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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Hadoop Fundamentals & Architecture | 20% | - YARN architecture and job execution - HDFS operations and file management - MapReduce concepts and job lifecycle |
| Topic 2: Data Ingestion | 25% | - Ingest streaming data with Flume - Import/export data using Sqoop - Load data into HDFS from external sources |
| Topic 3: Apache Hive Development | 25% | - Use Hive functions, views, and metastore - Write and optimize HiveQL queries - Create and manage Hive tables, partitions, and buckets |
| Topic 4: Apache Pig Development | 30% | - Data transformation, filtering, joining, and aggregation - Write and optimize Pig Latin scripts - Debug and tune Pig jobs |
1. You need to create a job that does frequency analysis on input data. You will do this by writing a Mapper that uses TextInputFormat and splits each value (a line of text from an input file) into individual characters. For each one of these characters, you will emit the character as a key and an InputWritable as the value. As this will produce proportionally more intermediate data than input data, which two resources should you expect to be bottlenecks?
A) Processor and RAM
B) Processor and disk I/O
C) Processor and network I/O
D) Disk I/O and network I/O
2. Indentify the utility that allows you to create and run MapReduce jobs with any executable
or script as the mapper and/or the reducer?
A) mapred
B) Hadoop Streaming
C) Flume
D) Oozie
E) Sqoop
3. Can you use MapReduce to perform a relational join on two large tables sharing a key? Assume that the two tables are formatted as comma-separated files in HDFS.
A) Yes.
B) Yes, but only if one of the tables fits into memory
C) No, MapReduce cannot perform relational operations.
D) Yes, so long as both tables fit into memory.
E) No, but it can be done with either Pig or Hive.
4. Which one of the following Hive commands uses an HCatalog table named x?
A) SELECT * FROM x;
B) SELECT x.-FROM org.apache.hcatalog.hive.HCatLoader('x');
C) SELECT * FROM org.apache.hcatalog.hive.HCatLoader('x');
D) Hive commands cannot reference an HCatalog table
5. Analyze each scenario below and indentify which best describes the behavior of the default partitioner?
A) The default partitioner computes the hash of the key and divides that valule modulo the number of reducers. The result determines the reducer assigned to process the key-value pair.
B) The default partitioner computes the hash of the value and takes the mod of that value with the number of reducers. The result determines the reducer assigned to process the key-value pair.
C) The default partitioner computes the hash of the key. Hash values between specific ranges are associated with different buckets, and each bucket is assigned to a specific reducer.
D) The default partitioner implements a round-robin strategy, shuffling the key-value pairs to each reducer in turn. This ensures an event partition of the key space.
E) The default partitioner assigns key-values pairs to reduces based on an internal random number generator.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: B | Question # 3 Answer: A | Question # 4 Answer: C | Question # 5 Answer: A |
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