Free Practice Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Exam Questions 2025

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Total 420 Questions | Updated On: Apr 08, 2025

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Question 1

Which of the followings are useful use cases of spark ?

A : Analyzing graph data sets and social networks

B : Building, training, and evaluating machine learning models using MLlib

C : Processing in parallel large data sets distributed across a cluster

D : Performing ad hoc or interactive queries to explore and visualize data sets

E : All of the answers are correct.

Answer: A

Question 2

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."""

A : spark.read.json(filePath, schema=json_schema)

B : spark.read.schema(json_schema).json(filePath) 1.schema = StructType.fromJson(json.loads(json_schema))2.spark.read.json(filePath, schema=schema)

C : spark.read.json(filePath, schema=schema_of_json(json_schema))

D : spark.read.json(filePath, schema=spark.read.json(json_schema))

Answer: B

Question 3

Given that the number of partitions of dataframe df is 4 and we want to write a parquet file in a given path. Choose the correct number of files after a successful write operation.

A : 4

B : 1

C : 8

Answer: A

Question 4

Which of the following code blocks returns all unique values across all values in columns value and productId in DataFrame transactionsDf in a one-column DataFrame?

A : tranactionsDf.select('value').join(transactionsDf.select('productId'), col('value')==col('productId'), 'outer')

B : transactionsDf.select(col('value'), col('productId')).agg({'*': 'count'})

C : transactionsDf.select('value', 'productId').distinct()

D : transactionsDf.select('value').union(transactionsDf.select('productId')).distinct()

E : transactionsDf.agg({'value': 'collect_set', 'productId': 'collect_set'})

Answer: D

Question 5

When joining two dataframes, if there is a need to evaluate the keys in both of the DataFrames or tables and include all rows from the right DataFrame as well as any rows

in the left DataFrame that have a match in the righ DataFrame also If there is no equivalent row in the left DataFrame, we want to instert null: which join type we should

select ? df1.join(person, joinExpression, joinType)

A : joinType = “right_outer”

B : joinType = “rightSemi”

C : joinType = “rightAnti”

D : joinType = “rightOuter”

Answer: A

Page: 1 / 84

Total 420 Questions | Updated On: Apr 08, 2025

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