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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Big Data Offline Batch Processing Scenario-based Solution | 25% | - Offline batch processing solutions - HDFS and Hive fundamentals - Spark SQL and data ingestion tools (Loader, Sqoop, Kettle) |
| Topic 2: Big Data Real-time Retrieval Scenario-based Solution | 30% | - Real-time query optimization - Real-time retrieval solution components - Distributed database concepts (HBase, Elasticsearch, GES) |
| Topic 3: Big Data Application Development Overall Guide | 15% | - Big data concepts and architecture - Big data scenario-based solutions - Application development fundamentals |
| Topic 4: Big Data Real-time Stream Computing Scenario-based Solution | 30% | - Stream data ingestion and processing - Streaming use cases and configuration - Real-time stream processing frameworks (Flume, Kafka, Spark Streaming, Flink) |
1. In Spark, because DataFrame is a higher-level interface than RDD, the call level will be more. Therefore, the performance of all programs developed based on DataFrame will be worse than that of programs developed based on RDD.
A) False
B) True
2. In a MapReduce application, the output of the map function is processed by the MapReduce framework and sent to the reduce function. This process is based on key-value pairs for sorting and grouping.
A) False
B) True
3. In FusionInsigt HD, the user wants to query the contents of a certain HBase table through the HBase shell operation. In this scenario, it is recommended that the administrator assign a machine-to-machine account to the user.
A) False
B) True
4. When the HBase application is running, the application can write data while creating a table.
A) False
B) True
5. When writing MapReduce applications, which two interfaces usually need developers to implement?
A) reduce and combine
B) map and reduce
C) combine and sort
D) map and combine
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: B | Question # 3 Answer: A | Question # 4 Answer: A | Question # 5 Answer: B |
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