Databricks Certified Associate Developer for Apache Spark 3.0 - Associate-Developer-Apache-Spark Exam Practice Test

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Total 179 questions

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Question 1

The code block displayed below contains an error. The code block should merge the rows of DataFrames transactionsDfMonday and transactionsDfTuesday into a new DataFrame, matching column names and inserting null values where column names do not appear in both DataFrames. Find the error.
Sample of DataFrame transactionsDfMonday:
1.+-------------+---------+-----+-------+---------+----+
2.|transactionId|predError|value|storeId|productId| f|
3.+-------------+---------+-----+-------+---------+----+
4.| 5| null| null| null| 2|null|
5.| 6| 3| 2| 25| 2|null|
6.+-------------+---------+-----+-------+---------+----+
Sample of DataFrame transactionsDfTuesday:
1.+-------+-------------+---------+-----+
2.|storeId|transactionId|productId|value|
3.+-------+-------------+---------+-----+
4.| 25| 1| 1| 4|
5.| 2| 2| 2| 7|
6.| 3| 4| 2| null|
7.| null| 5| 2| null|
8.+-------+-------------+---------+-----+
Code block:
sc.union([transactionsDfMonday, transactionsDfTuesday])

A. Instead of the Spark context, transactionDfMonday should be called with the union method.

B. Instead of the Spark context, transactionDfMonday should be called with the join method instead of the union method, making sure to use its default arguments.

C. Instead of union, the concat method should be used, making sure to not use its default arguments.

D. Instead of the Spark context, transactionDfMonday should be called with the unionByName method instead of the union method, making sure to not use its default arguments.

E. The DataFrames' RDDs need to be passed into the sc.union method instead of the DataFrame variable names.

Discussion 0

Correct Answer: D Vote an answer

Explanation: Only visible for Fast2test members. You can sign-up / login (it's free).

Question 2

Which of the following code blocks returns a new DataFrame with only columns predError and values of every second row of DataFrame transactionsDf?
Entire 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.filter("transactionId" % 2 == 0).select("predError", "value")

B. 1.transactionsDf.createOrReplaceTempView("transactionsDf")
2.spark.sql("FROM transactionsDf SELECT predError, value WHERE transactionId % 2 = 2")

C. transactionsDf.select(col("transactionId").isin([3,4,6]), "predError", "value")

D. transactionsDf.filter(col(transactionId).isin([3,4,6]))

E. transactionsDf.filter(col("transactionId") % 2 == 0).select("predError", "value") (Correct)

F. transactionsDf.filter(col("transactionId").isin([3,4,6])).select([predError, value])

Discussion 0

Correct Answer: E Vote an answer

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Question 3

Which of the following code blocks returns a 2-column DataFrame that shows the distinct values in column productId and the number of rows with that productId in DataFrame transactionsDf?

A. transactionsDf.count("productId").distinct()

B. transactionsDf.groupBy("productId").agg(col("value").count())

C. transactionsDf.groupBy("productId").count()

D. transactionsDf.groupBy("productId").select(count("value"))

E. transactionsDf.count("productId")

Discussion 0

Correct Answer: C Vote an answer

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Question 4

The code block displayed below contains an error. The code block should return DataFrame transactionsDf, but with the column storeId renamed to storeNumber. Find the error.
Code block:
transactionsDf.withColumn("storeNumber", "storeId")

A. Argument "storeId" should be the first and argument "storeNumber" should be the second argument to the withColumn method.

B. Instead of withColumn, the withColumnRenamed method should be used.

C. Arguments "storeNumber" and "storeId" each need to be wrapped in a col() operator.

D. The withColumn operator should be replaced with the copyDataFrame operator.

E. Instead of withColumn, the withColumnRenamed method should be used and argument "storeId" should be the first and argument "storeNumber" should be the second argument to that method.

Discussion 0

Correct Answer: E Vote an answer

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Question 5

The code block shown below should show information about the data type that column storeId of DataFrame transactionsDf contains. Choose the answer that correctly fills the blanks in the code block to accomplish this.
Code block:
transactionsDf.__1__(__2__).__3__

A. 1. select
2. "storeId"
3. printSchema()

B. 1. limit
2. "storeId"
3. printSchema()

C. 1. limit
2. 1
3. columns

D. 1. select
2. "storeId"
3. print_schema()

E. 1. select
2. storeId
3. dtypes

Discussion 0

Correct Answer: C Vote an answer

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Question 6

Which of the following code blocks returns a single-row DataFrame that only has a column corr which shows the Pearson correlation coefficient between columns predError and value in DataFrame transactionsDf?

A. transactionsDf.select(corr(["predError", "value"]).alias("corr")).first()

B. transactionsDf.select(corr("predError", "value"))

C. transactionsDf.select(corr(col("predError"), col("value")).alias("corr")) (Correct)

D. transactionsDf.select(corr(col("predError"), col("value")).alias("corr")).first()

E. transactionsDf.select(corr(predError, value).alias("corr"))

Discussion 0

Correct Answer: C Vote an answer

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Question 7

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 and the arguments to the drop operator should be column names predError, productId and value as strings.
(Correct)

B. 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)).

C. The select operator should be replaced with the deselect operator.

D. The select operator should be replaced by the drop operator.

E. 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)).

Discussion 0

Correct Answer: A Vote an answer

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Question 8

Which of the following code blocks reads all CSV files in directory filePath into a single DataFrame, with column names defined in the CSV file headers?
Content of directory filePath:
1._SUCCESS
2._committed_2754546451699747124
3._started_2754546451699747124
4.part-00000-tid-2754546451699747124-10eb85bf-8d91-4dd0-b60b-2f3c02eeecaa-298-1-c000.csv.gz
5.part-00001-tid-2754546451699747124-10eb85bf-8d91-4dd0-b60b-2f3c02eeecaa-299-1-c000.csv.gz
6.part-00002-tid-2754546451699747124-10eb85bf-8d91-4dd0-b60b-2f3c02eeecaa-300-1-c000.csv.gz
7.part-00003-tid-2754546451699747124-10eb85bf-8d91-4dd0-b60b-2f3c02eeecaa-301-1-c000.csv.gz spark.option("header",True).csv(filePath)

A. spark.read.format("csv").option("header",True).load(filePath)

B. spark.read.format("csv").option("header",True).option("compression","zip").load(filePath)

C. spark.read.load(filePath)

D. spark.read().option("header",True).load(filePath)

Discussion 0

Correct Answer: A Vote an answer

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Question 9

The code block shown below should return a DataFrame with two columns, itemId and col. In this DataFrame, for each element in column attributes of DataFrame itemDf there should be a separate row in which the column itemId contains the associated itemId from DataFrame itemsDf. The new DataFrame should only contain rows for rows in DataFrame itemsDf in which the column attributes contains the element cozy.
A sample of DataFrame itemsDf is below.
Code block:
itemsDf.__1__(__2__).__3__(__4__, __5__(__6__))

A. 1. filter
2. "array_contains(attributes, 'cozy')"
3. select
4. "itemId"
5. map
6. "attributes"

B. 1. filter
2. "array_contains(attributes, cozy)"
3. select
4. "itemId"
5. explode
6. "attributes"

C. 1. where
2. "array_contains(attributes, 'cozy')"
3. select
4. itemId
5. explode
6. attributes

D. 1. filter
2. "array_contains(attributes, 'cozy')"
3. select
4. "itemId"
5. explode
6. "attributes"

E. 1. filter
2. array_contains("cozy")
3. select
4. "itemId"
5. explode
6. "attributes"

Discussion 0

Correct Answer: D Vote an answer

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Question 10

Which of the following code blocks applies the Python function to_limit on column predError in table transactionsDf, returning a DataFrame with columns transactionId and result?

A. 1.spark.udf.register("LIMIT_FCN", to_limit)
2.spark.sql("SELECT transactionId, to_limit(predError) AS result FROM transactionsDf") spark.sql("SELECT transactionId, udf(to_limit(predError)) AS result FROM transactionsDf")

B. 1.spark.udf.register(to_limit, "LIMIT_FCN")
2.spark.sql("SELECT transactionId, LIMIT_FCN(predError) AS result FROM transactionsDf")

C. 1.spark.udf.register("LIMIT_FCN", to_limit)
2.spark.sql("SELECT transactionId, LIMIT_FCN(predError) AS result FROM transactionsDf") (Correct)

D. 1.spark.udf.register("LIMIT_FCN", to_limit)
2.spark.sql("SELECT transactionId, LIMIT_FCN(predError) FROM transactionsDf AS result")

Discussion 0

Correct Answer: C Vote an answer

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Question 11

Which of the following statements about the differences between actions and transformations is correct?

A. Actions generate RDDs, while transformations do not.

B. Actions do not send results to the driver, while transformations do.

C. Actions can trigger Adaptive Query Execution, while transformation cannot.

D. Actions can be queued for delayed execution, while transformations can only be processed immediately.

E. Actions are evaluated lazily, while transformations are not evaluated lazily.

Discussion 0

Correct Answer: C Vote an answer

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Question 12

Which of the following code blocks returns a single-column DataFrame showing the number of words in column supplier of DataFrame itemsDf?
Sample of DataFrame itemsDf:
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.+------+-----------------------------+-------------------+

A. itemsDf.split("supplier", " ").count()

B. spark.select(size(split(col(supplier), " ")))

C. itemsDf.split("supplier", " ").size()

D. itemsDf.select(size(split("supplier", " ")))

E. itemsDf.select(word_count("supplier"))

Discussion 0

Correct Answer: D Vote an answer

Explanation: Only visible for Fast2test members. You can sign-up / login (it's free).

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Total 179 questions

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