Describe the Nature and Use of MapReduce and Apache Hadoop DATA-SCIENCE-FOUNDATIONS Practice Questions (Page 1)

Questions 1–5

1. 1foundation · easy

   Which statement best describes HDFS's block-based storage?

   • AFiles are divided into fixed-size blocks that are stored independently across the cluster
   • BBlocks are compressed to save storage space
   • CBlocks are stored in a central database for easy access
   • DEach file is stored as a single contiguous block on one node
   Reveal Solution Discussion Select an answer first
2. 2expert · medium

   A company stores sensitive customer data in HDFS. They must ensure that if a DataNode is physically stolen, the data on that node cannot be read. Which approach provides the most direct protection?

   • AIncrease the replication factor.
   • BUse HDFS erasure coding.
   • CEnable HDFS encryption at rest.
   • DRestrict network access to DataNodes.
   Reveal Solution Discussion Select an answer first
4. 3expert · medium

   A Hadoop cluster has 5 DataNodes and a single NameNode. The administrator needs to ensure that the cluster remains available if the NameNode fails. They also want to avoid a significant increase in operational complexity. Which solution best meets these requirements?

   • AIncrease the replication factor to 5.
   • BConfigure a standby NameNode with ZooKeeper for automatic failover.
   • CUse the Secondary NameNode as a hot standby.
   • DRun the NameNode on a virtual machine with automatic restart.
   Reveal Solution Discussion Select an answer first
5. 4foundation · easy

   Which statement best describes how MapReduce achieves parallel processing of large datasets?

   • AIt runs all map tasks sequentially on a single node and then distributes the output
   • BIt divides the input into independent splits that are processed concurrently by multiple nodes
   • CIt processes the entire dataset on a single powerful machine
   • DIt stores a copy of the entire dataset in memory on every node
   Reveal Solution Discussion Select an answer first
6. 5expert · medium

   A MapReduce job is running slowly. The map phase completes quickly, but the reduce phase takes a long time. The job has a single reducer. The administrator suspects data skew. What is the most effective way to address this?

   • AIncrease the number of mappers.
   • BUse a combiner to reduce data sent to the reducer.
   • CUse a custom partitioner to distribute keys more evenly.
   • DIncrease the HDFS block size.
   Reveal Solution Discussion Select an answer first