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.

Six essential features of Apache SPARK:


Apache Spark’s run workload is 100x faster than Hadoop. Spark features a state-of-the-art, advanced Directed Acyclic Graph(DAG) engine that supports in-memory computing and cyclic data flow. Spark achieves a real-time data stream using a query optimizer, DAG scheduler, and an execution engine along with batch processing.


The cross-language API enables developers to write Spark code in Java, Scala, Python, R, and SQL. Spark comes with more than 80 high-level operators that can be used from Python, R, SQL, and Scala shells to simplify building parallel apps.


Spark's generality enables users to perform streaming, SQL, and complex analytics. Spark fuels a stack of libraries that include MLlib for machine learning, SQL and DataFrames, GraphX, and Spark Streaming. These libraries can be combined seamlessly in the same application.

Runs in all quarters

Spark runs everywhere, whether it be on Apache Mesos, Hadoop, Kubernetes, in the cloud, or standalone. Its ability to access diverse data sources is phenomenal. Spark also provides its standalone cluster mode, which runs on EC2, Hadoop YARN, Kubernetes, or Mesos. Spark can access data in Alluxio, HDFS, Apache HBase, Apache Cassandra, Apache Hive, and numerous other data sources.


The thriving community of Apache Spark needs no introduction. Spark is used in multiple organizations to process large-scale datasets. Many different developers build Apache Spark from more than 300 organizations. Since 2009, over 1200 developers have associated with Spark. The project's contributors come from various organizations across the globe. Learning Apache is also easy whether a professional is from a Scala, Java, R, Python, or SQL background.

Online Apache Spark assessments for evaluating crucial skills in developing applications 

The Apache Spark test is intended for Software Developers, Software Engineers, System Programmers, IT Analysts, and Java Developers at mid and senior levels. The assessment test is designed and developed by subject matter experts to help recruiting managers evaluate the knowledge and skills of Spark Language. 

This online assessment test is used to assess the skills and abilities of professionals who want to make a career in Spark. Mercer | Mettl's Apache Spark tests are pre-employment skill assessment tests that assess candidates' prior skills and knowledge by using various questions based on the multiple components of Apache Spark.

Recruiters spend more than their fair share of time filtering out applicants and shortlisting the right ones after shuffling through numerous applications. An unstructured and half-baked screening process can be a sheer waste of time and money. Administering Apache Spark tests online is a surefire way to expedite the hiring process. Hiring managers and recruiters benefit from these Spark assessments by speeding up the screening process and identifying misfits quite early in the recruitment process.

Looking for a customised test?

Are you looking for a customised version of this test. Or looking to get a new test build according to your requirements from scratch? Reach out to our subject matter experts and discuss the same.

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.

Answer to common queries:

What is Apache Spark?

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.

What is Apache Spark used for?

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.

Is Apache Spark difficult to learn?

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.  

When should one use Apache spark?

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

Is Apache Spark an ETL tool?

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

Is it possible to learn Spark without first learning Hadoop?

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

Is Apache Spark in demand?

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.

What are the advantages of Apache Spark?

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

What kind of data can be handled by Spark?

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

Is Spark written in Java?

Spark is written in Scala.

What is Apache Spark architecture?

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)

How does Apache Spark work?

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.

In addition to stream processing jobs, what all functionality Spark provides?

  • Graph processing
  • Machine learning
  • Batch-processing

What are some common Spark SQL questions?

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?

Is there any website where one can take Spark coding challenges?

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.

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