Hadoop Online Test

The Hadoop Online Test uses scenario-based MCQ questions to evaluate candidates' ability to install Hadoop clusters on the cloud, run optimized MapReduce jobs on Hadoop clusters and write efficient Pig instructions and Hive queries to perform data analysis on complex datasets. The test screens for core Hadoop framework knowledge that hiring managers look for in Hadoop developers and administrators.

Covered skills:

Installing and configuring Hadoop clusters
Core Hadoop architecture (HDFS; YARN; MapReduce)
Writing efficient Hive and Pig queries
Publishing data to clusters
Handling streaming data
Working with different file formats
Troubleshooting and monitoring

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About the Hadoop Test

The Hadoop 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:

Ability to install and configure Hadoop clusters
Understanding of core Hadoop architecture (HDFS, YARN, MapReduce)
Proficiency in writing efficient Hive and Pig queries
Capability to publish data to clusters
Able to handle streaming data
Knowledge of working with different file formats
Skill in troubleshooting and monitoring

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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 15,000+ questions. The actual questions on this Hadoop Online Test will be non-googleable.

🧐 Question
Easy Count number of occurrences Mappers Reducers Data Transformation Mapreduce	Solve
Chusk works as Hadoop developer at Pesla Inc. Chusk is tasked with processing input data to count number of occurrences of each unique word. Chusk did the following to achieve this: 1. Tokenize each word and emit lateral value 1 with Mapper 2. Reducer increments counter for each literal 1 it receives Chusk is now tasked with optimizing this by using a combiner. Will Chusk be able to reuse existing reducers as combiners? A: Yes B: No C: Because the sum operation is both associative and commutative and the input and output types to the reduce method match D: Because the sum operation in the Reducer is incompatible with the operation of a combiner E: Because the combiner is incompatible with a Mapper, which doesn't use the same data type for both the key and value F: Insufficient information
Medium Hive ngrams Hive Query Understanding Data Analysis	Solve
Assuming the following Hive statements execute successfully, choose the correct statements that describe the result: from fooddata select context_ngrams(sentences(lines), array("twiggy", "romato", null), 68); A. A bigram of the top 68 sentences that contain the substring "twiggy romato" in the lines column of the input data A1 table. B. An 68-value ngram of sentences that contain the words "twiggy" or "romato" in the lines column of the fooddata table. C. A trigram of the top 68 sentences that contain "twiggy romato" followed by a null space in the lines column of the fooddata table. D. A frequency distribution of the top 68 words that follow the subsequence "twiggy romato" in the lines column of the fooddata table.
Easy P Q relations Pig	Solve
Consider the following two relations, P and Q: What is the output of the following Pig command? Q = GROUP P BY p2; DUMP Q;

	🧐 Question	🔧 Skill
	Easy Count number of occurrences Mappers Reducers Data Transformation Mapreduce	3 mins Hadoop	Solve
Chusk works as Hadoop developer at Pesla Inc. Chusk is tasked with processing input data to count number of occurrences of each unique word. Chusk did the following to achieve this: 1. Tokenize each word and emit lateral value 1 with Mapper 2. Reducer increments counter for each literal 1 it receives Chusk is now tasked with optimizing this by using a combiner. Will Chusk be able to reuse existing reducers as combiners? A: Yes B: No C: Because the sum operation is both associative and commutative and the input and output types to the reduce method match D: Because the sum operation in the Reducer is incompatible with the operation of a combiner E: Because the combiner is incompatible with a Mapper, which doesn't use the same data type for both the key and value F: Insufficient information
	Medium Hive ngrams Hive Query Understanding Data Analysis	2 mins Hadoop	Solve
Assuming the following Hive statements execute successfully, choose the correct statements that describe the result: from fooddata select context_ngrams(sentences(lines), array("twiggy", "romato", null), 68); A. A bigram of the top 68 sentences that contain the substring "twiggy romato" in the lines column of the input data A1 table. B. An 68-value ngram of sentences that contain the words "twiggy" or "romato" in the lines column of the fooddata table. C. A trigram of the top 68 sentences that contain "twiggy romato" followed by a null space in the lines column of the fooddata table. D. A frequency distribution of the top 68 words that follow the subsequence "twiggy romato" in the lines column of the fooddata table.
	Easy P Q relations Pig	2 mins Hadoop	Solve
Consider the following two relations, P and Q: What is the output of the following Pig command? Q = GROUP P BY p2; DUMP Q;

	🧐 Question	🔧 Skill	💪 Difficulty	⌛ Time
	Count number of occurrences Mappers Reducers Data Transformation Mapreduce	Hadoop	Easy	3 mins	Solve
Chusk works as Hadoop developer at Pesla Inc. Chusk is tasked with processing input data to count number of occurrences of each unique word. Chusk did the following to achieve this: 1. Tokenize each word and emit lateral value 1 with Mapper 2. Reducer increments counter for each literal 1 it receives Chusk is now tasked with optimizing this by using a combiner. Will Chusk be able to reuse existing reducers as combiners? A: Yes B: No C: Because the sum operation is both associative and commutative and the input and output types to the reduce method match D: Because the sum operation in the Reducer is incompatible with the operation of a combiner E: Because the combiner is incompatible with a Mapper, which doesn't use the same data type for both the key and value F: Insufficient information
	Hive ngrams Hive Query Understanding Data Analysis	Hadoop	Medium	2 mins	Solve
Assuming the following Hive statements execute successfully, choose the correct statements that describe the result: from fooddata select context_ngrams(sentences(lines), array("twiggy", "romato", null), 68); A. A bigram of the top 68 sentences that contain the substring "twiggy romato" in the lines column of the input data A1 table. B. An 68-value ngram of sentences that contain the words "twiggy" or "romato" in the lines column of the fooddata table. C. A trigram of the top 68 sentences that contain "twiggy romato" followed by a null space in the lines column of the fooddata table. D. A frequency distribution of the top 68 words that follow the subsequence "twiggy romato" in the lines column of the fooddata table.
	P Q relations Pig	Hadoop	Easy	2 mins	Solve
Consider the following two relations, P and Q: What is the output of the following Pig command? Q = GROUP P BY p2; DUMP Q;

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Why you should use Pre-employment Hadoop Online Test?

The Hadoop Online 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:

Installing and configuring Hadoop clusters
Core Hadoop architecture including HDFS, YARN, and MapReduce
Writing efficient Hive and Pig queries
Publishing data to clusters
Handling streaming data
Working with different file formats
Troubleshooting and monitoring
Optimizing Hadoop cluster performance
Implementing data security in Hadoop
Designing and implementing Hadoop data models

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 Hadoop Online Test?

Installing and configuring Hadoop clusters: This skill involves setting up and configuring multiple machines to work together as a distributed cluster, with Hadoop software installed on each node. It requires knowledge of system administration, networking, and understanding the Hadoop ecosystem.

Core Hadoop architecture (HDFS; YARN; MapReduce): This skill covers the fundamental components of Hadoop, including the Hadoop Distributed File System (HDFS) which provides reliable storage, YARN for resource management and job scheduling, and MapReduce for processing large datasets in parallel. Understanding this architecture is crucial for designing and optimizing Hadoop applications.

Writing efficient Hive and Pig queries: This skill involves writing queries in Hive and Pig, which are high-level languages used for processing and analyzing data in Hadoop. Writing efficient queries is important for achieving optimal performance and extracting valuable insights from large datasets.

Publishing data to clusters: This skill requires the ability to import data from external sources into Hadoop clusters, ensuring data is properly formatted and optimized for storage. It also involves managing data replication, compression, and security to ensure data integrity and availability within the cluster.

Handling streaming data: This skill involves processing and analyzing real-time streaming data in Hadoop. It requires knowledge of technologies like Apache Kafka or Apache Storm to ingest and process data as it is generated, enabling real-time analytics and decision-making.

Working with different file formats: This skill involves working with various file formats, such as CSV, JSON, Avro, or Parquet, in Hadoop. It requires understanding file formats and their specific advantages and trade-offs for different use cases, as well as knowledge of tools to process and transform data in different formats.

Troubleshooting and monitoring: This skill is about identifying and resolving issues in Hadoop clusters, such as performance bottlenecks, data inconsistencies, or configuration problems. It involves using various monitoring tools and diagnostic techniques to ensure the smooth operation of the Hadoop environment.

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 Hadoop Online Test to be based on.

Installing Hadoop

Configuring Hadoop clusters

Hadoop distributed file system (HDFS)

YARN resource management

MapReduce programming model

Hive query language

Pig scripting

Publishing data to Hadoop clusters

Processing streaming data in Hadoop

Working with Avro file format

Working with Parquet file format

Working with ORC file format

Handling JSON data in Hadoop

How to troubleshoot Hadoop performance issues

Monitoring Hadoop clusters

Securing Hadoop clusters

Data ingestion techniques in Hadoop

Data processing workflows in Hadoop

Optimizing Hadoop jobs

Understanding Hadoop ecosystem components

Working with Hadoop command line tools

Analyzing Hadoop logs

Managing Hadoop clusters

Backup and recovery strategies for Hadoop

Working with Apache Spark

Hadoop cluster tuning

Handling data skew in Hadoop

Using Hadoop connectors

Hadoop data compression techniques

Data serialization in Hadoop

Hadoop cluster deployment models

Design patterns for Hadoop

Data repartitioning in Hadoop

Hadoop high availability

Job scheduling in Hadoop

Data partitioning in Hadoop

Understanding Hadoop distributed cache

Hadoop data locality

Using Hadoop streaming

File input/output in Hadoop

Understanding Hadoop configuration files

YARN application lifecycle

Hadoop input/output formats

Speculative execution in Hadoop

Understanding Hadoop resource management

Hadoop security mechanisms

Managing Hadoop users and groups

Hadoop cluster sizing and capacity planning

Hadoop data encryption

HDFS data replication

Working with Hadoop libraries

Working with Hadoop ecosystem tools

What roles can I use the Hadoop Online Test for?

Hadoop Admin
Hadoop Engineer
Hadoop Tester
Hadoop Application Developer
Big Data Developer - Hadoop
Senior Big Data Engineer - Hadoop
Senior Hadoop Administrator

How is the Hadoop 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

Experience with Hadoop ecosystem tools like Spark, HBase, and Impala
Managing and tuning Hadoop clusters
Fine-tuning and optimizing MapReduce jobs
Experience with Hadoop data ingestion and processing tools
Implementing high availability and disaster recovery for Hadoop clusters
Writing and optimizing Apache Hive queries for large datasets
Experience with data warehousing concepts and technologies
Designing and implementing data pipelines in Hadoop
Working with distributed computing frameworks in Hadoop
Experience with real-time data processing in Hadoop

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Have questions about the Hadoop Hiring Test?

What is the Hadoop Online Test?

The Hadoop Online Test is designed to assess candidates' proficiency in various Hadoop-related skills. Used by recruiters and hiring managers, it helps identify individuals knowledgeable in Hadoop clusters, core architecture, and real-time data processing.

Can I combine Hadoop Online Test with Hive questions?

Yes, recruiters can request a custom test that includes both Hadoop and Hive Test questions. This allows for a comprehensive assessment of candidates' abilities in both areas.

What skills are evaluated in the Hadoop Online Test?

The test covers skills like installing and configuring Hadoop clusters, core Hadoop architecture (HDFS, YARN, MapReduce), writing Hive and Pig queries, and handling streaming data, among others.

How to use the Hadoop Online Test in my hiring process?

We recommend using the Hadoop Online Test as a pre-screening tool at the beginning of your recruitment process. Include a test link in your job post or invite candidates via email to identify the most skilled candidates early.

Can I test Hadoop and SQL together in a test?

Yes, you can. Combining Hadoop with SQL skills provides a more complete evaluation. Check out our C#, .NET & SQL Test for an example of a combined skills assessment.

What are the main Big Data tests?

We offer a range of Big Data tests including the Hadoop Online Test, Pig Test, MapReduce Test and Spark Test.

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.

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