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Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Exam With Confidence Using Practice Dumps

Exam Code:
Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0
Exam Name:
Databricks Certified Associate Developer for Apache Spark 3.0 Exam
Certification:
Vendor:
Questions:
180
Last Updated:
Apr 3, 2025
Exam Status:
Stable
Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0

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

Question 1

The code block shown below should write DataFrame transactionsDf as a parquet file to path storeDir, using brotli compression and replacing any previously existing file. Choose the answer that

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

transactionsDf.__1__.format("parquet").__2__(__3__).option(__4__, "brotli").__5__(storeDir)

Options:

A.

1. save

2. mode

3. "ignore"

4. "compression"

5. path

B.

1. store

2. with

3. "replacement"

4. "compression"

5. path

C.

1. write

2. mode

3. "overwrite"

4. "compression"

5. save

(Correct)

D.

1. save

2. mode

3. "replace"

4. "compression"

5. path

E.

1. write

2. mode

3. "overwrite"

4. compression

5. parquet

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Question 2

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

Options:

A.

The column names should be listed directly as arguments to the operator and not as a list.

B.

The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all column names should be expressed

as strings without being wrapped in a col() operator.

C.

The select operator should be replaced by a drop operator.

D.

The column names should be listed directly as arguments to the operator and not as a list and following the pattern of how column names are expressed in the code block, columns productId and

f should be replaced by transactionId, predError, value and storeId.

E.

The select operator should be replaced by a drop operator, the column names should be listed directly as arguments to the operator and not as a list, and all col() operators should be removed.

Question 3

The code block shown below should return a new 2-column DataFrame that shows one attribute from column attributes per row next to the associated itemName, for all suppliers in column supplier

whose name includes Sports. Choose the answer that correctly fills the blanks in the code block to accomplish this.

Sample of 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.__1__(__2__).select(__3__, __4__)

Options:

A.

1. filter

2. col("supplier").isin("Sports")

3. "itemName"

4. explode(col("attributes"))

B.

1. where

2. col("supplier").contains("Sports")

3. "itemName"

4. "attributes"

C.

1. where

2. col(supplier).contains("Sports")

3. explode(attributes)

4. itemName

D.

1. where

2. "Sports".isin(col("Supplier"))

3. "itemName"

4. array_explode("attributes")

E.

1. filter

2. col("supplier").contains("Sports")

3. "itemName"

4. explode("attributes")