Databricks Certified Associate Developer for Apache Spark 3.0 Exam
Last Update Apr 28, 2024
Total Questions : 180
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The code block displayed below contains an error. The code block should read the csv file located at path data/transactions.csv into DataFrame transactionsDf, using the first row as column header
and casting the columns in the most appropriate type. Find the error.
First 3 rows of transactions.csv:
1.transactionId;storeId;productId;name
2.1;23;12;green grass
3.2;35;31;yellow sun
4.3;23;12;green grass
Code block:
transactionsDf = spark.read.load("data/transactions.csv", sep=";", format="csv", header=True)
The code block shown below should return a two-column DataFrame with columns transactionId and supplier, with combined information from DataFrames itemsDf and transactionsDf. The code
block should merge rows in which column productId of DataFrame transactionsDf matches the value of column itemId in DataFrame itemsDf, but only where column storeId of DataFrame
transactionsDf does not match column itemId of DataFrame itemsDf. Choose the answer that correctly fills the blanks in the code block to accomplish this.
Code block:
transactionsDf.__1__(itemsDf, __2__).__3__(__4__)
Which of the following code blocks concatenates rows of DataFrames transactionsDf and transactionsNewDf, omitting any duplicates?
Which of the following code blocks reads in the JSON file stored at filePath, enforcing the schema expressed in JSON format in variable json_schema, shown in the code block below?
Code block:
1.json_schema = """
2.{"type": "struct",
3. "fields": [
4. {
5. "name": "itemId",
6. "type": "integer",
7. "nullable": true,
8. "metadata": {}
9. },
10. {
11. "name": "supplier",
12. "type": "string",
13. "nullable": true,
14. "metadata": {}
15. }
16. ]
17.}
18."""
The code block shown below should add a column itemNameBetweenSeparators to DataFrame itemsDf. The column should contain arrays of maximum 4 strings. The arrays should be composed of
the values in column itemsDf which are separated at - or whitespace characters. Choose the answer that correctly fills the blanks in the code block to accomplish this.
Sample of DataFrame itemsDf:
1.+------+----------------------------------+-------------------+
2.|itemId|itemName |supplier |
3.+------+----------------------------------+-------------------+
4.|1 |Thick Coat for Walking in the Snow|Sports Company Inc.|
5.|2 |Elegant Outdoors Summer Dress |YetiX |
6.|3 |Outdoors Backpack |Sports Company Inc.|
7.+------+----------------------------------+-------------------+
Code block:
itemsDf.__1__(__2__, __3__(__4__, "[\s\-]", __5__))
The code block shown below should return a DataFrame with columns transactionsId, predError, value, and f from DataFrame transactionsDf. Choose the answer that correctly fills the blanks in the
code block to accomplish this.
transactionsDf.__1__(__2__)
Which of the following describes a difference between Spark's cluster and client execution modes?
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")
The code block displayed below contains an error. The code block should save DataFrame transactionsDf at path path as a parquet file, appending to any existing parquet file. Find the error.
Code block:
Which of the following is the idea behind dynamic partition pruning in Spark?
The code block displayed below contains multiple errors. The code block should return a DataFrame that contains only columns transactionId, predError, value and storeId of DataFrame
transactionsDf. Find the errors.
Code block:
transactionsDf.select([col(productId), col(f)])
Sample of 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.+-------------+---------+-----+-------+---------+----+
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")
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.+------+-----------------------------+-------------------+
The code block displayed below contains an error. The code block should produce a DataFrame with color as the only column and three rows with color values of red, blue, and green, respectively.
Find the error.
Code block:
1.spark.createDataFrame([("red",), ("blue",), ("green",)], "color")
Instead of calling spark.createDataFrame, just DataFrame should be called.
The code block displayed below contains multiple errors. The code block should remove column transactionDate from DataFrame transactionsDf and add a column transactionTimestamp in which
dates that are expressed as strings in column transactionDate of DataFrame transactionsDf are converted into unix timestamps. Find the errors.
Sample of DataFrame transactionsDf:
1.+-------------+---------+-----+-------+---------+----+----------------+
2.|transactionId|predError|value|storeId|productId| f| transactionDate|
3.+-------------+---------+-----+-------+---------+----+----------------+
4.| 1| 3| 4| 25| 1|null|2020-04-26 15:35|
5.| 2| 6| 7| 2| 2|null|2020-04-13 22:01|
6.| 3| 3| null| 25| 3|null|2020-04-02 10:53|
7.+-------------+---------+-----+-------+---------+----+----------------+
Code block:
1.transactionsDf = transactionsDf.drop("transactionDate")
2.transactionsDf["transactionTimestamp"] = unix_timestamp("transactionDate", "yyyy-MM-dd")
The code block displayed below contains at least one error. The code block should return a DataFrame with only one column, result. That column should include all values in column value from
DataFrame transactionsDf raised to the power of 5, and a null value for rows in which there is no value in column value. Find the error(s).
Code block:
1.from pyspark.sql.functions import udf
2.from pyspark.sql import types as T
3.
4.transactionsDf.createOrReplaceTempView('transactions')
5.
6.def pow_5(x):
7. return x**5
8.
9.spark.udf.register(pow_5, 'power_5_udf', T.LongType())
10.spark.sql('SELECT power_5_udf(value) FROM transactions')
The code block shown below should convert up to 5 rows in DataFrame transactionsDf that have the value 25 in column storeId into a Python list. Choose the answer that correctly fills the blanks in
the code block to accomplish this.
Code block:
transactionsDf.__1__(__2__).__3__(__4__)
The code block displayed below contains an error. The code block should configure Spark so that DataFrames up to a size of 20 MB will be broadcast to all worker nodes when performing a join.
Find the error.
Code block:
Which of the following code blocks returns a copy of DataFrame transactionsDf that only includes columns transactionId, storeId, productId and f?
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.+-------------+---------+-----+-------+---------+----+
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__
Which of the following code blocks returns a new DataFrame in which column attributes of DataFrame itemsDf is renamed to feature0 and column supplier to feature1?
Which of the following code blocks returns the number of unique values in column storeId of DataFrame transactionsDf?
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to
transactionNumber. Find the error.
Code block:
transactionsDf.withColumn("transactionNumber", "transactionId")
TESTED 29 Apr 2024
