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Huawei H13-711_V3.5: HCIA-Big Data V3.5 Exam is an excellent opportunity for professionals who want to validate their skills in big data management. H13-711_V3.5 exam covers all aspects of big data management and is recognized globally. Passing the exam can help professionals enhance their career prospects and stand out in the job market. H13-711_V3.5 exam is challenging, and candidates are required to have a good understanding of big data management to pass it.
The Huawei H13-711_V3.5 exam consists of multiple-choice questions and simulations that test the candidate's ability to work with big data technologies. H13-711_V3.5 exam duration is three hours, and candidates must score at least 600 out of 1000 points to pass. H13-711_V3.5 exam can be taken in-person at a Huawei testing center or online through the Huawei certification portal.
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Huawei H13-711_V3.5 Exam Syllabus Topics:
| Section | Objectives |
| Big Data Platform Components | - Hive data warehouse
- ClickHouse columnar database
- Flink stream computing framework
- Spark memory computing framework
- HBase distributed database
- Kafka distributed message queue
- Elasticsearch search engine
- YARN resource scheduling
- DataArt Studio data development platform
- MapReduce computing framework
- HDFS distributed file system
- Hadoop ecosystem overview
- GES (Graph Engine Service) concepts and application scenarios
- Sqoop data import/export
- Flume log collection
- GES graph engine service
|
| Big Data Platform Security | - Data encryption and desensitization
- Security authentication and permission management (Kerberos, Ranger, LDAP)
- Platform security design
|
| FusionInsight HD Overview | - Huawei FusionInsight HD architecture and features
- MRS service component introduction
- Cluster management and O&M basics
|
| Big Data Development Practice | - Data visualization and analysis
- Real-time and batch data processing
- Data loading and ETL processing
- Typical big data application cases
|
| Big Data Application Scenarios | - Big data strategy and data governance overview
- Industry pain points and application scenarios of big data
|