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Huawei H13-711_V3.0 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Flume Log Collection | 7% | - Flume architecture and components - Data collection workflow |
| Elasticsearch Search Engine | 5% | - Architecture and indexing mechanism - Full-text search and analysis |
| Spark Distributed Computing Framework | 12% | - Spark architecture and RDD concept - Spark SQL and Spark Streaming - Spark task scheduling and optimization |
| Flink Stream Processing Framework | 8% | - Flink architecture and data flow model - Stream processing scenarios - Event time and window mechanism |
| Big Data Development Trend & Kunpeng Big Data Solution | 3% | - Characteristics of big data - Big Data industry development trends - Huawei Kunpeng big data overview |
| HBase Distributed Database | 10% | - HBase read/write process - HBase architecture and data model - HBase table design and application scenarios |
| Redis In-Memory Database | 5% | - Data types and storage structure - High availability and application scenarios |
| LDAP & Kerberos Security | 5% | - Unified authentication principles - Kerberos authentication process |
| MapReduce & YARN | 12% | - Task execution and resource management - YARN architecture and resource scheduling - MapReduce programming model and workflow |
| Hive Distributed Data Warehouse | 10% | - Hive data storage and query execution - Hive data model and SQL - Hive architecture and components |
| HDFS & ZooKeeper | 12% | - HDFS architecture and principles - HDFS data storage and fault tolerance - ZooKeeper architecture and coordination mechanism |
| Loader Data Conversion | 5% | - Loader functions and use cases - Data import/export process |
| Huawei Big Data Solution | 4% | - Platform functions and industry cases - FusionInsight HD/MRS overview |
| Kafka Messaging System | 9% | - Kafka architecture and core concepts - Message production and consumption |
Huawei HCIA-Big Data V3.0 Sample Questions:
Question 1
BelowcloseAtWhat does Flick's fault tolerance mechanism describe errors?
A. SaveThe point mechanism ensures thatAfter the program is upgraded or modified and developed, etC. Also able to restore from a saved state bit healthy boot
B. Chackpoint mechanismGuaranteed in real timeprogram operationrun time, even ifunexpectedlycan alsoEnterRowsinceIrecovercomplex
C. SavepointmechanismGuaranteed in real timeprogram operationrun time, even ifunexpectedlycan alsoEnterRowsinceIrecover
D. Chackpointmechanism guaranteesAfter the program is upgraded or modified and developed, etC. Also able to restore from a saved state bit healthy boot
Question 2
In Spark SQL tables, there are often many small files (the size is much smaller than the HDFS block size). In this case, Spark will start more Tasks to process these small files. When there is a Shuffle operation in the SQL logic, it will Greatly increase the number of hash buckets. This seriously affects performance.
A. True
B. False
Question 3
F1ume is used to collect data, and the basic unit of data transmitted by the device is
A. Split
B. Event
C. Block
D. Packet
Question 4
As an authentication server center, Kerberos1 can provide unified authentication services to all services in the cluster and secondary development applications of customers.
A. True
B. False
Question 5
By configuring which of the following parameters can the logs generated in Kafkal be cleaned up?
A. log.retention.hours
B. server.properties
C. log.retention.bytes
D. log.cleanup.policy
Solutions:
| Question 1 Answer: A,B | Question 2 Answer: B | Question 3 Answer: B | Question 4 Answer: A | Question 5 Answer: A,C,D |
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