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Spark CRT020 : Databricks Certified Associate Developer for Spark 2.4 and Scala 2.11 - Assessment Certification Exam
- 240+ Multiple choice Questions and Answers for real exam
- 40 Assessment Questions for Hands On section
- Multiple Choice Exam
- Assessment Exam : You have to complete HandsOn Exercises on the Databricks platform
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material for preparing for this new version of the Exam. As per
new syllabus of Spark CRT020 Certifications we have 240+ multiple
choice questions as well as 40-Assessment exercise. And soon more
assessment exercise would be added.In this Exam your knowledge
would be tested for the Spark 2.4 using Scala-2.11. Since last 6
years BigData and AI, one of the fastest growing technology and
Spark is one of them for sure. Every BigData solution provider has
to adopt Spark technology on their platform whether its Cloudera,
Hortonworks, MapR, IBM, Cassandra, Azure, or AWS etc. All these
companies know 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)
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