Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Practice Exam Questions and Answers

Which of the following code blocks returns about 150 randomly selected rows from the 1000-row DataFrame transactionsDf, assuming that any row can appear more than once in the returned

DataFrame?

The code block displayed below contains one or more errors. The code block should load parquet files at location filePath into a DataFrame, only loading those files that have been modified before

2029-03-20 05:44:46. Spark should enforce a schema according to the schema shown below. Find the error.

Schema:

1.root

2. |-- itemId: integer (nullable = true)

3. |-- attributes: array (nullable = true)

4. | |-- element: string (containsNull = true)

5. |-- supplier: string (nullable = true)

Code block:

1.schema = StructType([

2. StructType("itemId", IntegerType(), True),

3. StructType("attributes", ArrayType(StringType(), True), True),

4. StructType("supplier", StringType(), True)

5.])

6.

7.spark.read.options("modifiedBefore", "2029-03-20T05:44:46").schema(schema).load(filePath)

Which of the following code blocks reads in the JSON file stored at filePath as a DataFrame?

Which of the following code blocks returns a new DataFrame with the same columns as DataFrame transactionsDf, except for columns predError and value which should be removed?

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?

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 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 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__))

Which of the following describes Spark's way of managing memory?

Which of the following statements about Spark's configuration properties is incorrect?

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.+------+-----------------------------+-------------------+

Which of the following is the deepest level in Spark's execution hierarchy?

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.+-------------+---------+-----+-------+---------+----+

The code block displayed below contains an error. The code block should combine data from DataFrames itemsDf and transactionsDf, showing all rows of DataFrame itemsDf that have a matching

value in column itemId with a value in column transactionsId of DataFrame transactionsDf. Find the error.

Code block:

itemsDf.join(itemsDf.itemId==transactionsDf.transactionId)

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 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")

Which of the following code blocks reads in the two-partition parquet file stored at filePath, making sure all columns are included exactly once even though each partition has a different schema?

Schema of first partition:

1.root

2. |-- transactionId: integer (nullable = true)

3. |-- predError: integer (nullable = true)

4. |-- value: integer (nullable = true)

5. |-- storeId: integer (nullable = true)

6. |-- productId: integer (nullable = true)

7. |-- f: integer (nullable = true)

Schema of second partition:

1.root

2. |-- transactionId: integer (nullable = true)

3. |-- predError: integer (nullable = true)

4. |-- value: integer (nullable = true)

5. |-- storeId: integer (nullable = true)

6. |-- rollId: integer (nullable = true)

7. |-- f: integer (nullable = true)

8. |-- tax_id: integer (nullable = false)

The code block displayed below contains an error. The code block is intended to perform an outer join of DataFrames transactionsDf and itemsDf on columns productId and itemId, respectively.

Find the error.

Code block:

transactionsDf.join(itemsDf, [itemsDf.itemId, transactionsDf.productId], "outer")

The code block shown below should return an exact copy of DataFrame transactionsDf that does not include rows in which values in column storeId have the value 25. Choose the answer that

correctly fills the blanks in the code block to accomplish this.

The code block shown below should add column transactionDateForm to DataFrame transactionsDf. The column should express the unix-format timestamps in column transactionDate as string

type like Apr 26 (Sunday). Choose the answer that correctly fills the blanks in the code block to accomplish this.

transactionsDf.__1__(__2__, from_unixtime(__3__, __4__))

The code block shown below should return a single-column DataFrame with a column named consonant_ct that, for each row, shows the number of consonants in column itemName of DataFrame

itemsDf. Choose the answer that correctly fills the blanks in the code block to accomplish this.

DataFrame itemsDf:

1.+------+----------------------------------+-----------------------------+-------------------+

2.|itemId|itemName |attributes |supplier |

3.+------+----------------------------------+-----------------------------+-------------------+

4.|1 |Thick Coat for Walking in the Snow|[blue, winter, cozy] |Sports Company Inc.|

5.|2 |Elegant Outdoors Summer Dress |[red, summer, fresh, cooling]|YetiX |

6.|3 |Outdoors Backpack |[green, summer, travel] |Sports Company Inc.|

7.+------+----------------------------------+-----------------------------+-------------------+

Code block:

itemsDf.select(__1__(__2__(__3__(__4__), "a|e|i|o|u|\s", "")).__5__("consonant_ct"))

In which order should the code blocks shown below be run in order to create a DataFrame that shows the mean of column predError of DataFrame transactionsDf per column storeId and productId,

where productId should be either 2 or 3 and the returned DataFrame should be sorted in ascending order by column storeId, leaving out any nulls in that column?

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.+-------------+---------+-----+-------+---------+----+

1. .mean("predError")

2. .groupBy("storeId")

3. .orderBy("storeId")

4. transactionsDf.filter(transactionsDf.storeId.isNotNull())

5. .pivot("productId", [2, 3])

Which of the following code blocks reorders the values inside the arrays in column attributes of DataFrame itemsDf from last to first one in the alphabet?

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.+------+-----------------------------+-------------------+

Which of the following statements about broadcast variables is correct?

Which of the following options describes the responsibility of the executors in Spark?

Which of the following code blocks displays various aggregated statistics of all columns in DataFrame transactionsDf, including the standard deviation and minimum of values in each column?

In which order should the code blocks shown below be run in order to return the number of records that are not empty in column value in the DataFrame resulting from an inner join of DataFrame

transactionsDf and itemsDf on columns productId and itemId, respectively?

1. .filter(~isnull(col('value')))

2. .count()

3. transactionsDf.join(itemsDf, col("transactionsDf.productId")==col("itemsDf.itemId"))

4. transactionsDf.join(itemsDf, transactionsDf.productId==itemsDf.itemId, how='inner')

5. .filter(col('value').isnotnull())

6. .sum(col('value'))