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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Huawei Big Data Solution | 4% | - Platform functions and industry cases - FusionInsight HD/MRS overview |
| Topic 2: LDAP & Kerberos Security | 5% | - Unified authentication principles - Kerberos authentication process |
| Topic 3: MapReduce & YARN | 12% | - Task execution and resource management - YARN architecture and resource scheduling - MapReduce programming model and workflow |
| Topic 4: Hive Distributed Data Warehouse | 10% | - Hive data storage and query execution - Hive data model and SQL - Hive architecture and components |
| Topic 5: Elasticsearch Search Engine | 5% | - Full-text search and analysis - Architecture and indexing mechanism |
| Topic 6: HDFS & ZooKeeper | 12% | - ZooKeeper architecture and coordination mechanism - HDFS architecture and principles - HDFS data storage and fault tolerance |
| Topic 7: Kafka Messaging System | 9% | - Kafka architecture and core concepts - Message production and consumption |
| Topic 8: Loader Data Conversion | 5% | - Loader functions and use cases - Data import/export process |
| Topic 9: HBase Distributed Database | 10% | - HBase read/write process - HBase table design and application scenarios - HBase architecture and data model |
| Topic 10: Flume Log Collection | 7% | - Flume architecture and components - Data collection workflow |
| Topic 11: Redis In-Memory Database | 5% | - Data types and storage structure - High availability and application scenarios |
| Topic 12: Big Data Development Trend & Kunpeng Big Data Solution | 3% | - Characteristics of big data - Huawei Kunpeng big data overview - Big Data industry development trends |
| Topic 13: Spark Distributed Computing Framework | 12% | - Spark task scheduling and optimization - Spark SQL and Spark Streaming - Spark architecture and RDD concept |
| Topic 14: Flink Stream Processing Framework | 8% | - Event time and window mechanism - Stream processing scenarios - Flink architecture and data flow model |
1. Comparing Spark and MapReducei, which of the following statements are correct?
A) Shuffle is a necessary process in Spark tasks
B) Spark can cache intermediate results in memory reducing disk I0
C) Intermediate results of Spark tasks can only be stored in memory
D) Shuffle is not a necessary process in Spark tasks
2. In the YARN service, if you want to set the capacity of the queue QuqueA to 30%, which parameter should be configured?
A) YARN.scheduler.capacity.root.QueueA. capacity
B) YARN.scheduler.capacity.root.QueueA. state
C) YARN.scheduler.capacity.root.QueueA. minimum-user-limit-percent
D) YARN.scheduler.capacity.root.QueueA. user-limit-factor
3. In Hadoop, if yarn.scheduler.capacity.root.Queue A.
minimum-user-limitpercent is set to 50, which of the following statements is wrong?
A) QueueA must ensure that each user gets at least 50% of the resources.
B) Each user in QueueA can only get up to 50% of the resources.
C) A user submits a task and can use 100% of QueueA's resources.
D) If there are already 2 user tasks running in QueueA, then the task submitted by the third user needs to wait for the release of resources.
4. Which of the following statements about the comparison of Sparkstreamingi and Streaming is incorrect?
A) SparkStreaming:It is a micro-batch framework, and events need to be processed when they are exhausted to a certain amount
B) The throughput of SparkStreaming is about 2-5 times that of Streaming
C) SparkStreaming event processing latency is higher than Streaming
D) The execution logic of Streamingl is to start immediately, and then recycle after running.
5. In the MRS service, the unavailability of the Zookeeper service will result in the unavailability of the kafka service.
A) True
B) False
Solutions:
| Question # 1 Answer: B,D | Question # 2 Answer: A | Question # 3 Answer: B | Question # 4 Answer: D | Question # 5 Answer: A |
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