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Huawei H13-711_V3.0 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Spark Distributed Computing Framework | 12% | - Spark architecture and RDD concept - Spark task scheduling and optimization - Spark SQL and Spark Streaming |
| Topic 2: HBase Distributed Database | 10% | - HBase architecture and data model - HBase read/write process - HBase table design and application scenarios |
| Topic 3: LDAP & Kerberos Security | 5% | - Unified authentication principles - Kerberos authentication process |
| Topic 4: MapReduce & YARN | 12% | - MapReduce programming model and workflow - YARN architecture and resource scheduling - Task execution and resource management |
| Topic 5: Hive Distributed Data Warehouse | 10% | - Hive architecture and components - Hive data storage and query execution - Hive data model and SQL |
| Topic 6: Huawei Big Data Solution | 4% | - Platform functions and industry cases - FusionInsight HD/MRS overview |
| Topic 7: Kafka Messaging System | 9% | - Kafka architecture and core concepts - Message production and consumption |
| Topic 8: Elasticsearch Search Engine | 5% | - Full-text search and analysis - Architecture and indexing mechanism |
| Topic 9: Big Data Development Trend & Kunpeng Big Data Solution | 3% | - Characteristics of big data - Big Data industry development trends - Huawei Kunpeng big data overview |
| Topic 10: HDFS & ZooKeeper | 12% | - HDFS architecture and principles - ZooKeeper architecture and coordination mechanism - HDFS data storage and fault tolerance |
| Topic 11: Flink Stream Processing Framework | 8% | - Event time and window mechanism - Stream processing scenarios - Flink architecture and data flow model |
| Topic 12: Loader Data Conversion | 5% | - Loader functions and use cases - Data import/export process |
| Topic 13: Flume Log Collection | 7% | - Flume architecture and components - Data collection workflow |
| Topic 14: Redis In-Memory Database | 5% | - High availability and application scenarios - Data types and storage structure |
Huawei HCIA-Big Data V3.0 Sample Questions:
1. Spark's intermediate data is stored in memory, which is more efficient and has higher latency for iterative operations and batch calculations.
A) True
B) False
2. What data types of application scenarios is HBase of Hadoop not suitable for?
A) High throughput application scenarios
B) Semi-structured data application scenarios
C) Large file application scenarios
D) Mass data application scenarios
3. Which of the following options cannot be achieved through big data technology?
4. Map Reducein MaThe combine in stage p is a pre-grouping process and is optional.
A) TRUE
B) FALSE
5. Which of the following statements about data management and architecture faced by traditional enterprises is correct?
A) Mass data operation and maintenance needs to ensure data stability, support high concurrency and increase server load.
B) Traditional databases are not suitable for handling petabytes of data
C) Traditional databases do not consider the diversity of data,Especially compatible with structured data, unstructured data and semi-structured data.
D) Traditional databases do not require high data processing time, while big data needs to process data in real time
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: C | Question # 3 Answer: Only visible for members | Question # 4 Answer: B | Question # 5 Answer: A,B,C,D |
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