Databricks DATABRICKS-CERTIFIED-ASSOCIATE-DEVELOPER-FOR-APACHE-SPARK - Questions & Answers

Which of the following code blocks silently writes DataFrame itemsDf in avro format to location fileLocation if a file does not yet exist at that location?

Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?

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 code blocks displays the 10 rows with the smallest values of column value in DataFrame transactionsDf in a nicely formatted way?

Which of the following code blocks can be used to save DataFrame transactionsDf to memory only, recalculating partitions that do not fit in memory when they are needed?

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 return a DataFrame with only columns from DataFrame transactionsDf for which there is a corresponding transactionId in DataFrame itemsDf. DataFrame itemsDf is very small and much smaller than DataFrame transactionsDf. The query should be executed in an optimized way. Choose the answer that correctly fills the blanks in the code block to accomplish this.
__1__.__2__(__3__, __4__, __5__)

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)

Which of the following is a viable way to improve Spark's performance when dealing with large amounts of data, given that there is only a single application running on the cluster?

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