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Which of the following describes a way for resizing a DataFrame from 16 to 8 partitions in the most efficient way?
Use a narrow transformation to reduce the number of partitions.
Correct! DataFrame.coalesce(n) is a narrow transformation, and in fact the most efficient way to resize the DataFrame of all options listed. One would run DataFrame.coalesce(8) to resize the
Use operation DataFrame.coalesce(8) to fully shuffle the DataFrame and reduce the number of partitions.
Wrong. The coalesce operation avoids a full shuffle, but will shuffle data if needed. This answer is incorrect because it says "fully shuffle" – this is something the coalesce operation will not do. As a
general rule, it will reduce the number of partitions with the very least movement of data possible. More info: distributed computing - Spark - repartition() vs coalesce() - Stack Overflow
Use operation DataFrame.coalesce(0.5) to halve the number of partitions in the DataFrame.
Incorrect, since the num_partitions parameter needs to be an integer number defining the exact number of partitions desired after the operation. More info: pyspark.sql.DataFrame.coalesce —
PySpark 3.1.2 documentation
Use operation DataFrame.repartition(8) to shuffle the DataFrame and reduce the number of partitions.
No. The repartition operation will fully shuffle the DataFrame. This is not the most efficient way of reducing the number of partitions of all listed options.
Use a wide transformation to reduce the number of partitions.
No. While possible via the DataFrame.repartition(n) command, the resulting full shuffle is not the most efficient way of reducing the number of partitions.
Which of the following is a problem with using accumulators?
Accumulator values can only be read by the driver, but not by executors.
Correct. So, for example, you cannot use an accumulator variable for coordinating workloads between executors. The typical, canonical, use case of an accumulator value is to report data, for
example for debugging purposes, back to the driver. For example, if you wanted to count values that match a specific condition in a UDF for debugging purposes, an accumulator provides a good
way to do that.
Only numeric values can be used in accumulators.
No. While pySpark's Accumulator only supports numeric values (think int and float), you can define accumulators for custom types via the AccumulatorParam interface (documentation linked below).
Accumulators do not obey lazy evaluation.
Incorrect – accumulators do obey lazy evaluation. This has implications in practice: When an accumulator is encapsulated in a transformation, that accumulator will not be modified until a
subsequent action is run.
Accumulators are difficult to use for debugging because they will only be updated once, independent if a task has to be re-run due to hardware failure.
Wrong. A concern with accumulators is in fact that under certain conditions they can run for each task more than once. For example, if a hardware failure occurs during a task after an accumulator
variable has been increased but before a task has finished and Spark launches the task on a different worker in response to the failure, already executed accumulator variable increases will be
Only unnamed accumulators can be inspected in the Spark UI.
No. Currently, in PySpark, no accumulators can be inspected in the Spark UI. In the Scala interface of Spark, only named accumulators can be inspected in the Spark UI.
More info: Aggregating Results with Spark Accumulators | Sparkour, RDD Programming Guide - Spark 3.1.2 Documentation, pyspark.Accumulator — PySpark 3.1.2 documentation, and
pyspark.AccumulatorParam — PySpark 3.1.2 documentation
Which of the following code blocks selects all rows from DataFrame transactionsDf in which column productId is zero or smaller or equal to 3?
This QUESTION NO: targets your knowledge about how to chain filtering conditions. Each filtering condition should be in parentheses. The correct operator for "or" is the pipe character (|) and not
the word or. Another operator of concern is the equality operator. For the purpose of comparison, equality is expressed as two equal signs (==).
Static notebook | Dynamic notebook: See test 2, QUESTION NO: 21 (Databricks import instructions)
Which of the following code blocks reads the parquet file stored at filePath into DataFrame itemsDf, using a valid schema for the sample of itemsDf shown below?
Sample of itemsDf:
2.|itemId|attributes |supplier |
4.|1 |[blue, winter, cozy] |Sports Company Inc.|
5.|2 |[red, summer, fresh, cooling]|YetiX |
6.|3 |[green, summer, travel] |Sports Company Inc.|
The challenge in this QUESTION NO: comes from there being an array variable in the schema. In addition, you should know how to pass a schema to the DataFrameReader that is invoked by
The correct way to define an array of strings in a schema is through ArrayType(StringType()). A schema can be passed to the DataFrameReader by simply appending schema(structType) to the
read() operator. Alternatively, you can also define a schema as a string. For example, for the schema of itemsDf, the following string would make sense: itemId integer, attributes array
A thing to keep in mind is that in schema definitions, you always need to instantiate the types, like so: StringType(). Just using StringType does not work in pySpark and will fail.
Another concern with schemas is whether columns should be nullable, so allowed to have null values. In the case at hand, this is not a concern however, since the QUESTION NO: just asks for a
schema. Both non-nullable and nullable column schemas would be valid here, since no null value appears in the DataFrame sample.
More info: Learning Spark, 2nd Edition, Chapter 3
Static notebook | Dynamic notebook: See test 3, QUESTION NO: 19 (Databricks import instructions)
The code block shown below should return a copy of DataFrame transactionsDf without columns value and productId and with an additional column associateId that has the value 5. Choose the
answer that correctly fills the blanks in the code block to accomplish this.
transactionsDf.__1__(__2__, __3__).__4__(__5__, 'value')
Correct code block:
transactionsDf.withColumn('associateId', lit(5)).drop('productId', 'value')
For solving this QUESTION NO: it is important that you know the lit() function (link to documentation below). This function enables you to add a column of a constant value to a DataFrame.
More info: pyspark.sql.functions.lit — PySpark 3.1.1 documentation
Static notebook | Dynamic notebook: See test 1, QUESTION NO: 57 (Databricks import instructions)