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Exam Code: H13-723-ENU
Exam Name: HCIP-Big Data Developer
Updated: Jul 30, 2026
Q & A: 155 Questions and Answers
H13-723-ENU Free Demo download
The HCIP-Big Data Developer V1.0 exam covers a wide range of topics, such as big data architecture, data modeling, data processing, and data analysis. Huawei's Big Data platforms are some of the most popular platforms in the industry, and mastering them can be a valuable asset for any developer.
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The H13-723 certification exam covers a wide range of topics related to big data development, including data modeling, data processing, data analysis, and data visualization with Hadoop and Spark. H13-723-ENU exam also covers other important aspects of big data development, such as data storage, data security, and data management. To successfully pass the exam, candidates must have a deep understanding of these topics and be able to apply them in practical scenarios. HCIP-Big Data Developer certification exam is recognized globally and is a valuable asset for professionals looking to advance their career in the big data industry. Passing H13-723-ENU exam demonstrates that a candidate has the knowledge and skills required to develop and maintain big data applications in a professional setting.
To prepare for the exam, candidates should have a good understanding of big data technologies and concepts, as well as experience in developing and deploying big data solutions. They should also be familiar with Huawei's Big Data products and solutions and have experience working with these products in real-world scenarios.
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Big Data Offline Batch Processing Scenario-based Solution | 25% | - Offline solution design and optimization - Data ingestion tools: Loader, Sqoop, Kettle - HDFS and Hive principles and usage - Spark SQL and batch processing |
| Topic 2: Big Data Application Development Overall Guide | 15% | - Big data concepts and architecture - Application development fundamentals - Scenario-based big data solutions overview - Mainstream big data technologies and ecosystems |
| Topic 3: Big Data Real-time Retrieval Scenario-based Solution | 30% | - HBase architecture and development - GaussDB(DWS) and distributed query - Elasticsearch and real-time search - Real-time retrieval optimization |
| Topic 4: Big Data Real-time Stream Computing Scenario-based Solution | 30% | - Flume and Kafka data ingestion - Flink stream processing framework - Spark Streaming and Structured Streaming - Stream solution deployment and tuning |
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