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Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 - Databricks Certified Associate Developer for Apache Spark 3.0 Exam

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Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Questions and Answers

Question # 1

The code block displayed below contains an error. The code block should arrange the rows of DataFrame transactionsDf using information from two columns in an ordered fashion, arranging first by

column value, showing smaller numbers at the top and greater numbers at the bottom, and then by column predError, for which all values should be arranged in the inverse way of the order of items

in column value. Find the error.

Code block:

transactionsDf.orderBy('value', asc_nulls_first(col('predError')))

A.

Two orderBy statements with calls to the individual columns should be chained, instead of having both columns in one orderBy statement.

B.

Column value should be wrapped by the col() operator.

C.

Column predError should be sorted in a descending way, putting nulls last.

D.

Column predError should be sorted by desc_nulls_first() instead.

E.

Instead of orderBy, sort should be used.

Question # 2

Which of the following statements about Spark's configuration properties is incorrect?

A.

The maximum number of tasks that an executor can process at the same time is controlled by the spark.task.cpus property.

B.

The maximum number of tasks that an executor can process at the same time is controlled by the spark.executor.cores property.

C.

The default value for spark.sql.autoBroadcastJoinThreshold is 10MB.

D.

The default number of partitions to use when shuffling data for joins or aggregations is 300.

E.

The default number of partitions returned from certain transformations can be controlled by the spark.default.parallelism property.

Question # 3

Which of the following describes the characteristics of accumulators?

A.

Accumulators are used to pass around lookup tables across the cluster.

B.

All accumulators used in a Spark application are listed in the Spark UI.

C.

Accumulators can be instantiated directly via the accumulator(n) method of the pyspark.RDD module.

D.

Accumulators are immutable.

E.

If an action including an accumulator fails during execution and Spark manages to restart the action and complete it successfully, only the successful attempt will be counted in the accumulator.

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