Search test library by skills or roles
⌘ K

About the test:

The Apache Spark Online Test evaluates the candidate's ability to transform structured data with RDD API and SparkSQL (Datasets and DataFrames), convert big-data challenges into iterative/ multi-stage Spark scripts, optimize existing Spark jobs using partitioning/ caching and analyze graph structures using GraphX.

Covered skills:

  • Fundamentals of Spark Core
  • Spark Resilient Distributed Datasets (RDD)
  • Dataframes and Datasets
  • Running Spark on a cluster
  • Tuning and troubleshooting Spark jobs in a cluster
  • Migrating data from data sources/ databases
  • Developing and running Spark jobs (Java; Scala; Python)
  • Data processing with Spark SQL
  • Spark Streaming to process real-time data
  • Implementing iterative and multi-stage algorithms
  • Graph/ Network analysis with GraphX library

9 reasons why
9 reasons why

Adaface Spark Test is the most accurate way to shortlist Spark Developers

Reason #1

Tests for on-the-job skills

The Spark Online Test helps recruiters and hiring managers identify qualified candidates from a pool of resumes, and helps in taking objective hiring decisions. It reduces the administrative overhead of interviewing too many candidates and saves time by filtering out unqualified candidates at the first step of the hiring process.

The test screens for the following skills that hiring managers look for in candidates:

  • Understanding the fundamentals and architecture of Spark Core
  • Developing and running Spark jobs using Java, Scala, and Python
  • Working with Resilient Distributed Datasets (RDD) in Spark
  • Performing data processing with Spark SQL
  • Manipulating data using Dataframes and Datasets in Spark
  • Implementing Spark Streaming to process real-time data
  • Deploying and running Spark on a cluster
  • Applying iterative and multi-stage algorithms in Spark
  • Tuning and troubleshooting Spark jobs in a cluster
  • Conducting graph and network analysis using the GraphX library
  • Managing data migration from various sources and databases
Reason #2

No trick questions

no trick questions

Traditional assessment tools use trick questions and puzzles for the screening, which creates a lot of frustration among candidates about having to go through irrelevant screening assessments.

View sample questions

The main reason we started Adaface is that traditional pre-employment assessment platforms are not a fair way for companies to evaluate candidates. At Adaface, our mission is to help companies find great candidates by assessing on-the-job skills required for a role.

Why we started Adaface
Reason #3

Non-googleable questions

We have a very high focus on the quality of questions that test for on-the-job skills. Every question is non-googleable and we have a very high bar for the level of subject matter experts we onboard to create these questions. We have crawlers to check if any of the questions are leaked online. If/ when a question gets leaked, we get an alert. We change the question for you & let you know.

How we design questions

These are just a small sample from our library of 10,000+ questions. The actual questions on this Spark Test will be non-googleable.

🧐 Question


Character count
Penny created a jar file for her character count example written in Java. The jar name is attempt.jar and the main class is, which requires an input file name and output directory as input parameters. Which of the following is the correct command to submit a job in Spark with the given constraints?


File system director
Spark Scala API
Spark Streaming
Review the following Spark job description:

1. Monitor file system director for new files. 
2. For new files created in the “/rambo” dictionary, perform word count.

Which of the following snippets would achieve this?


Spark Scala API
Consider the following Spark DataFrame:
Which of the given code fragments produce the following result:
🧐 Question🔧 Skill


Character count

2 mins



File system director
Spark Scala API
Spark Streaming

3 mins



Spark Scala API

4 mins

🧐 Question🔧 Skill💪 Difficulty⌛ Time
Character count
Easy2 mins
File system director
Spark Scala API
Spark Streaming
Medium3 mins
Spark Scala API
Medium4 mins
Reason #4

1200+ customers in 75 countries

customers in 75 countries

With Adaface, we were able to optimise our initial screening process by upwards of 75%, freeing up precious time for both hiring managers and our talent acquisition team alike!

Brandon Lee, Head of People, Love, Bonito

Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment Spark Test in your hiring process is that it is an elimination tool, not a selection tool. In other words: you want to use the test to eliminate the candidates who do poorly on the test, not to select the candidates who come out at the top. While they are super valuable, pre-employment tests do not paint the entire picture of a candidate’s abilities, knowledge, and motivations. Multiple easy questions are more predictive of a candidate's ability than fewer hard questions. Harder questions are often "trick" based questions, which do not provide any meaningful signal about the candidate's skillset.

Science behind Adaface tests
Reason #6

1 click candidate invites

Email invites: You can send candidates an email invite to the Spark Test from your dashboard by entering their email address.

Public link: You can create a public link for each test that you can share with candidates.

API or integrations: You can invite candidates directly from your ATS by using our pre-built integrations with popular ATS systems or building a custom integration with your in-house ATS.

invite candidates
Reason #7

Detailed scorecards & benchmarks

View sample scorecard
Reason #8

High completion rate

Adaface tests are conversational, low-stress, and take just 25-40 mins to complete.

This is why Adaface has the highest test-completion rate (86%), which is more than 2x better than traditional assessments.

test completion rate
Reason #9

Advanced Proctoring

Learn more

About the Spark Assessment Test

Why you should use Pre-employment Spark Online Test?

The Spark Test makes use of scenario-based questions to test for on-the-job skills as opposed to theoretical knowledge, ensuring that candidates who do well on this screening test have the relavant skills. The questions are designed to covered following on-the-job aspects:

  • Fundamentals of Spark Core
  • Developing and running Spark jobs in Java, Scala, and Python
  • Understanding Spark Resilient Distributed Datasets (RDD)
  • Data processing with Spark SQL
  • Working with Dataframes and Datasets in Spark
  • Utilizing Spark Streaming for real-time data processing
  • Running Spark on a cluster
  • Implementing iterative and multi-stage algorithms in Spark
  • Tuning and troubleshooting Spark jobs in a cluster
  • Performing Graph/Network analysis with GraphX library in Spark

Once the test is sent to a candidate, the candidate receives a link in email to take the test. For each candidate, you will receive a detailed report with skills breakdown and benchmarks to shortlist the top candidates from your pool.

What topics are covered in the Spark Online Test?

  • Fundamentals of Spark Core

    Understanding Spark Core involves knowledge of the basic building blocks and execution model of Apache Spark, such as RDDs, transformations, and actions. This skill is necessary to develop efficient and scalable Spark applications.

  • Developing and running Spark jobs (Java; Scala; Python)

    Developing and running Spark jobs requires proficiency in programming languages like Java, Scala, or Python. This skill is crucial to write Spark applications using Spark APIs, perform data processing tasks, and leverage the power of Spark's distributed computing capabilities.

  • Spark Resilient Distributed Datasets (RDD)

    Spark RDDs are fundamental data structures in Spark that allow for distributed data processing and fault tolerance. Understanding RDDs is essential for efficient data manipulation, transformation, and parallel computing in Spark.

  • Data processing with Spark SQL

    Spark SQL is a module in Spark that provides a programming interface for querying structured and semi-structured data using SQL-like syntax. This skill is important to analyze and process structured data using SQL operations and leverage the optimizations provided by Spark SQL's query engine.

  • Dataframes and Datasets

    Dataframes and Datasets are higher-level abstractions built on top of RDDs in Spark. They provide a more expressive and efficient way to work with structured and unstructured data. Understanding Dataframes and Datasets is crucial for performing data manipulations, transformations, and aggregations efficiently in Spark.

  • Spark Streaming to process real-time data

    Spark Streaming is a scalable and fault-tolerant stream processing library in Spark that allows for real-time data processing. This skill is important to handle continuous streams of data and perform real-time analytics, enabling applications to react to data changes in near real-time.

  • Running Spark on a cluster

    Running Spark on a cluster involves configuring and deploying Spark applications across a distributed cluster infrastructure. This skill is necessary to take advantage of Spark's distributed computing capabilities and ensure optimal performance and scalability.

  • Implementing iterative and multi-stage algorithms

    Implementing iterative and multi-stage algorithms in Spark involves designing and optimizing algorithms that require multiple iterations or stages to achieve the desired output. This skill is important for tasks like machine learning and graph processing that often involve complex iterative and multi-stage computations.

  • Tuning and troubleshooting Spark jobs in a cluster

    Tuning and troubleshooting Spark jobs in a cluster requires expertise in identifying and resolving performance issues, optimizing resource utilization, and ensuring fault tolerance. This skill is crucial to maximize the efficiency and reliability of Spark applications running on a distributed cluster.

  • Graph/ Network analysis with GraphX library

    GraphX is a graph computation library in Spark that provides an API for graph processing and analysis. Understanding GraphX is important for tasks like social network analysis, recommendation systems, and fraud detection that involve analyzing relationships and patterns in graph data.

  • Migrating data from data sources/ databases

    Migrating data from data sources or databases to Spark involves understanding various data ingestion techniques, such as batch processing, streaming, and data connectors. This skill is necessary to efficiently transfer and process data from external sources in Spark for further analysis and computation.

  • Full list of covered topics

    The actual topics of the questions in the final test will depend on your job description and requirements. However, here's a list of topics you can expect the questions for Spark Test to be based on.

    Spark RDD
    Spark DataFrame
    Spark Dataset
    Spark SQL
    Spark Streaming
    Spark GraphX
    Spark Cluster
    Spark Graph and Network Analysis
    Spark Iterative Algorithms
    Spark Multi-stage Algorithms
    Spark Job Tuning
    Spark Job Troubleshooting
    Spark Data Migration
    Spark Core Fundamentals
    Java Spark Development
    Scala Spark Development
    Python Spark Development
    Data Processing in Spark
    Real-time Data Processing in Spark
    Spark Architecture
    Handling Exceptions and Errors in Spark
    Spark Data Manipulation
    Spark Data Aggregation
    Spark Data Filtering
    Spark Data Transformation
    Spark Data Visualization
    Spark Data Joins
    Spark Data Partitioning
    Spark Data Caching
    Spark Data Serialization
    Spark Data Compression
    Spark Data Sources
    Spark Data Loading
    Spark Data Saving
    Spark Data Exploration
    Spark Data Preprocessing
    Spark Data Analytics
    Spark Data Mining
    Spark Data Quality
    Spark Data Integration
    Spark Data Streaming
    Spark Data Pipelines
    Spark Data Storage
    Spark Data Security
    Spark Data Access Control
    Spark Data Backup
    Spark Data Recovery
    Spark Data Replication
    Spark Data Compression
    Spark Data Encryption
    Spark Data Schema
    Spark Data Serialization
    Spark Data Indexing
    Spark Data Visualization
    Spark Data Benchmarking
    Spark Machine Learning
    Spark Deep Learning
    Spark Neural Networks
    Spark Graph Algorithms
    Spark Social Network Analysis
    Spark Community Detection
    Spark Clustering
    Spark Classification
    Spark Regression
    Spark Anomaly Detection
    Spark Recommendation Systems
    Spark Sentiment Analysis
    Spark Natural Language Processing
    Spark Geospatial Analysis
    Spark Time Series Analysis
    Spark Collaborative Filtering
    Spark Dimensionality Reduction
    Spark Model Evaluation
    Spark Feature Engineering
    Spark Feature Selection
    Spark Hyperparameter Tuning
    Spark Model Deployment
    Spark Model Monitoring
    Spark Model Interpretability

What roles can I use the Spark Online Test for?

  • Spark Developer
  • Software Developer - Spark
  • Big Data Engineer
  • Senior Spark Developer
  • Scala Big Data Developer
  • Senior Big Data Engineer
  • Spark Engineer

How is the Spark Online Test customized for senior candidates?

For intermediate/ experienced candidates, we customize the assessment questions to include advanced topics and increase the difficulty level of the questions. This might include adding questions on topics like

  • Migrating data from various data sources/databases
  • Working with Spark MLlib for machine learning tasks
  • Optimizing Spark performance using caching and persistence
  • Using Spark for natural language processing (NLP) tasks
  • Implementing Spark for real-time analytics
  • Understanding and managing Spark Executors and Workers
  • Utilizing Spark for large-scale data processing
  • Implementing Spark for real-time data visualization
  • Integrating Spark with other Big Data technologies like Hadoop and Cassandra
  • Implementing Spark on cloud platforms for scalability and flexibility
Singapore government logo

The hiring managers felt that through the technical questions that they asked during the panel interviews, they were able to tell which candidates had better scores, and differentiated with those who did not score as well. They are highly satisfied with the quality of candidates shortlisted with the Adaface screening.

reduction in screening time

Spark Hiring Test FAQs

How is the test customized based on programming languages?

Spark supports different programming languages like Java, Scala, Python and R. We customize Spark tests according to programming language in the following ways:

  • The code snippets in scenario-based Spark MCQ questions will be of the programming language you pick
  • MCQ questions designed to evaluate the particular programming language will be added to the assessment
  • Coding questions to be programmed in the chosen programming language will be added to the assessment

You can check our standard Java, Scala, and Python tests to get a sense of question quality.

Can I combine multiple skills into one custom assessment?

Yes, absolutely. Custom assessments are set up based on your job description, and will include questions on all must-have skills you specify. Here's a quick guide on how you can request a custom test.

Do you have any anti-cheating or proctoring features in place?

We have the following anti-cheating features in place:

  • Non-googleable questions
  • IP proctoring
  • Screen proctoring
  • Web proctoring
  • Webcam proctoring
  • Plagiarism detection
  • Secure browser
  • Copy paste protection

Read more about the proctoring features.

How do I interpret test scores?

The primary thing to keep in mind is that an assessment is an elimination tool, not a selection tool. A skills assessment is optimized to help you eliminate candidates who are not technically qualified for the role, it is not optimized to help you find the best candidate for the role. So the ideal way to use an assessment is to decide a threshold score (typically 55%, we help you benchmark) and invite all candidates who score above the threshold for the next rounds of interview.

What experience level can I use this test for?

Each Adaface assessment is customized to your job description/ ideal candidate persona (our subject matter experts will pick the right questions for your assessment from our library of 10000+ questions). This assessment can be customized for any experience level.

Does every candidate get the same questions?

Yes, it makes it much easier for you to compare candidates. Options for MCQ questions and the order of questions are randomized. We have anti-cheating/ proctoring features in place. In our enterprise plan, we also have the option to create multiple versions of the same assessment with questions of similar difficulty levels.

I'm a candidate. Can I try a practice test?

No. Unfortunately, we do not support practice tests at the moment. However, you can use our sample questions for practice.

What is the cost of using this test?

You can check out our pricing plans.

Can I get a free trial?

Yes, you can sign up for free and preview this test.

I just moved to a paid plan. How can I request a custom assessment?

Here is a quick guide on how to request a custom assessment on Adaface.

customers across world
Join 1200+ companies in 75+ countries.
Try the most candidate friendly skills assessment tool today.
g2 badges
Ready to use the Adaface Spark Test?
Ready to use the Adaface Spark Test?
40 min tests.
No trick questions.
Accurate shortlisting.
Terms Privacy Trust Guide

🌎 Pick your language

English Norsk Dansk Deutsche Nederlands Svenska Français Español Chinese (简体中文) Italiano Japanese (日本語) Polskie Português Russian (русский)
● Online
Score: NA