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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Apache Hive Development | 25% | - Write and optimize HiveQL queries - Use Hive functions, views, and metastore - Create and manage Hive tables, partitions, and buckets |
| Topic 2: Data Ingestion | 25% | - Import/export data using Sqoop - Ingest streaming data with Flume - Load data into HDFS from external sources |
| Topic 3: Apache Pig Development | 30% | - Debug and tune Pig jobs - Write and optimize Pig Latin scripts - Data transformation, filtering, joining, and aggregation |
| Topic 4: Hadoop Fundamentals & Architecture | 20% | - YARN architecture and job execution - HDFS operations and file management - MapReduce concepts and job lifecycle |
Question 1
Which one of the following statements regarding the components of YARN is FALSE?
A. The ResourceManager monitors and restarts any failed Containers
B. A Container executes a specific task as assigned by the ApplicationMaster
C. A client application submits a YARW job to the ResourceManager
D. The ResourceManager is responsible for scheduling and allocating resources
Question 2
For each input key-value pair, mappers can emit:
A. As many intermediate key-value pairs as designed, as long as all the keys have the same types and all the values have the same type.
B. As many intermediate key-value pairs as designed, but they cannot be of the same type as the input key-value pair.
C. One intermediate key-value pair, of a different type.
D. As many intermediate key-value pairs as designed. There are no restrictions on the types of those key-value pairs (i.e., they can be heterogeneous).
E. One intermediate key-value pair, but of the same type.
Question 3
In a MapReduce job with 500 map tasks, how many map task attempts will there be?
A. Between 500 and 1000.
B. At most 500.
C. Exactly 500.
D. At least 500.
E. It depends on the number of reduces in the job.
Question 4
MapReduce v2 (MRv2/YARN) is designed to address which two issues?
A. Ability to run frameworks other than MapReduce, such as MPI.
B. Single point of failure in the NameNode.
C. Standardize on a single MapReduce API.
D. Reduce complexity of the MapReduce APIs.
E. Resource pressure on the JobTracker.
F. HDFS latency.
Question 5
Your cluster's HDFS block size in 64MB. You have directory containing 100 plain text files, each of which is 100MB in size. The InputFormat for your job is TextInputFormat. Determine how many Mappers will run?
A. 640
B. 200
C. 100
D. 64
Solutions:
| Question 1 Answer: A | Question 2 Answer: A | Question 3 Answer: D | Question 4 Answer: B,E | Question 5 Answer: B |
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