these concepts. ACA Big Data Certification Exam certification is recognized globally and provides an excellent opportunity for professionals to enhance their career prospects and stay relevant in today's competitive job market.

The ACA Big Data Certification Exam focuses on various aspects of big data, such as architecture, storage, computing, analysis, and real-time processing. It validates skills in data modeling, data governance, data security, and data optimization. Additionally, the exam is designed to test knowledge of various Alibaba Cloud products such as MaxCompute, DataWorks, and E-MapReduce.

Reference: https://edu.alibabacloud.com/certification/aca_bigdata?spm=a3c0i.11597324.1621765730.10.e69e4755kTNSGC

Alibaba Cloud ACA-BigData1 (ACA Big Data Certification) Exam is an essential certification for professionals who want to advance their career in the field of big data. It covers a wide range of topics related to big data, including data analysis, processing, storage, and management. ACA Big Data Certification Exam certification is recognized globally and is highly valued by employers, making it a valuable credential for any big data professional.

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Alibaba Cloud ACA-BigData1 Exam Syllabus Topics:

SectionWeightObjectives
E-MapReduce (EMR)- Operations
  • 1. Common big data application scenarios
    • 2. Instance types and pricing models
      - Cluster and ecosystem components
      • 1. Spark, Hadoop, Kafka, ZooKeeper basics
        • 2. Cluster deployment and scaling
          MaxCompute≈41%- Security and management
          • 1. Users, roles, ACL and policy authorization
            • 2. Project isolation and security levels
              - Core concepts
              • 1. Projects, tables, partitions, resources, tasks
                - Data processing and usage
                • 1. UDF, UDAF, UDTF development
                  • 2. Data upload and download (Tunnel, SDKs)
                    • 3. SQL operations and query optimization
                      Distributed Systems Fundamentals- Distributed computing concepts
                      • 1. MapReduce fundamentals
                        • 2. Distributed file systems (HDFS concepts)
                          • 3. Coordination services (ZooKeeper)
                            • 4. Cluster resource management (YARN)
                              DataWorks- Data development and orchestration
                              • 1. Data integration workflows
                                • 2. Scheduling and O&M monitoring
                                  • 3. Data Studio usage

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