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CRT020 : Databricks Certified Associate Developer for Apache Spark 2.4 and Python-3 - Assessment Certification Exam Preparation

  • 260+ Multiple choice Questions  and Answers for real exam
  • 56+ Assessment Questions .
Databricks has now newer version of Spark 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 much more. You can check detailed syllabus below. And for that in the same 3 Hrs. exam they are having two different exam as below.
  • Multiple Choice Exam
  • Assessment Exam : You have to complete HandsOn Exercise on the Databricks platform

HadoopExam is delighted to announce that, as previously we are also having material for preparing for this new version of the Exam. As per new syllabus we have 260+ multiple choice questions as well as 30-Assessment exercise. And soon more assessment exercise would be added.In this Exam your knowledge would be tested for the Spark 2.4 using Python-3. Since last 6 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, MapR, 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.


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Syllabus

Spark Architecture Components : Candidates are expected to be familiar with the following architectural components and their relationship to each other:

  • Driver
  • Executor
  • Cores/Slots/Threads
  • Partitions

Spark Execution : Candidates are expected to be familiar with Spark’s execution model and the breakdown between the different elements:

  • Jobs
  • Stages
  • Tasks

Spark Concepts : Candidates are expected to be familiar with the following concepts:

  • Caching
  • Shuffling
  • Partitioning
  • Wide vs. Narrow Transformations
  • DataFrame Transformations vs. Actions vs. Operations
  • High-level Cluster Configuration

DataFrames API : Candidates are expected to have a command of the following APIs.

SparkContext : Candidates are expected to know how to use the SparkContext to control basic configuration settings such as spark.sql.shuffle.partitions.

SparkSession : Candidates are expected to know how to:

  • Create a DataFrame/Dataset from a collection (e.g. list or set)
  • Create a DataFrame for a range of numbers
  • Access the DataFrameReaders
  • Register User Defined Functions (UDFs).

DataFrameReader : Candidates are expected to know how to:

  • Read data for the “core” data formats (CSV, JSON, JDBC, ORC, Parquet, text and tables)
  • How to configure options for specific formats
  • How to read data from non-core formats using format() and load()
  • How to specify a DDL-formatted schema
  • How to construct and specify a schema using the StructType classes

DataFrameWriter : Candidates are expected to know how to:

  • Write data to the “core” data formats (csv, json, jdbc, orc, parquet, text and tables)
  • Overwriting existing files
  • How to configure options for specific formats
  • How to write a data source to 1 single file or N separate files
  • How to write partitioned data
  • How to bucket data by a given set of columns

DataFrame (Dataset) :

  • Have a working understanding of every action such as take(), collect(), and foreach()
  • Have a working understanding of the various transformations and how they work such as producing a distinct set, filtering data, repartitioning and coalescing, performing joins and unions as well as producing aggregates
  • Know how to cache data, specifically to disk, memory or both
  • Know how to uncache previously cached data
  • Converting a DataFrame to a global or temp view.
  • Applying hints

Row & Column : Candidates are expected to know how to work with row and columns to successfully extract data from a DataFrame

Spark SQL Functions : When instructed what to do, candidates are expected to be able to employ the multitude of Spark SQL functions. Examples include, but are not limited to:

  • Aggregate functions: getting the first or last item from an array or computing the min and max values of a column.
  • Collection functions: testing if an array contains a value, exploding or flattening data.
  • Date time functions: parsing strings into timestamps or formatting timestamps into strings
  • Math functions: computing the cosign, floor or log of a number
  • Misc functions: converting a value to crc32, md5, sha1 or sha2
  • Non-aggregate functions: creating an array, testing if a column is null, not-null, nan, etc
  • Sorting functions: sorting data in descending order, ascending order, and sorting with proper null handling
  • String functions: applying a provided regular expression, trimming string and extracting substrings.
  • UDF functions: employing a UDF function.
  • Window functions: computing the rank or dense rank.


  • Datastax Apache Cassandra 3.x Developer Associate Certification Exam : Total 258 Questions (New) 
  • Datastax Apache Cassandra 3.x Administrator Associate Certification Exam : Total 214 Questions (New)
  • Apache Cassandra Interview Preparation : Total 185+ Interview Questions & Video Cum Audio Books (New) 




       

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