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Online Spark Assessments to Measure Critical Employability Skills: Find Spark Professionals Faster than Ever!

Apache Spark is a general-purpose distributed cluster-computing framework. It's a unified analytics engine for big data processing. Although primarily developed at the AMPLab at UC Berkeley,  the Spark codebase was soon handed out to the Apache Software Foundation, which has retained it.

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Inside This Assessment

Questions in the Spark Assessment test span across the topics given below :

  • Datasets
  • Installation
  • Introduction
  • Programming in Spark
  • RDD
  • Scala
  • SQL
  • Streaming
  • Apache Spark Architecture
  • Deployment and Programming
  • Introduction to Apache Spark
  • RDD Programming
  • Spark SQL
  • SparkR
  • GraphX Programming
  • Performance Tuning
  • Spark MLlib
  • Spark Streaming
  • Structured Streaming

Key profiles the Apache Spark test is useful for:

  • Spark Developer
  • Hadoop/Spark Developer
  • Java Spark Developer
  • Machine Learning Engineer – Spark
  • Spark/Scala Engineer
  • Spark Data Engineer

Why should you use Mercer | Mettl's Spark assessments?

Mettl's Apache Spark test has been designed especially for assessing a candidate's job prospects by measuring job readiness and employability skills, with the increased emphasis being placed on gauging an individual's applied skills gained through professional experience rather than theoretical understanding. 

Apache Spark assessments will help recruiters hire the best talent by providing them with the insights they need to filter out unsuitable candidates from a large candidate pool, thus saving the recruiter's time and resources.

This assessment test is specifically intended to evaluate a Spark engineer's practical implementation skills – as per industry standards. The Spark skill test is developed, reviewed, and validated by subject matter experts.

The assessment will provide an in-depth analysis of the strengths and areas of improvement of prospective candidates. By adding these pre-employment assessments to the candidate selection process, you could be just one test away from finding your perfect Spark developer.

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Frequently Asked Questions (FAQs)

Apache Spark is a robust open-source framework built for distributed data processing. Its speed, usability, and advanced analytics, combined with the provision for Scala, Python, R, SQL, and Java APIs, make it very popular and useful. Spark is 100 times faster than Hadoop MapReduce in memory and ten times faster on disk.

Apache Spark is an open-source, distributed data processing framework used for big data processing. Its explicit in-memory cache and optimized query execution are used for performing fast queries against copious amounts of data.

Learning Spark is not hard if you are well-versed in Python or any other programming language because Spark provides APIs in Java, Scala, R, SQL, and Python.  

Here are some critical use cases of Apache Spark technology:
 
 Streaming data - it is widely used for processing streaming data and analyzing it in real-time
 Machine learning - it is commonly used for processing machine learning algorithms
 Interactive analytics - it is also used for its interactive analytics and visualization capabilities
 Fog computing- its application in fog computing for analyzing and working on IoT data is evident
 Many companies are leveraging Spark for getting valuable insights and gaining a competitive edge; listed below are some of them:
 
 Netflix
 Uber
 Conviva
 Pinterest

Apache Spark is a highly sought-after big data tool that enables developers to write ETL with the utmost ease.

No, learning Hadoop is not a prerequisite for learning Spark. 

Apache Spark tool is in great demand, and professionals in this field are highly sought-after in the job market. If combined with various other big data tools, it strengthens the overall portfolio. Nowadays, the big data market is thriving, and many professionals are already making the most of it.

Apache Spark is widely popular for a host of reasons, such as:
 
 Speed
 User-friendly
 Advanced analytics
 Dynamic nature
 Multilingual
 Powerful
 Enhanced access to big data
 Largest open-source community

Spark can process data in HBase, HDFS, Cassandra, Hive, and any other Hadoop InputFormat.

Spark is written in Scala.

Apache Spark has a well-structured layered architecture in which all the Spark layers and components are loosely coupled. This architecture is further combined with different extensions and libraries. The Spark architecture premised on two key abstractions: Directed Acyclic Graph (DAG) and Resilient Distributed Dataset (RDD)

Apache Spark has a hierarchical architecture. This architecture is based on a master-slave pattern wherein the Spark driver acts as the master node that manages the cluster manager, which controls the slave nodes and presents data results to an application client.
 
 Dependent on the application code, Spark driver creates the SparkContext, which works with either Spark’s own standalone cluster manager or various other cluster managers such as Kubernetes, Mesos, or YARN - to allocate and manage execution throughout the nodes. It also creates an RDD, short for Resilient Distributed Datasets, which are the driving force behind Spark’s impeccable processing speed.

Graph processing
 Machine learning
 Batch-processing

Listed below are some Spark SQL questions that are commonly asked during an interview:
 
 Can you list down some typically used Spark Ecosystems?
 What do you understand by the term ''Spark SQL''?
 Is real-time processing using Spark SQL feasible?
 What are the essential libraries that make up the Spark ecosystem?
 What is a Parquet file?
 Can you list down the entire functions of Spark SQL?
 Is SparkSQL different from SQL and HQL?
 What is the Catalyst framework?
 How much different would the Hadoop and Spark be in terms of usability?    
 Can you enumerate some benefits of Spark over MapReduce?
 How to use Spark with Hive?
 What is a Parquet file in Spark?
 What is the use of BlinkDB?

Although many websites provide a plethora of Apache Spark mock practice questions, we would recommend building your complete projects that would be immensely beneficial instead of participating in online quizzes. It's a great way to apply working knowledge and assess learning by undertaking projects based on real-world challenges.