Which language is commonly used for writing scripts that can be processed by Hadoop Streaming?
- C++
- Java
- Python
- Ruby
Python is commonly used for writing scripts that can be processed by Hadoop Streaming. The flexibility of Hadoop Streaming allows the use of scripting languages, and Python is a popular choice for its simplicity and readability.
In complex Hadoop applications, ____ is a technique used for isolating performance bottlenecks.
- Caching
- Clustering
- Load Balancing
- Profiling
Profiling is a technique used in complex Hadoop applications to identify and isolate performance bottlenecks. It involves analyzing the execution of the code to understand resource utilization, execution time, and memory usage, helping developers optimize performance-critical sections.
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.
What strategy does Parquet use to enhance query performance on columnar data in Hadoop?
- Compression
- Data Encoding
- Indexing
- Predicate Pushdown
Parquet enhances query performance through Predicate Pushdown. This strategy involves pushing parts of the query execution directly to the storage layer, reducing the amount of data that needs to be processed by the query engine. This is particularly effective for columnar data storage, like Parquet, where only relevant columns are read during query execution.
For a company dealing with sensitive information, which Hadoop component should be prioritized for enhanced security during cluster setup?
- DataNode
- JobTracker
- NameNode
- ResourceManager
For enhanced security in a Hadoop cluster dealing with sensitive information, prioritizing the security of the NameNode is crucial. The NameNode contains metadata and information about data locations, making it a potential target for security threats. Securing the NameNode helps safeguard sensitive data in the cluster.
MapReduce ____ is an optimization technique that allows for efficient data aggregation.
- Combiner
- Mapper
- Partitioner
- Reducer
MapReduce Combiner is an optimization technique that allows for efficient data aggregation before sending data to the reducers. It helps reduce the amount of data shuffled across the network, improving overall performance in MapReduce jobs.
Which component of Apache Pig translates scripts into MapReduce jobs?
- Pig Compiler
- Pig Engine
- Pig Parser
- Pig Server
The component of Apache Pig that translates scripts into MapReduce jobs is the Pig Compiler. It takes Pig Latin scripts as input and converts them into a series of MapReduce jobs that can be executed on a Hadoop cluster for data processing.
Apache Spark's ____ feature allows for dynamic allocation of resources based on workload.
- ClusterManager
- DynamicExecutor
- ResourceManager
- SparkAllocation
Apache Spark's ClusterManager feature allows for dynamic allocation of resources based on workload. The ClusterManager dynamically adjusts the resources allocated to Spark applications based on their needs, optimizing resource utilization.
In Hadoop, ____ is a key aspect of managing and optimizing cluster performance.
- Data Encryption
- Data Replication
- Data Serialization
- Resource Management
Resource management is a key aspect of managing and optimizing cluster performance in Hadoop. Tools like YARN (Yet Another Resource Negotiator) play a crucial role in efficiently allocating and managing resources for running applications in the Hadoop cluster.