Hadoop Interview Questions and Answers for Beginners

Introduction

Big Data testing is an integral part of data science because it ensures that the vast amounts of data we work with are accurate and reliable, much like checking if the ingredients we're using for a recipe are of good quality before cooking. Knowing about Big Data testing in interviews shows that you can handle data well and solve problems effectively. These big data testing interview questions and answers cover many topics to help you ace your data science interview.

Q1: What Is Hadoop Big Data Testing?

A: Hadoop Big Data Testing verifies the quality and functionality of systems handling large volumes of structured and unstructured data. Traditional methods need help to process such vast amounts of data efficiently. Testing Big Data involves using specialized tools, frameworks, and methods to effectively manage and analyze these massive datasets.

Q2: How Is Data Quality Tested?

A: Data quality testing is crucial in Big Data testing. It involves checking various aspects such as accuracy, completeness, consistency, reliability, and data validity. Before testing begins, ensuring that the data being analyzed meets specific quality standards is essential.

Q3: What Do You Think About Testing Big Data? Can You Name Some Tools Used For It?

A: Because of the huge amount of data, Testing Big Data requires advanced tools, special frameworks, and good methods. Some common tools used are MongoDB, MapReduce, Cassandra, Apache Hadoop, Apache Pig, and Apache Spark.

Q4: What's Needed For A Good Testing Setup?

A: The proper testing setup depends on the application. Here's what you typically need:

• Enough space to process data and store test information.

• Good CPU use and not too much memory usage for better performance.

• A setup with different parts connected for testing data spread out.

Q5: What Is Architecture Testing?

A: Architecture Testing ensures that the various components of a system's architecture function well together. In the context of Big Data, it involves checking the functionality and efficiency of components like data sources, storage, batch processing, real-time message handling, stream processing, and analytical data storage. This testing helps prevent performance issues and ensures the system meets expectations.

Q6: When Should MapReduce Be Used With Big Data?

A: MapReduce is a model for processing big data in parallel. It's commonly used with Apache Hadoop to query and manipulate data stored in HDFS. MapReduce is great for tasks like querying and summarizing large datasets, especially when processing data in parallel. It's also useful for iterative tasks that involve a lot of data and need parallel processing.

Q7: What Is Grid Search?

A: Grid search is the quest for the right meta-parameters in training. It is difficult to predict how varying the learning rate or batch size in stochastic gradient descent affects the quality of the final model. Multiple independent fits can be run in parallel, and in the end, we choose the best one according to our evaluation.

Q8: Define Hashing And Hash Function.

A: Hashing is a technique that can often turn quadratic algorithms into linear time algorithms, making them tractable for dealing with the scale of data we hope to work with.

A hash function h maps an object x to a specific integer h(x). The key idea is that whenever x = y, h(x) = h(y). Thus, we can use h(x) as an integer to index an array and collect all similar objects in the same place. Different items are usually mapped to different places, assuming a well-designed hash function, but there are no guarantees.

Q9: What Is Feature Selection?

A: Feature selection is the process of choosing specific data attributes from a large dataset for analysis. This helps focus on relevant information and reduces processing time and resource usage. There are different methods for feature selection:

• Filters Method: This method ranks features based on their importance and usefulness.

• Wrappers Method: It uses induction algorithms to select features that can be used to build a classifier.

• Embedded Method: This method combines the efficiency of filters and wrappers for feature selection.

Q10: What Are The Performance Test Parameters?

A: Performance testing involves evaluating several parameters to ensure the system performs optimally. These parameters include:

• Data storage distribution across different system nodes.

• Confirmation of commit logs generation.

• Determination of concurrency levels for read and write operations.

• Optimization of caching settings like "key cache" and "row cache."

• Setting query timeout limits.

• Configuring Java Virtual Machine (JVM) parameters like garbage collection algorithms and heap size.

• Map-reduce optimization for tasks like merging.

• Monitoring message queue size and message rates.

Q11: What Are The Main Parts Of Hadoop?

A: Hadoop is a tool for handling big data across multiple computers.

Key Components of Hadoop:

• HDFS (Hadoop Distributed File System): This is where large amounts of data are stored. It's designed to handle massive datasets spread across different computers.

• Hadoop MapReduce: This part of Hadoop processes data. It splits tasks into smaller chunks and processes them simultaneously across different computers. There are two main stages: "Map," where data is prepared for processing, and "Reduce," where processed data is combined.

• YARN manages resources in Hadoop and supports various data processing tasks, such as real-time streaming and batch processing.

Q12: What's The Difference Between Testing Big Data And Regular Databases?

A: Here's a comparison:

Traditional Database Testing	Big Data Testing
Uses tools like Excel macros or UI-based automation tools.	It doesn't need specific tools; testing methods are more flexible.
Testing tools are simple and don't only need a little specialized knowledge.	Big Data testers need special training and keep updating their skills.
Works with organized and compact data.	Big Data includes both organized and unorganized data.
Testing methods are well-established and transparent.	Big Data testing methods are still improving and need ongoing research.

Q13: How Is HDFS Different From Traditional NFS?

A: NFS (Network File System) is a way for computers to access files over a network. It makes remote files appear as if they're stored locally.

HDFS (Hadoop Distributed File System), on the other hand, is designed to store files across multiple computers. It's fault-tolerant, meaning it can handle failures without losing data. HDFS stores multiple copies of files by default.

The main difference is in fault tolerance. HDFS can handle failures because it keeps multiple copies of files, while NFS doesn't have built-in fault tolerance.

Benefits of HDFS over NFS:

• Fault Tolerance: HDFS can recover from failures because it stores multiple copies of files.

• Scalability: HDFS spreads files across multiple computers, so it can handle many users accessing files simultaneously without slowing down.

• Performance: Reading data from HDFS is faster because files are stored on multiple disks, unlike NFS, which stores files on a single disk.

Q14: What Are The Different Ways Hadoop Can Be Set Up?

A: Hadoop can run in three modes:

• Local Mode or Standalone Mode: Hadoop runs as a single Java process in this mode and uses the local file system instead of HDFS. It's good for debugging and doesn't require complex configurations.

• Pseudo-distributed Mode: Each part of Hadoop runs on a separate Java process, requiring some custom configuration. HDFS is used for storage. This mode is helpful for testing and debugging.

• Fully Distributed Mode: This is Hadoop's production mode, where different machines in a cluster take on specific roles like NameNode and Resource Manager. It requires detailed configurations but offers full distributed computing capabilities, security, fault tolerance, and scalability.

Q15: What Are Traditional Algorithm Analyses Based On?

A: Traditional algorithm analysis is based on an abstract computer called the Random Access Machine or RAM. On such a model:

• Each simple operation takes precisely one step.

• Each memory operation takes precisely one step.

Hence, counting up the operations performed over the course of the algorithm gives its running time. Generally speaking, the number of operations performed by any algorithm is a function of the size of the input n: a matrix with n rows, a text with n words, and a point set with n points. Algorithm analysis estimates or bounds the number of steps the algorithm takes as a function of n.

Q16: What Are The Three Vs Of Big Data?

A: Management consulting types have latched onto a notion of the three Vs. of big data to explain it: the properties of volume, variety, and velocity. They provide a foundation to talk about what makes big data different. The Vs are:

• Volume: Big data is more significant than little data. The distinction is one of class. We have left the world where we can represent our data in a spreadsheet or process it on a single machine. This requires developing a more sophisticated computational infrastructure and restricting our analysis to linear-time algorithms for efficiency.

• Variety: Ambient data collection typically moves beyond the matrix to amass heterogeneous data, often requiring ad hoc integration techniques.

• Velocity: Collecting data from ambient sources implies that the system is live, meaning it is constantly collecting data. In contrast, the data sets we have studied have generally been dead, meaning they were collected once and stuffed into a file for later analysis.

Q17: What Are Some Applications Of Hash Functions?

A: Hash functions are beneficial things. Major applications include:

Dictionary maintenance: A hash table is an array-based data structure using h(x) to define an object's position, coupled with an appropriate collision-resolution method. Properly implemented, such hash tables yield constant time (or O(1)) search times in practice.

Frequency counting: A common task in analyzing logs is tabulating the frequencies of given events, such as word counts or page hits. The fastest/easiest approach is to set up a hash table with event types as the key and increment the associated counter for each new event. 

Duplicate removal: A necessary data cleaning chore is identifying duplicate records in a data stream and removing them. These are all the email addresses we have of our customers, and we want to make sure we only spam each of them once. 

Canonization: Often, the same object can be referred to by multiple different names. Vocabulary words are generally case-insensitive, meaning that "The" is equivalent to "the." Determining a language's vocabulary requires unifying alternate forms and mapping them to a single key.

Cryptographic hashing: Hashing can be used to monitor and constrain human behavior by constructing concise and noninvertible representations. How can you prove that an input file remains unchanged since you last analyzed it? Construct a hash code or checksum for the file when you worked on it, and save this code for comparison with the file hash at any future time. They will be the same if the file is unchanged and almost surely differ if any alterations have occurred.

Q18: Explain The Levels Of The Storage Hierarchy.

A: The significant levels of the storage hierarchy are:

Cache memory: Modern computer architectures feature a complex system of registers and caches to store working copies of the data actively being used. Some of this is used for prefetching, grabbing larger blocks of data around memory locations that have been recently accessed in anticipation of them being needed later. 

Main memory: This holds the general state of the computation and where large data structures are hosted and maintained. Main memory is generally measured in gigabytes and runs hundreds to thousands of times faster than disk storage. To the greatest extent possible, we need data structures that fit into main memory and avoid the paging behavior of virtual memory.

Main memory on another machine: Latency times on a local area network run into the low-order milliseconds, making it generally faster than secondary storage devices like disks. This means that distributed data structures like hash tables can be meaningfully maintained across networks of machines but with access times that can be hundreds of times slower than main memory.

Disk storage: Secondary storage devices can be measured in terabytes, providing the capacity that enables big data to get big. Physical devices like spinning disks take considerable time to move the read head to the position where the data is. Once there, reading a large block of data is relatively quick. This motivates prefetching, copying large chunks of files into memory under the assumption that they will be needed later.

Q19: What Are Some Reasons For Subsampling Good Data?

A: Sampling means arbitrarily selecting an appropriate size subset without domain-specific criteria. There are several reasons why we may want to subsample good, relevant data:

Right-sizing training data: Simple, robust models generally have few parameters, making big data unnecessary to fit them. Subsampling your data unbiasedly leads to efficient model fitting but is still representative of the entire data set.

Data partitioning: Model-building hygiene requires cleanly separating training, testing, and evaluation data, typically in a 60%, 20%, and 20% mix. Unbiased partition construction is necessary for the integrity of this process.

Exploratory data analysis and visualization: Spreadsheet-sized data sets are fast and easy to explore. An unbiased sample is representative of the whole while remaining comprehensible.

Sampling records in an efficient and unbiased manner is a more subtle task than it may appear at first. There are two general approaches, deterministic and randomized, detailed in the following sections.

Q20: What Are A Few Factors That Affect Truncated Samples?

A: The order of records in a file often encodes semantic information, meaning that truncated samples often contain subtle effects from factors such as:

Temporal biases: Log files are typically constructed by appending new records to the end of the file. Thus, the first n records would be the oldest available and would not reflect recent regime changes.

Lexicographic biases: Many files are sorted according to the primary key, which means that the first n records are biased to a particular population. Imagine a personnel roster sorted by name. The first n records might consist only of the As, so we will probably over-sample Arabic names from the general population and under-sample Chinese ones.

Numerical biases: Files are often sorted by identity numbers, which may appear arbitrarily defined. But ID numbers can encode meaning. Consider sorting the personnel records by their U.S. social security numbers. The first five digits of social security numbers are generally a function of the year and place of birth. Thus, truncation leads to a geographically and age-biased sample.

Q21: What Are Some Biases And Limitations Of Big Data?

A: Big data can be an excellent resource. But it is particularly prone to biases and limitations that make it difficult to draw accurate conclusions from, including:

Unrepresentative participation: Any ambient data source has sampling biases. The data from any particular social media site does not reflect the people who don't use it, and you must be careful not to overgeneralize.

Spam and machine-generated content: Big data sources are worse than unrepresentative. Often, they have been engineered to be deliberately misleading. Any online platform large enough to generate enormous amounts of data is large enough for there to be economic incentives to pervert it. 

Too much redundancy: Many human activities follow a power law distribution, meaning that a tiny percentage of the items account for a large percentage of the total activity. This law of unequal coverage implies that much of the data we see through ambient sources is something we have seen before. Removing this duplication is an essential cleaning step for many applications.

Susceptibility to temporal bias: Products change in response to competition and changes in consumer demand. Often, these improvements change the way people use these products. A time series resulting from ambient data collection might well encode several product/interface transitions, making distinguishing artifacts from signals hard.

Q22: What Are The Two Approaches To Simultaneously Computing With Multiple Machines?

A: There are two distinct approaches to simultaneously computing with multiple machines: parallel and distributed computing. The distinction is how tightly coupled the machines are and whether the tasks are CPU-bound or memory/IO-bound. Roughly:

1.  Parallel processing happens on one machine, involving multiple cores and processors that communicate through threads and operating system resources. Such tightly coupled computation is often CPU-bound, limited more by the number of cycles than the movement of data through the machine. The emphasis is solving a particular computing problem faster than one could sequentially.

2.  Distributed processing happens on many machines using network communication. The potential scale here is enormous, but it is most appropriate for loosely coupled jobs that communicate little. The goal of distributed processing often involves sharing resources like memory and secondary storage across multiple machines, more so than exploiting multiple CPUs. Whenever the data speed from a disk is the bottleneck, we are better off having many machines reading as many different disks as possible simultaneously.

Q23: What Is A MapReduce Runtime System?

A: The major component of MapReduce environments for Hadoop or Spark is their runtime system, the layer of software that regulates such tasks as:

Processor scheduling: Which cores get assigned to running which map and reduce tasks, and on which input files? The programmer can help by suggesting how many mappers and reducers should be active at any time, but the assignment of jobs to cores is up to the runtime system.

Data distribution: This might involve moving data to an available processor that can deal with it, but recall that typical map and reduce operations require simple linear sweeps through potentially large files. Moving a file is more expensive than just doing the locally desired computation.

Synchronization: Reducers can't run until something has been mapped to them, and can't complete until after the mapping is done. Spark permits more complicated work flows, beyond synchronized rounds of map and reduce. It is the runtime system that handles this synchronization.                                                                                                    
Error and fault tolerance: The reliability of MapReduce requires recovering gracefully from hardware and communications failures. When the runtime system detects a worker failure, it attempts to restart the computation. When this fails, it transfers the uncompleted tasks to other workers. That this all happens seamlessly, without the programmer's involvement, enables us to scale computations to large networks of machines on the scale where hiccups become likely instead of rare events.

Conclusion

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