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Download DemoVendor: Databricks
Certifications: Databricks Certification
Exam Code: DATABRICKS-CERTIFIED-ASSOCIATE-DEVELOPER-FOR-APACHE-SPARK
Exam Name: Databricks Certified Associate Developer for Apache Spark 3.0
Updated: Mar 20, 2024
Q&As: 180
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VCE
Which of the following statements about garbage collection in Spark is incorrect?
A. Garbage collection information can be accessed in the Spark UI's stage detail view.
B. Optimizing garbage collection performance in Spark may limit caching ability.
C. Manually persisting RDDs in Spark prevents them from being garbage collected.
D. In Spark, using the G1 garbage collector is an alternative to using the default Parallel garbage collector.
E. Serialized caching is a strategy to increase the performance of garbage collection.
Correct Answer: C
The code block displayed below contains an error. The code block should return a DataFrame in which column predErrorAdded contains the results of Python function add_2_if_geq_3 as applied to numeric and nullable column predError in DataFrame transactionsDf.
Find the error.
Code block:
1.def add_2_if_geq_3(x):
2.
if x is None:
3.
return x
4.
elif x >= 3:
5.
return x+2
6.
return x
7.
8.add_2_if_geq_3_udf = udf(add_2_if_geq_3)
9.
10.transactionsDf.withColumnRenamed("predErrorAdded", add_2_if_geq_3_udf(col("predError")))
A. The operator used to adding the column does not add column predErrorAdded to the DataFrame.
B. Instead of col("predError"), the actual DataFrame with the column needs to be passed, like so transactionsDf.predError.
C. The udf() method does not declare a return type.
D. UDFs are only available through the SQL API, but not in the Python API as shown in the code block.
E. The Python function is unable to handle null values, resulting in the code block crashing on execution.
Correct Answer: A
Correct code block:
def add_2_if_geq_3(x):
if x is None:
return x
elif x >= 3:
return x+2
return x
add_2_if_geq_3_udf = udf(add_2_if_geq_3)
transactionsDf.withColumn("predErrorAdded",
add_2_if_geq_3_udf(col("predError"))).show()
Instead of withColumnRenamed, you should use the withColumn operator.
The udf() method does not declare a return type.
It is fine that the udf() method does not declare a return type, this is not a required argument. However, the
default return type is StringType. This may not be the ideal return type for numeric,
nullable data ?but the code will run without specified return type nevertheless. The Python function is
unable to handle null values, resulting in the code block crashing on execution.
The Python function is able to handle null values, this is what the statement if x is None does.
UDFs are only available through the SQL API, but not in the Python API as shown in the code block.
No, they are available through the Python API. The code in the code block that concerns UDFs is correct.
Instead of col("predError"), the actual DataFrame with the column needs to be passed, like so
transactionsDf.predError.
You may choose to use the transactionsDf.predError syntax, but the col("predError") syntax is fine.
The code block displayed below contains an error. The code block should return a DataFrame where all entries in column supplier contain the letter combination et in this order. Find the error.
Code block:
itemsDf.filter(Column('supplier').isin('et'))
A. The Column operator should be replaced by the col operator and instead of isin, contains should be used.
B. The expression inside the filter parenthesis is malformed and should be replaced by isin('et', 'supplier').
C. Instead of isin, it should be checked whether column supplier contains the letters et, so isin should be replaced with contains. In addition, the column should be accessed using col['supplier'].
D. The expression only returns a single column and filter should be replaced by select.
Correct Answer: B
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, 51 (Databricks import instructions)
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