Apache Spark 3.0: Databricks Certified Associate Developer Interview Questions
Apache Spark has become the industry standard, open source cluster processing framework. And becoming a Databricks Certified Associate Developer for Apache Spark 3.0 will certainly give a strong boost to your career opportunities. Saying so, to pass the interview, you need to demonstrate your mastery of Spark’s capabilities and knowledge of using the Spark DataFrame API to complete individual data manipulation tasks. Additionally, you ought to have a good understanding of the architecture of Apache Spark 3.0, and also the core APIs to manipulate complex data by writing SQL queries, including joining, grouping, and aggregating.
Passing the Apache Spark 3.0: Databricks Certified Associate Developer interview is not an easy thing. Now is your time to prepare yourself for a world of opportunity. So below is a comprehensive list of the highly expected questions for the Apache Spark 3.0: Databricks Certified Associate Developer interview, to give you a head start. Let’s begin!
1. Can you tell what Apache Spark is used for?
Apache Spark, an open source big data processing framework based on the Scala programming language, provides in-memory caching and leverages optimized query execution for fast analytical queries against large datasets
2. How does Apache Spark works?
Apache Spark is capable of reading data from a number of data storage systems, including Hadoop Distributed File System (HDFS), NoSQL databases, and relational data stores, such as Apache Hive Spark Core relies on resilient distributed datasets, or RDDs, to process information.
3. Is it possible to run spark on computer clusters?
Spark uses resilient distributed datasets (RDDs) to achieve parallel processing across a cluster. Spark provides simple APIs for operating on large datasets in various programming languages such as Java, Scala and Python.
4. Can you explain how Apache Spark runs on a cluster?
Spark applications run as separate sets of processes that coordinate with the SparkContext object in your main program (called the driver program). When running an application, Spark acquires executors on nodes in the cluster, which are processes that run computations and store data for your application.
5. Could you tell me about Apache Spark and Hadoop?
Hadoop, an Apache framework for processing data, relies on the MapReduce system; Spark, another popular Apache project, performs big data tasks more swiftly using resilient distributed datasets (RDDs).
6. What makes Apache Spark different from MapReduce?
Spark is not a database, but many people view it as one because of its SQL-like capability. Spark can operate on files on disk just like MapReduce, but it uses memory extensively. Spark’s in-memory data processing speeds make it up to 100 times faster than MapReduce.
7. What is cluster mode and client mode in Spark?
Cluster mode puts the Spark driver in an application master process managed by YARN on the cluster. In client mode, the driver can run in the client process without an application master, which simplifies deployment and makes the driver easier to test.
8. Can you elaborate on the advantages and limitations of Apache Spark when compared with MapReduce?
Apache Spark is known for its speed. It processes data in-memory and on-disk 100 times faster than Hadoop MapReduce, a competing product. This is because Apache Spark loads data from memory to make calculations and then persists the resulting data back onto disk.
9. How would you define spark optimization?
Apache Spark optimization is designed to process data under certain circumstances such as analytics or data movement. This processing can be accomplished most efficiently if the data is in a better-serialized format.
10. How is the execution of the Spark application controlled?
The Driver is the Spark application that clients use to submit applications for execution. The Driver coordinates and controls the execution of Spark programs and returns status and/or results (data) to the client.
11. Can you name the elements of Apache spark execution hierarchy?
- The Spark driver
- The Spark executors
- The cluster manager
- Cluster mode
- Client mode
- Local mode
12. What do you know about DataFrame API in Spark?
DataFrames provide an API for manipulating data in Spark. This API requires fewer boilerplate methods than those used when working directly with Scala code, and thus is the more user friendly option.
13. Can you differentiate between DataFrame and spark SQL?
A Spark DataFrame is a distributed collection of rows with the same schema. It is essentially a Dataset organized into named columns. Note that Datasets are an extension of the DataFrame API—type-safe, object-oriented programming interface.
14. What is the difference between DataFrame and dataset in Spark?
A DataFrame is conceptually the same as any collection of objects. However, a DataFrame is an implementation of a Dataset in Spark. Dataset is a strongly-typed API for interacting with structured data represented in Scala or Java.
15. Could you explain why DataFrames are not type safe?
DataFrame’s elements are of the Row type, which is not parameterized by a type at compile time. Because of this, DataFrame cannot be checked for type compatibility at compile time, making it untyped and not type-safe.
16. Which is better RDD or DataFrame?
To group data, you can use the RDD class. It provides an easy API for aggregating the data but takes a bit longer to do so than do Dataframes and Datasets. Datasets are faster than RDDs but slower than Dataframes when it comes to grouping data.
17. What is meant by manipulation of data?
Data manipulation is the process of adjusting data so it’s easier to read and understand. A programming language called DML (data manipulation language) makes this adjustment possible.
18. What is the difference between union and union all?
Union and Union All are similar except that Union only selects the rows specified in the query, while Union All selects all the rows including duplicates (repeated values) from both queries.
19. What is a transformation in Apache Spark?
RDD.sparkTransformation is a function that takes RDDs as input and produces new RDDs as output. It uses an existing RDD and applies transformations to it to create new ones. Each time it creates a new RDD, we call an action on the existing RDD. As RDDs are immutable, you cannot change their contents.
20. What is the difference between an action and a transformation in Apache Spark?
Spark supports two different kinds of functions: transformations and actions. Transformations change data, while actions don’t change the dataset but give an output.
21. Can you name the ways of Spark deployment?
There are three ways of Spark deployment namely Standalone, Hadoop Yarn, Spark in MapReduce.
22. What are UDFs in Spark?
Like many things, user-defined functions (UDFs) require a set of classes for creation and registration. This documentation not only lists those required classes but also provides examples for defining and registering UDFs in Spark SQL.
23. Could you explain how you use spark UDF?
When you use Spark with Scala, you can create a UDF by writing a Scala function and wrapping it in the udf() function or registering it as UDF to use it on DataFrame and SQL respectively.
24. Can you explain why is UDF important?
A User-Defined Function, or UDF for short, is a part of many SQL environments that allows developers to extend the system’s built-in functionality. This is done by abstracting their lower-level language implementations.
25. Are spark UDFs distributed?
Yes, the spark data frame is distributed to different partitions. Spark does not stop a UDF from running on each partition.
26. Can you name all the functions of Spark SQL?
- Spark SQL Functions
- String Functions.
- Date & Time Functions.
- Collection Functions.
- Math Functions.
- Aggregate Functions.
- Window Functions.
27. Is spark SQL faster than SQL?
Spark SQL performs 3.2 times faster than Big SQL on average. If you extrapolate the test duration across the average I/O rate, Spark reads 11.7 times as much data as Big SQL and writes 30 times more data.
28. What all functions are provided by Spark?
Spark Core contains most of Spark’s features, including: components for task scheduling, memory management, fault recovery, interacting with storage systems, and more. Spark Core also houses the API that defines resilient distributed datasets (RDDs), which are Spark’s main programming constructs.
29. What is Apache Spark code?
Spark provides a programming environment to create applications capable of processing large and complex data sets. The project was founded in 2009 by researchers at the University of California, Berkeley’s AMPLab.
30. Where can I run spark?
Scala and Java users can include Spark in their projects using its Maven coordinates, and Python users can install Spark from PyPI. Spark runs on Windows, UNIX-like systems (e.g. Linux and Mac OS), and it should run on any platform that runs a supported version of Java.
