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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 .
- 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.
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