Search results
45 Apache Spark interview questions
- To help you spot the best Apache Spark talent, we've put together a list of 45 Apache Spark interview questions and built a comprehensive Spark test.
www.testgorilla.com/blog/spark-interview-questions/
People also ask
How many Apache Spark interview questions are there?
What are some popular Apache Spark interview questions for 2024?
Does Apache Spark have a checkpoint API?
Should I include an Apache Spark test in my application?
What is Apache Spark?
Is Apache Spark a good choice for big data projects?
4 days ago · In this blog, we have curated 100 most important Apache Spark interview questions, catering to a range of expertise from beginners to experienced professionals.
- This is a straightforward question that requires you to give a duration for which you have worked remotely. For example, if you have been working r...
- The interviewer wants to assess the ease with which you can keep in touch and seamlessly work with a remote team. Outline your experience working w...
- The interviewer wants to assess whether you can complete tasks on time without being monitored. Outline your turn-around time for different tasks.
- This question is an extension of the previous question. The interviewer wants to ensure that you are well-organized and can deliver tasks on time....
- The interviewer may want to know what resources you may need to perform your work efficiently. These may include access to specific software. Be su...
- This is an extension of the previous question. Think of a scenario where the lack of a particular resource was a challenge. Maybe you found a free...
- The interviewer wants to know that you are serious about your work and that you won’t be constantly disturbed. Unlike an office, a house may have s...
- The remote developer jobs interview questions are designed to assess the body of work that you have successfully completed in the remote environmen...
- Through the remote programmer jobs interview questions, the interviewer wants to know whether there are sustainable and strong reasons for you to c...
- Outline skills like task-focus, time-management, careful planning, and the ability to shut out distractions. Additionally, you can also talk about...
Here we have compiled a list of the top Apache Spark interview questions. These will help you gauge your Apache Spark preparation for cracking that upcoming interview. Do you think you can get the answers right?
- Spark Core acts as the base engine for large-scale parallel and distributed data processing. It is the distributed execution engine used in conjunc...
- GraphX – Implements graphs and graph-parallel computationMLib – Used for machine learningSpark Core – Base engine used for large-scale parallel and...
- GraphX is the API used for implementing graphs and graph-parallel computing in Apache Spark. It extends the Spark RDD with a Resilient Distributed...
- Spark SQL modules help in integrating relational processing with Spark’s functional programming API. It supports querying data via SQL or HiveQL (H...
- Parquet is a columnar format that is supported by several data processing systems. With it, Spark SQL performs both read as well as write operation...
- Apache Mesos – Commonly used cluster managerStandalone – A basic cluster manager for setting up a clusterYARN – Used for resource management
- Yes, it is possible to use Apache Spark for accessing as well as analyzing data stored in Cassandra databases using the Spark Cassandra Connector....
- Enhanced Speed – MapReduce makes use of persistent storage for carrying out any of the data processing tasks. On the contrary, Spark uses in-memory...
- Having compatibility with Hadoop is one of the leading advantages of Apache Spark. The duo makes up for a powerful tech pair. Using Apache Spark an...
- How to Programmatically Specify A Schema For Dataframe?
- Does Apache Spark Provide Checkpoints?
- What Do You Mean by Sliding Window Operation?
- What Are The Different Levels of Persistence in Spark?
- How Would You Compute The Total Count of Unique Words in Spark?
- What Are The Different MLlib Tools Available in Spark?
- What Are The Different Data Types Supported by Spark MLlib?
- What Is A Sparse vector?
- Describe How Model Creation Works with MLlib and How The Model Is applied.
- What Are The Functions of Spark Sql?
DataFrame can be created programmatically with three steps: 1. Create an RDD of Rows from the original RDD; 2. Create the schema represented by a StructType matching the structure of Rows in the RDD created in Step 1. 3. Apply the schema to the RDD of Rows via createDataFrame method provided by SparkSession.
This is one of the most frequently asked spark interview questions where the interviewer expects a detailed answer (and not just a yes or no!). Give as detailed an answer as possible here. Yes, Apache Spark provides an API for adding and managing checkpoints. Checkpointing is the process of making streaming applications resilient to failures. It al...
Controlling the transmission of data packets between multiple computer networks is done by the sliding window. Spark Streaming library provides windowed computations where the transformations on RDDs are applied over a sliding window of data.
DISK_ONLY - Stores the RDD partitions only on the disk MEMORY_ONLY_SER - Stores the RDD as serialized Java objects with a one-byte array per partition MEMORY_ONLY - Stores the RDD as deserialized Java objects in the JVM. If the RDD is not able to fit in the memory available, some partitions won’t be cached OFF_HEAP - Works like MEMORY_ONLY_SER but ...
1. Load the text file as RDD: sc.textFile(“hdfs://Hadoop/user/test_file.txt”); 2. Function that breaks each line into words: def toWords(line): return line.split(); 3. Run the toWords function on each element of RDD in Spark as flatMap transformation: words = line.flatMap(toWords); 4. Convert each word into (key,value) pair: def toTuple(word): retu...
ML Algorithms: Classification, Regression, Clustering, and Collaborative filteringFeaturization: Feature extraction, Transformation, Dimensionality reduction,Spark MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices. Local Vector: MLlib supports two types of local vectors - dense and sparse Example: vector(1.0, 0.0, 3.0) dense format: [1.0, 0.0, 3.0] sparse format: (3, [0, 2]. [1.0, 3.0]) Labeled point: A labeled point is a local vector, either dense or ...
A Sparse vector is a type of local vector which is represented by an index array and a value array. public class SparseVector extends Object implements Vector Example: sparse1 = SparseVector(4, [1, 3], [3.0, 4.0]) where: 4 is the size of the vector [1,3] are the ordered indices of the vector [3,4] are the value Do you have a better example for this...
MLlib has 2 components: Transformer: A transformer reads a DataFrame and returns a new DataFrame with a specific transformation applied. Estimator: An estimator is a machine learning algorithm that takes a DataFrame to train a model and returns the model as a transformer. Spark MLlib lets you combine multiple transformations into a pipeline to appl...
Spark SQL is Apache Spark’s module for working with structured data. Spark SQL loads the data from a variety of structured data sources. It queries data using SQL statements, both inside a Spark program and from external tools that connect to Spark SQL through standard database connectors (JDBC/ODBC). It provides a rich integration between SQL and ...
Jun 27, 2024 · Essential Spark interview questions with example answers for job-seekers, data professionals, and hiring managers. Jun 27, 2024. Apache Spark is a unified analytics engine for data engineering, data science, and machine learning at scale. It can be used with Python, SQL, R, Java, or Scala.
- How does Spark differ from Hadoop, and what advantages does it offer for big data processing? Spark differs from Hadoop primarily in its data processing approach and performance.
- Can you explain the architecture of Spark, highlighting the roles of key components such as the Driver Program, Cluster Manager, and the Executors? Apache Spark’s architecture follows a master/worker paradigm, with the Driver Program acting as the master and Executors as workers.
- What is the role of the DAG scheduler in Spark, and how does it contribute to optimizing query execution? The DAG scheduler in Spark plays a crucial role in optimizing query execution by transforming the logical execution plan into a physical one, consisting of stages and tasks.
- What are the key differences between RDD, DataFrame, and Dataset in Spark, and when would you choose to use each one? RDD (Resilient Distributed Dataset) is Spark’s low-level data structure, providing fault tolerance and parallel processing.
Follow along and learn the 23 most common and advanced Apache Spark interview questions and answers to prepare for your next big data and machine learning interview. Q1 : Briefly compare Apache Spark vs Apache Hadoop
To help you spot the best Apache Spark talent, we've put together a list of 45 Apache Spark interview questions and built a comprehensive Spark test. Read on to find out more. Table of contents. How can you assess a candidate’s Spark skills? Top 20 Spark interview questions to hire the best talent.
