MapReduce Online Test

The MapReduce Online Test uses scenario-based MCQs to evaluate candidates on their knowledge of MapReduce framework, including their proficiency in working with Hadoop, HDFS, and YARN. The test also evaluates a candidate's familiarity with Pig and Hive for data analysis and their ability to work with Big Data technologies. The test aims to evaluate a candidate's ability to design and develop applications using MapReduce framework and related technologies effectively.

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Test duration: ~ 30 mins

Difficulty level: Moderate

Availability: Available as custom test

Questions:

8 MapReduce MCQs
7 Hadoop MCQs

Covered skills:

MapReduce

Big Data Processing

Distributed Computing

Data Analysis

Hadoop

Data Processing

Parallel Computing

Data Aggregation

Data Transformation

Performance Optimization

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Use the MapReduce Test to shortlist qualified candidates

The MapReduce 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 write efficient MapReduce programs
Understanding of Big Data processing principles
Knowledge of distributed computing concepts
Proficiency in data analysis techniques
Experience with Hadoop framework
Ability to process large volumes of data
Understanding of parallel computing principles
Skills in data aggregation and summarization
Proficiency in data transformation and manipulation
Knowledge of performance optimization techniques

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Screen candidates with the highest quality 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 MapReduce Test will be non-googleable.

🧐 Question
Easy Count number of occurrences Mappers Reducers	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	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	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	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	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	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;

Test candidates on core MapReduce Hiring Test topics

MapReduce: MapReduce is a programming model and software framework used for processing and generating large datasets in a distributed computing environment. It allows for parallel execution of data processing tasks across a cluster of computers, making it suitable for big data processing. Assessing MapReduce skills in this test will help recruiters evaluate candidates' ability to efficiently utilize this important technique in big data processing.

Big Data Processing: Big data processing involves the management and analysis of large volumes of complex data from various sources. It requires techniques and tools, such as MapReduce, to efficiently process and extract meaningful insights from the data. Evaluating candidates' skills in big data processing will help recruiters identify individuals who can handle the challenges related to working with massive datasets.

Distributed Computing: Distributed computing refers to the use of multiple computers to solve a problem or perform a task. It allows for parallel processing and can significantly improve overall performance and scalability. Measuring candidates' skills in distributed computing is essential as it indicates their ability to design and implement scalable and efficient solutions in a distributed environment.

Data Analysis: Data analysis involves the exploration, transformation, and modeling of data to extract valuable insights and support decision-making. Assessing candidates' skills in data analysis enables recruiters to identify individuals who can effectively analyze and interpret complex data sets, providing valuable insights to drive business outcomes.

Hadoop: Hadoop is an open-source framework that provides a distributed file system and supports the processing of big data using the MapReduce programming model. Evaluating candidates' Hadoop skills is crucial as it demonstrates their proficiency in utilizing this powerful tool for managing and processing large datasets.

Data Processing: Data processing refers to the manipulation and transformation of data to extract useful information or prepare it for further analysis. Assessing candidates' skills in data processing ensures that they can effectively manage and clean large datasets, enhancing their ability to work with big data effectively.

Parallel Computing: Parallel computing involves dividing a problem into smaller tasks that can be executed simultaneously on multiple processors or computers. It enables faster processing of complex computations and is particularly useful in big data processing. Measuring candidates' skills in parallel computing helps identify individuals capable of designing and implementing parallel algorithms for efficient data processing.

Data Aggregation: Data aggregation is the process of collecting and summarizing data from multiple sources into a single, easily manageable form. It plays a crucial role in big data processing as it allows for efficient storage and retrieval of relevant information. Evaluating candidates' skills in data aggregation ensures that they can effectively collect and consolidate data from different sources, supporting more advanced data analysis tasks.

Data Transformation: Data transformation involves converting data from one format or structure to another, often to prepare it for analysis or integration with other systems. It is an essential step in the data processing pipeline and requires knowledge of various techniques and tools. Measuring candidates' skills in data transformation helps recruiters identify individuals who can efficiently manipulate and reshape data to meet specific requirements.

Performance Optimization: Performance optimization involves enhancing the efficiency, speed, and scalability of software and systems. Evaluating candidates' skills in performance optimization is important as it indicates their ability to identify and resolve bottlenecks, improve computational efficiency, and optimize resource utilization. This skill is particularly relevant in the context of big data processing, where performance impacts the processing of massive datasets.

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