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

Explanation

The executed physical plan depends on a cost optimization from a previous stage.

Correct! Spark considers multiple physical plans on which it performs a cost analysis and selects the final physical plan in accordance with the lowest-cost outcome of that analysis. That final

physical plan is then executed by Spark.

Spark uses the catalog to resolve the optimized logical plan.

No. Spark uses the catalog to resolve the unresolved logical plan, but not the optimized logical plan. Once the unresolved logical plan is resolved, it is then optimized using the Catalyst Optimizer.

The optimized logical plan is the input for physical planning.

The catalog assigns specific resources to the physical plan.

No. The catalog stores metadata, such as a list of names of columns, data types, functions, and databases. Spark consults the catalog for resolving the references in a logical plan at the beginning

of the conversion of the query into an execution plan. The result is then an optimized logical plan.

Depending on whether DataFrame API or SQL API are used, the physical plan may differ.

Wrong – the physical plan is independent of which API was used. And this is one of the great strengths of Spark!

The catalog assigns specific resources to the optimized memory plan.

There is no specific "memory plan" on the journey of a Spark computation.

More info: Spark’s Logical and Physical plans … When, Why, How and Beyond. | by Laurent Leturgez | datalex | Medium

Explanation

Correct code block:

transactionsDf.select((col("storeId").between(20, 30)) & (col("productId")==2))

Although this QUESTION NO: may make you think that it asks for a filter or where statement, it does not. It asks explicity to return a column with booleans – this should point you to the select

statement.

Another trick here is the rarely used between() method. It exists and resolves to ((storeId >= 20) AND (storeId <= 30)) in SQL. geq() and leq() do not exist.

Another riddle here is how to chain the two conditions. The only valid answer here is &. Operators like && or and are not valid. Other boolean operators that would be valid in Spark are | and.

Static notebook | Dynamic notebook: See test 1, QUESTION NO: 42 (Databricks import instructions)

Explanation

The key here is to understand that columns that are passed to DataFrame.drop() are ignored if they do not exist in the DataFrame. So, passing column name associateId to transactionsDf.drop()

does not have any effect.

Passing a list to transactionsDf.drop() is not valid. The documentation (link below) shows the call structure as DataFrame.drop(*cols). The * means that all arguments that are passed to

DataFrame.drop() are read as columns. However, since a list of columns, for example ["predError", "productId", "associateId"] is not a column, Spark will run into an error.

More info: pyspark.sql.DataFrame.drop — PySpark 3.1.1 documentation

Static notebook | Dynamic notebook: See test 1, QUESTION NO: 50 (Databricks import instructions)