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Download Databricks Certified Associate Developer for Apache Spark 3.0 Exam Exam Dumps
NEW QUESTION 48
The code block displayed below contains an error. The code block should create DataFrame itemsAttributesDf which has columns itemId and attribute and lists every attribute from the attributes column in DataFrame itemsDf next to the itemId of the respective row in itemsDf. Find the error.
A sample of DataFrame itemsDf is below.
Code block:
itemsAttributesDf = itemsDf.explode("attributes").alias("attribute").select("attribute", "itemId")
Answer: E
Explanation:
The correct code block looks like this:
Then, the first couple of rows of itemAttributesDf look like this:
explode() is not a method of DataFrame. explode() should be used inside the select() method instead.
This is correct.
The split() method should be used inside the select() method instead of the explode() method.
No, the split() method is used to split strings into parts. However, column attributs is an array of strings. In this case, the explode() method is appropriate.
Since itemId is the index, it does not need to be an argument to the select() method.
No, itemId still needs to be selected, whether it is used as an index or not.
The explode() method expects a Column object rather than a string.
No, a string works just fine here. This being said, there are some valid alternatives to passing in a string:
The alias() method needs to be called after the select() method.
No.
More info: pyspark.sql.functions.explode - PySpark 3.1.1 documentation (https://bit.ly/2QUZI1J) Static notebook | Dynamic notebook: See test 1 (https://flrs.github.io/spark_practice_tests_code/#1/22.html ,
https://bit.ly/sparkpracticeexams_import_instructions)
NEW QUESTION 49
Which of the following statements about Spark's execution hierarchy is correct?
Answer: E
Explanation:
Explanation
In Spark's execution hierarchy, a job may reach over multiple stage boundaries.
Correct. A job is a sequence of stages, and thus may reach over multiple stage boundaries.
In Spark's execution hierarchy, tasks are one layer above slots.
Incorrect. Slots are not a part of the execution hierarchy. Tasks are the lowest layer.
In Spark's execution hierarchy, a stage comprises multiple jobs.
No. It is the other way around - a job consists of one or multiple stages.
In Spark's execution hierarchy, executors are the smallest unit.
False. Executors are not a part of the execution hierarchy. Tasks are the smallest unit!
In Spark's execution hierarchy, manifests are one layer above jobs.
Wrong. Manifests are not a part of the Spark ecosystem.
NEW QUESTION 50
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
Answer: E
Explanation:
Explanation
1: Correct - This should just read "API" or "DataFrame API". The DataFrame is not part of the SQL API. To make a DataFrame accessible via SQL, you first need to create a DataFrame view. That view can then be accessed via SQL.
4: Although "K_38_INU" looks odd, it is a completely valid name for a DataFrame column.
6: No, StringType is a correct type.
7: Although a StringType may not be the most efficient way to store a phone number, there is nothing fundamentally wrong with using this type here.
8: Correct - TreeType is not a type that Spark supports.
9: No, Spark DataFrames support ArrayType variables. In this case, the variable would represent a sequence of elements with type LongType, which is also a valid type for Spark DataFrames.
10: There is nothing wrong with this row.
More info: Data Types - Spark 3.1.1 Documentation (https://bit.ly/3aAPKJT)
NEW QUESTION 51
In which order should the code blocks shown below be run in order to assign articlesDf a DataFrame that lists all items in column attributes ordered by the number of times these items occur, from most to least often?
Sample of DataFrame articlesDf:
1.+------+-----------------------------+-------------------+
2.|itemId|attributes |supplier |
3.+------+-----------------------------+-------------------+
4.|1 |[blue, winter, cozy] |Sports Company Inc.|
5.|2 |[red, summer, fresh, cooling]|YetiX |
6.|3 |[green, summer, travel] |Sports Company Inc.|
7.+------+-----------------------------+-------------------+
2. articlesDf = articlesDf.select(explode(col("attributes")))
3. articlesDf = articlesDf.orderBy("count").select("col")
4. articlesDf = articlesDf.sort("count",ascending=False).select("col")
5. articlesDf = articlesDf.groupby("col").count()B. 2, 5, 3C. 2, 3, 4D. 4, 5E. 5, 2F. 2, 5, 4
Answer: C
Explanation:
Explanation
Correct code block:
articlesDf = articlesDf.select(explode(col('attributes')))
articlesDf = articlesDf.groupby('col').count()
articlesDf = articlesDf.sort('count',ascending=False).select('col')
Output of correct code block:
+-------+
| col|
+-------+
| summer|
| winter|
| blue|
| cozy|
| travel|
| fresh|
| red|
|cooling|
| green|
+-------+
Static notebook | Dynamic notebook: See test 2
NEW QUESTION 52
Which of the following code blocks performs an inner join of DataFrames transactionsDf and itemsDf on columns productId and itemId, respectively, excluding columns value and storeId from DataFrame transactionsDf and column attributes from DataFrame itemsDf?
2.itemsDf.createOrReplaceTempView('itemsDf')
3.
4.spark.sql("SELECT -value, -storeId FROM transactionsDf INNER JOIN itemsDf ON productId==itemId").drop("attributes")B. 1.transactionsDf.createOrReplaceTempView('transactionsDf')
2.itemsDf.createOrReplaceTempView('itemsDf')
3.
4.statement = """
5.SELECT * FROM transactionsDf
6.INNER JOIN itemsDf
7.ON transactionsDf.productId==itemsDf.itemId
8."""
9.spark.sql(statement).drop("value", "storeId", "attributes")C. transactionsDf.drop('value', 'storeId').join(itemsDf.select('attributes'), transactionsDf.productId==itemsDf.itemId)D. transactionsDf.drop("value", "storeId").join(itemsDf.drop("attributes"),
"transactionsDf.productId==itemsDf.itemId")E. 1.transactionsDf \
2. .drop(col('value'), col('storeId')) \
3. .join(itemsDf.drop(col('attributes')), col('productId')==col('itemId'))
Answer: B
Explanation:
Explanation
This question offers you a wide variety of answers for a seemingly simple question. However, this variety reflects the variety of ways that one can express a join in PySpark. You need to understand some SQL syntax to get to the correct answer here.
transactionsDf.createOrReplaceTempView('transactionsDf')
itemsDf.createOrReplaceTempView('itemsDf')
statement = """
SELECT * FROM transactionsDf
INNER JOIN itemsDf
ON transactionsDf.productId==itemsDf.itemId
"""
spark.sql(statement).drop("value", "storeId", "attributes")
Correct - this answer uses SQL correctly to perform the inner join and afterwards drops the unwanted columns. This is totally fine. If you are unfamiliar with the triple-quote """ in Python: This allows you to express strings as multiple lines.
transactionsDf \
drop(col('value'), col('storeId')) \
join(itemsDf.drop(col('attributes')), col('productId')==col('itemId'))
No, this answer option is a trap, since DataFrame.drop() does not accept a list of Column objects. You could use transactionsDf.drop('value', 'storeId') instead.
transactionsDf.drop("value", "storeId").join(itemsDf.drop("attributes"),
"transactionsDf.productId==itemsDf.itemId")
Incorrect - Spark does not evaluate "transactionsDf.productId==itemsDf.itemId" as a valid join expression.
This would work if it would not be a string.
transactionsDf.drop('value', 'storeId').join(itemsDf.select('attributes'), transactionsDf.productId==itemsDf.itemId) Wrong, this statement incorrectly uses itemsDf.select instead of itemsDf.drop.
transactionsDf.createOrReplaceTempView('transactionsDf')
itemsDf.createOrReplaceTempView('itemsDf')
spark.sql("SELECT -value, -storeId FROM transactionsDf INNER JOIN itemsDf ON productId==itemId").drop("attributes") No, here the SQL expression syntax is incorrect. Simply specifying -columnName does not drop a column.
More info: pyspark.sql.DataFrame.join - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 3
NEW QUESTION 53
......
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