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Huawei H13-723-ENU Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Big Data Application Development Overall Guide | 15% | - Big data architecture: Lambda and Kappa - HDFS advanced features and YARN scheduling - FusionInsight HD architecture and components - Security framework: Kerberos, Ranger, ZooKeeper |
| Topic 2: Big Data Offline Batch Processing Scenario-based Solution | 25% | - Hive development: UDF/UDAF/UDTF, ACID, optimization - Spark development: RDD, DataFrame, Catalyst, AQE - Data ingestion and lakehouse formats |
| Topic 3: Big Data Real-time Stream Computing Scenario-based Solution | 30% | - Spark Structured Streaming - Kafka integration and transactions - Flink SQL and CEP - Apache Flink: event time, windowing, state, checkpoint |
| Topic 4: Big Data Real-time Retrieval Scenario-based Solution | 30% | - HBase design, coprocessors, optimization - Elasticsearch, SolrCloud, CarbonData usage - Apache Phoenix SQL and indexing - GaussDB(DWS) data warehouse |
Huawei HCIP-Big Data Developer Sample Questions:
Question #1
A large manufacturing company plans to transform its internal logistics data and sales data into big data. The design department has given an analysis of the data storage plan. Which of the following descriptions is correct?
A. HBase can be built on ordinary commercial servers, and the construction cost is relatively low.
B. Hbase supports efficient random reading, and can complete real-time analysis and command of the logistics situation after a reasonable design.
C. Logistics data is very sparse, and HBase can efficiently process sparse data.
D. HBase stores massive data and supports dynamic expansion, which can fulfill the storage requirements of logistics and sales data.
Question #2
In the Hive application of FusionInsight HD, there are scenarios where the data storage file has a high compression efficiency, and most queries only involve a part of the file field. This scenario is suitable for ORC File storage.
A. False
B. True
Question #3
When the HBase application is running, the application can write data while creating a table.
A. False
B. True
Question #4
In Spark, assuming that lines is a DStream object, which of the following statements can periodically count the number of words on this stream?
A. Iines.flatMap(_.split (" " )).map(word => (word,word.Iength())).reduceByKey (_ +_).print()
B. Iines.flatMap(_.split (" " )).map(word => (word, 1)).reduce(_ +_).print()
C. Iines.fIatMap(_.spIit (" " )).map(word => (word, 1)).reduceByKey(_ +_).print()
D. Iines.flatMap(_.split (" " )).flatMap(word => (word, 1)).groupByKey(_ +_).print()
Question #5
Which of the following is not a feature of MapReduce?
A. Real-time calculation
B. Good scalability
C. Easy to program
D. High fault tolerance
Solutions:
| Question #1 Answer: A,B,C,D | Question #2 Answer: B | Question #3 Answer: A | Question #4 Answer: C | Question #5 Answer: A |


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