In a scenario of frequent data processing slowdowns, which Hadoop performance monitoring tool should be prioritized?

  • Ambari
  • Ganglia
  • Nagios
  • Prometheus
In the case of frequent data processing slowdowns, prioritizing Hadoop performance monitoring using tools like Ambari is crucial. Ambari provides a comprehensive view of cluster health, performance metrics, and allows for efficient management and troubleshooting to identify and address performance bottlenecks.

Advanced MapReduce jobs often require ____ to manage complex data dependencies and transformations.

  • Apache Flink
  • Apache HBase
  • Apache Hive
  • Apache Spark
Advanced MapReduce jobs often require Apache Spark to manage complex data dependencies and transformations. Apache Spark provides in-memory processing and a rich set of APIs, making it suitable for iterative algorithms, machine learning, and advanced analytics on large datasets.

How does Hadoop ensure data durability in the event of a single node failure?

  • Data Compression
  • Data Encryption
  • Data Replication
  • Data Shuffling
Hadoop ensures data durability through data replication. Each data block is replicated across multiple nodes in the cluster, and in the event of a single node failure, the data can still be accessed from the replicated copies, ensuring fault tolerance and data availability.

Which language does HiveQL in Apache Hive resemble most closely?

  • C++
  • Java
  • Python
  • SQL
HiveQL in Apache Hive resembles SQL (Structured Query Language) most closely. It is designed to provide a familiar querying interface for users who are already familiar with SQL syntax. This makes it easier for SQL developers to transition to working with big data using Hive.

HiveQL allows users to write custom mappers and reducers using the ____ clause.

  • CUSTOM
  • MAPREDUCE
  • SCRIPT
  • TRANSFORM
HiveQL allows users to write custom mappers and reducers using the TRANSFORM clause. This clause enables the integration of external scripts, such as those written in Python or Perl, to process data in a customized way within the Hive framework.

Python's integration with Hadoop is enhanced by ____ library, which allows for efficient data processing and analysis.

  • NumPy
  • Pandas
  • PySpark
  • SciPy
Python's integration with Hadoop is enhanced by the PySpark library, which provides a Python API for Apache Spark. PySpark enables efficient data processing, machine learning, and analytics, making it a popular choice for Python developers working with Hadoop.

Sqoop's ____ mode is used to secure sensitive data during transfer.

  • Encrypted
  • Kerberos
  • Protected
  • Secure
Sqoop's encrypted mode is used to secure sensitive data during transfer. By enabling encryption, Sqoop ensures that the data being transferred between systems is protected and secure, addressing concerns related to data confidentiality during the import/export process.

To implement role-based access control in Hadoop, ____ is typically used.

  • Apache Ranger
  • Kerberos
  • LDAP
  • OAuth
Apache Ranger is typically used to implement role-based access control (RBAC) in Hadoop. It provides a centralized framework for managing and enforcing fine-grained access policies, allowing administrators to define roles and permissions for Hadoop components.

What strategy does Hadoop employ to balance load and ensure data availability across the cluster?

  • Data Replication
  • Data Shuffling
  • Load Balancing
  • Task Scheduling
Hadoop employs the strategy of data replication to balance load and ensure data availability across the cluster. Data is replicated across multiple nodes, providing fault tolerance and enabling parallel processing by allowing tasks to be executed on the closest available data copy.

In Hadoop, the ____ is vital for monitoring and managing network traffic and data flow.

  • DataNode
  • NameNode
  • NetworkTopology
  • ResourceManager
In Hadoop, the NetworkTopology is vital for monitoring and managing network traffic and data flow. It represents the physical network structure, helping optimize data transfer by placing computation closer to the data source.