PR000023 : Databricks Certified Associate Developer for Apache Spark 3.0 - Scala Certification Exam Preparation: 340+ Multiple choice Questions and Answers for real exam
Databricks® has now newer version of Spark 3.x Certification in which they would be testing your concepts, underline Spark Engine Knowledge, How Spark works, What is Catalyst optimizer and how it works, and extensively on DataFrame API and Some part of Spark SQL functions. See FAQ at the end of the page and Syllabus for more detail. And for that in the same 2 Hrs. exam they are having only one section as below. It is very difficult to find the detail about the questions and answers which are asked by Databricks during the exam and to the point preparation material is available only on HadoopExam.com . In this new certification exam Databricks is focusing more on the DataFrame, SparkSQL and Architecture of Spark.
HadoopExam is delighted to announce the availabilty of certification preparation material for this new exam as well. All questions and answers are based on the completely new syllabus, currently we are providing 230+ multiple choice questions and few of the fill in the blanks questions to test DataFrame API extensively. In this Exam your knowledge would be tested for the Spark 3.x using Python. Since last 8 years in the BigData world, one of the fastest growing technology is Spark for sure. Every BigData solution provider has to adopt Spark technology on their platform whether its Cloudera, Hortonworks, HP-MapR, Azure Cloud, IBM etc. All these companies knows the power of Spark and the way Spark had changed the BigData, Analytics and Data Science industry. At the same time Spark itself had changed a lot to make itself gold standard BigData technology and one big driver behind that is Databricks. There are mainly two topics which would be tested for this certification and these are below.
- Basics of Spark Architecture and Adaptive Query Execution Framework
- Be able to apply the Spark DataFrame API to complete individual data manipulation task, including:
- Selecting, renaming and manipulating columns
- Filtering, dropping, sorting, and aggregating rows
- Joining, reading, writing and partitioning DataFrames
- Working with UDFs and Spark SQL functions
|During the exam you will be given input and expected output and you need to select the correct code segment which can produce the output result. Even you would be given sample data and need to find the min, max, avg, rename column, add new column in the final output. Another possible couple of questions would be on Join. Hence, you must be aware about all kind of joins like left, right, outer, inner, semi, anti joins. There would be many questions in which you would be asked to find the correct code segment for achieving desired result.
|No, keep in mind in this new certification exam Databricks is not asking any question based on the RDD. Hence, you can safely ignore the RDD API while preparing for this certification exam.
|You would find 3-4 questions around Spark Architecture. Specially prepare for the Adaptive Query Execution framework. You will certainly have questions around that. HadoopExam certification simulator has around 25+ questions based on AQE framework. So please go through all those questions including the explanation given in the answer. Another couple of questions would be around following concepts
- What is narrow and wide transformations?
- How a simple or a complex query in Spark is executed?
- What means ‘Lazy evaluation’, ‘Actions’, ‘Transformations’?
- What all are Cluster deployment and which one to choose and when?
|No, all these topics are not part of this new certification. You can safely ignore these topics. Thats is where HadoopExam is providing questions and answer for preparing this certification, which include the entire syllabus and no questions would be given out of syllabus.
|You should have good understanding for the following concepts, each one would have around 1 question.
- Broadcast Hash Joins
- Broadcast variables
- DataFrame and DataSet Persistence
- Passing functions to Spark
- DataFrame Coalesce, repartition
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