Core Corporate Functions>IT>Big Data>Apache Spark

Apache Spark Test to measure candidates' critical employability skills

The Apache Spark Test evaluates candidates' knowledge of the Spark framework, ability to configure Spark clusters, and skills in performing distributed processing of large data sets across computer clusters. This test measures candidates' proficiency in applying Apache Spark fundamentals, working with Spark Core and Spark Streaming/Graph X and utilizing Spark for data transformation and processing. This screening test assists in identifying data engineers and other professionals with substantial experience in the Spark framework.

Trusted By:

About This Test

Customize This Test

Assessment Features

About Mercer | Mettl Apache Spark Test

The Apache Spark Test assesses applicants' skills and abilities in using Apache Spark for large-scale data processing, data streaming, and data analytics. This test measures the candidate's skills and knowledge of the Spark framework and helps recruiters make informed hiring decisions.

What is the use of the Apache Spark Test?

The Apache Spark Test is vital for assessing candidates' expertise in one of the industry's most popular distributed data processing frameworks. It evaluates candidates' ability to work with large data sets. The assessment helps find candidates who can use Spark to develop efficient data processing systems for various roles, such as Spark developer, Spark tester, data engineer, big data engineer, etc. It covers multiple skills and evaluates candidates' ability to design and create applications with Spark and their experience with related tools.

Why is the Apache Spark Assessment important?

An Apache Spark Assessment is essential for checking a candidate's technical skills and knowledge of Apache Spark technology. As the demand for big data analysis and processing grows, businesses need a team with strong Apache Spark skills to remain competitive. Organizations can find qualified candidates who can handle big data and utilize Spark's features to build effective data processing systems by measuring their knowledge of Spark. It helps employers identify individuals who can analyze complex data and support better business decisions. With this test, employers can ensure they hire candidates with the technical expertise to meet their business needs.

Why should you use the Apache Spark Assessment?

The Mercer | Mettl 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 help recruiters hire top talents by giving them the insights they need to select suitable candidates from a large pool, thus saving the recruiter's time and resources.

This assessment evaluates 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 provides an in-depth analysis of prospective candidates' strengths and areas of improvement. Adding these pre-employment assessments to the candidate selection process aids recruiters in finding the most-suited Spark developers.

Apache Spark Test competency framework

Get a detailed look inside the test

Apache Spark Test competencies under scanner

Apache Spark skills

Competencies:

Hands-on programming

It includes Codelysis - (Java/Python) skills.

Apache Spark core concepts

It includes Apache Spark - built-in functions, Apache Spark – data frames and datasets, Apache Spark – RDD, Apache Spark – programming, Apache Spark – components and Apache Spark – features.

Apache Spark libraries

It includes Apache Spark – streaming, Apache Spark – GraphX and Apache Spark – MLlib.

Customize this Apache Spark Test

Flexible customization options to suit your needs

Set difficulty level of test

Choose easy, medium or hard questions from our skill libraries to assess candidates of different experience levels.

Combine multiple skills into one test

Add multiple skills in a single test to create an effective assessment. Assess multiple skills together.

Add your own questions to the test

Add, edit or bulk upload your own coding questions, MCQ, whiteboarding questions & more.

Request a tailor-made test

Get a tailored assessment created with the help of our subject matter experts to ensure effective screening.

The Mercer | Mettl Apache Spark Assessment advantage

Apache Spark Test can be setup in four steps

Step 1: Add test

Add this test your tests

Step 2: Share link

Share test link from your tests

Step 3: Test View

Candidate take the test

Step 4: Insightful Report

You get their tests report

Our Customers Vouch for Our Quality and Service

Frequently Asked Questions (FAQs)

1. 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.

2. 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.

3. 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.

4. 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 to gain valuable insights and a competitive edge; listed below are some of them:

Netflix
Uber
Conviva
Pinterest

5. 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.

6. 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

7. Is Spark written in Java?

Spark is written in Scala.

8. 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).

9. How does Apache Spark work?

Apache Spark has a hierarchical architecture. This architecture is based on a driver-executor 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.

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

Graph processing
Machine learning
Batch-processing

11. 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 the complete 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?