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

Explanation

DAG stands for "Directing Acyclic Graph".

No, DAG stands for "Directed Acyclic Graph".

Spark strategically hides DAGs from developers, since the high degree of automation in Spark means that developers never need to consider DAG layouts.

No, quite the opposite. You can access DAGs through the Spark UI and they can be of great help when optimizing queries manually.

In contrast to transformations, DAGs are never lazily executed.

DAGs represent the execution plan in Spark and as such are lazily executed when the driver requests the data processed in the DAG.

Explanation

Caching means storing a copy of a partition on an executor, so it can be accessed quicker by subsequent operations, instead of having to be recalculated. cache() is a lazily-evaluated method of the

DataFrame. Since count() is an action (while filter() is not), it triggers the caching process.

More info: pyspark.sql.DataFrame.cache — PySpark 3.1.2 documentation, Learning Spark, 2nd Edition, Chapter 7

Static notebook | Dynamic notebook: See test 2, QUESTION NO: 20 (Databricks import instructions)

Explanation

Correct code block:

itemsDf.filter(col('supplier').contains('et'))

A mixup can easily happen here between isin and contains. Since we want to check whether a column "contains" the values et, this is the operator we should use here. Note that both methods are

methods of Spark's Column object. See below for documentation links.

A specific Column object can be accessed through the col() method and not the Column() method or through col[], which is an essential thing to know here. In PySpark, Column references a generic

column object. To use it for queries, you need to link the generic column object to a specific DataFrame. This can be achieved, for example, through the col() method.

More info:

- isin documentation: pyspark.sql.Column.isin — PySpark 3.1.1 documentation

- contains documentation: pyspark.sql.Column.contains — PySpark 3.1.1 documentation

Static notebook | Dynamic notebook: See test 1, QUESTION NO: 51 (Databricks import instructions)