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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Flink Stream Processing Framework | 8% | - Flink architecture and data flow model - Event time and window mechanism - Stream processing scenarios |
| Topic 2: HBase Distributed Database | 10% | - HBase table design and application scenarios - HBase read/write process - HBase architecture and data model |
| Topic 3: Loader Data Conversion | 5% | - Loader functions and use cases - Data import/export process |
| Topic 4: Huawei Big Data Solution | 4% | - Platform functions and industry cases - FusionInsight HD/MRS overview |
| Topic 5: Big Data Development Trend & Kunpeng Big Data Solution | 3% | - Huawei Kunpeng big data overview - Characteristics of big data - Big Data industry development trends |
| Topic 6: Hive Distributed Data Warehouse | 10% | - Hive data storage and query execution - Hive architecture and components - Hive data model and SQL |
| Topic 7: Kafka Messaging System | 9% | - Kafka architecture and core concepts - Message production and consumption |
| Topic 8: Flume Log Collection | 7% | - Flume architecture and components - Data collection workflow |
| Topic 9: LDAP & Kerberos Security | 5% | - Unified authentication principles - Kerberos authentication process |
| Topic 10: Redis In-Memory Database | 5% | - Data types and storage structure - High availability and application scenarios |
| Topic 11: Spark Distributed Computing Framework | 12% | - Spark task scheduling and optimization - Spark SQL and Spark Streaming - Spark architecture and RDD concept |
| Topic 12: MapReduce & YARN | 12% | - YARN architecture and resource scheduling - MapReduce programming model and workflow - Task execution and resource management |
| Topic 13: Elasticsearch Search Engine | 5% | - Architecture and indexing mechanism - Full-text search and analysis |
| Topic 14: HDFS & ZooKeeper | 12% | - HDFS architecture and principles - HDFS data storage and fault tolerance - ZooKeeper architecture and coordination mechanism |
1. Which fields can ElasticSearchl's balancing algorithm be applied to?
A) export data
B) volume reduction
C) Expansion
D) Import Data
2. What is the purpose of periodically executing Compaction in the HBase cluster?
A) Reduce the number of files under the same ColumnFamily in the same Region
B) Improve data read performance
C) Reduce the number of files in the same Region
D) Improve data writing capability
3. HBasetable design, column families andRowKeyIt is an important part of table design. Which of the following options is not suitableDoRowKey?
A) userID
B) identity number
C) handmachine number
D) username
4. An Executorl of a Spark task can run multiple tasks at the same time
A) False
B) True
5. Flink is a computing framework that combines batch processing and stream processing. Its core is a stream data processing engine for data classification and parallel computing.
A) False
B) True
Solutions:
| Question # 1 Answer: B,C,D | Question # 2 Answer: A,B,D | Question # 3 Answer: D | Question # 4 Answer: B | Question # 5 Answer: B |
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