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Hortonworks HADOOP-PR000007 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Ingestion & Ecosystem Integration | 5% | - Workflow orchestration with Oozie - Sqoop and Flume usage basics |
| Topic 2: Apache Pig Development | 40% | - User-defined functions (UDFs) and optimization - Pig Latin syntax, data loading, and storage - Integration with HCatalog and Hive - Data transformation, filtering, grouping, and joining |
| Topic 3: Apache Hive Development | 35% | - HiveQL queries, joins, aggregations, and partitioning - Performance tuning and indexing - Hive architecture, metastore, and table management - Data types, serialization, and file formats |
| Topic 4: Hadoop Fundamentals & HDFS | 20% | - Hadoop 2.0 architecture & YARN - HDFS file operations, permissions, and data management |
Hortonworks-Certified-Apache-Hadoop-2.0-Developer(Pig and Hive Developer) Sample Questions:
You have just executed a MapReduce job. Where is intermediate data written to after being emitted from
the Mapper's map method?
- A. Into in-memory buffers on the TaskTracker node running the Reducer that spill over and are written into
HDFS. - B. Intermediate data in streamed across the network from Mapper to the Reduce and is never written to
disk. - C. Into in-memory buffers on the TaskTracker node running the Mapper that spill over and are written into
HDFS. - D. Into in-memory buffers that spill over to the local file system of the TaskTracker node running the
Mapper. - E. Into in-memory buffers that spill over to the local file system (outside HDFS) of the TaskTracker node
running the Reducer
Correct Answer: D 🗳️
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. Disk I/O and network I/O
- B. Processor and network I/O
- C. Processor and RAM
- D. Processor and disk I/O
Correct Answer: A 🗳️
Which one of the following statements is true regarding a MapReduce job?
- A. The Mapper must sort its output of (key.value) pairs in descending order based on value
- B. The job's Partitioner shuffles and sorts all (key.value) pairs and sends the output to all reducers
- C. The default Hash Partitioner sends key value pairs with the same key to the same Reducer
- D. The reduce method is invoked once for each unique value
Correct Answer: B 🗳️
You want to populate an associative array in order to perform a map-side join. You've decided to put this
information in a text file, place that file into the DistributedCache and read it in your Mapper before any
records are processed. Indentify which method in the Mapper you should use to implement code for
reading the file and populating the associative array?
- A. init
- B. configure
- C. map
- D. combine
Correct Answer: B 🗳️
What are the TWO main components of the YARN ResourceManager process? Choose 2 answers
- A. Job Tracker
- B. Applications Manager
- C. Scheduler
- D. Task Tracker
Correct Answer: B,C 🗳️
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