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Databricks Certified Associate Developer for Apache Spark 3.0 Exam Sample Questions (Q78-Q83):

NEW QUESTION # 78
Which of the following code blocks sorts DataFrame transactionsDf both by column storeId in ascending and by column productId in descending order, in this priority?

• A. transactionsDf.sort("storeId").sort(desc("productId"))
• B. transactionsDf.order_by(col(storeId), desc(col(productId)))
• C. transactionsDf.sort(col(storeId)).desc(col(productId))
• D. transactionsDf.sort("storeId", asc("productId"))
• E. transactionsDf.sort("storeId", desc("productId"))

Answer: E Explanation:
Explanation
In this question it is important to realize that you are asked to sort transactionDf by two columns. This means that the sorting of the second column depends on the sorting of the first column.
So, any option that sorts the entire DataFrame (through chaining sort statements) will not work. The two columns need to be channeled through the same call to sort().
Also, order_by is not a valid DataFrame API method.
More info: pyspark.sql.DataFrame.sort - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 2
NEW QUESTION # 79
The code block displayed below contains an error. The code block should return the average of rows in column value grouped by unique storeId. Find the error.
Code block:
transactionsDf.agg("storeId").avg("value")

• A. The avg("value") should be specified as a second argument to agg() instead of being appended to it.
• B. All column names should be wrapped in col() operators.
• C. Instead of avg("value"), avg(col("value")) should be used.
• D. agg should be replaced by groupBy.
• E. "storeId" and "value" should be swapped.

Answer: D Explanation:
Explanation
Static notebook | Dynamic notebook: See test 1
(https://flrs.github.io/sparkpracticetestscode/#1/30.html ,
https://bit.ly/sparkpracticeexamsimport_instructions)
NEW QUESTION # 80
The code block displayed below contains an error. The code block is intended to return all columns of DataFrame transactionsDf except for columns predError, productId, and value. Find the error.
Excerpt of DataFrame transactionsDf:
transactionsDf.select(~col("predError"), ~col("productId"), ~col("value"))

• A. The select operator should be replaced by the drop operator.
• B. The select operator should be replaced by the drop operator and the arguments to the drop operator should be column names predError, productId and value wrapped in the col operator so they should be expressed like drop(col(predError), col(productId), col(value)).
• C. The column names in the select operator should not be strings and wrapped in the col operator, so they should be expressed like select(~col(predError), ~col(productId), ~col(value)).
• D. The select operator should be replaced by the drop operator and the arguments to the drop operator should be column names predError, productId and value as strings.
  (Correct)
• E. The select operator should be replaced with the deselect operator.

Answer: D Explanation:
Explanation
Correct code block:
transactionsDf.drop("predError", "productId", "value")
Static notebook | Dynamic notebook: See test 1
NEW QUESTION # 81
Which of the following code blocks creates a new DataFrame with 3 columns, productId, highest, and lowest, that shows the biggest and smallest values of column value per value in column productId from DataFrame transactionsDf?
Sample of DataFrame transactionsDf:
1.+-------------+---------+-----+-------+---------+----+
2.|transactionId|predError|value|storeId|productId| f|
3.+-------------+---------+-----+-------+---------+----+
4.| 1| 3| 4| 25| 1|null|
5.| 2| 6| 7| 2| 2|null|
6.| 3| 3| null| 25| 3|null|
7.| 4| null| null| 3| 2|null|
8.| 5| null| null| null| 2|null|
9.| 6| 3| 2| 25| 2|null|
10.+-------------+---------+-----+-------+---------+----+

• A. transactionsDf.groupby('productId').agg(max('value').alias('highest'), min('value').alias('lowest'))
• B. transactionsDf.groupby("productId").agg({"highest": max("value"), "lowest": min("value")})
• C. transactionsDf.max('value').min('value')
• D. transactionsDf.agg(max('value').alias('highest'), min('value').alias('lowest'))
• E. transactionsDf.groupby(col(productId)).agg(max(col(value)).alias("highest"), min(col(value)).alias("lowest"))

Answer: A Explanation:
Explanation
transactionsDf.groupby('productId').agg(max('value').alias('highest'), min('value').alias('lowest')) Correct. groupby and aggregate is a common pattern to investigate aggregated values of groups.
transactionsDf.groupby("productId").agg({"highest": max("value"), "lowest": min("value")}) Wrong. While DataFrame.agg() accepts dictionaries, the syntax of the dictionary in this code block is wrong.
If you use a dictionary, the syntax should be like {"value": "max"}, so using the column name as the key and the aggregating function as value.
transactionsDf.agg(max('value').alias('highest'), min('value').alias('lowest')) Incorrect. While this is valid Spark syntax, it does not achieve what the question asks for. The question specifically asks for values to be aggregated per value in column productId - this column is not considered here. Instead, the max() and min() values are calculated as if the entire DataFrame was a group.
transactionsDf.max('value').min('value')
Wrong. There is no DataFrame.max() method in Spark, so this command will fail.
transactionsDf.groupby(col(productId)).agg(max(col(value)).alias("highest"), min(col(value)).alias("lowest")) No. While this may work if the column names are expressed as strings, this will not work as is. Python will interpret the column names as variables and, as a result, pySpark will not understand which columns you want to aggregate.
More info: pyspark.sql.DataFrame.agg - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 3
NEW QUESTION # 82
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?

• A. 1, 8
• B. 7, 9, 10
• C. 0
• D. 1, 10
• E. 1, 4, 6, 9

Answer: A 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 "K38INU" 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 # 83
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