Databricks Certified Associate Developer for Apache Spark 3.5 - Python : Associate-Developer-Apache-Spark-3.5

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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:

1. Given the schema:

event_ts TIMESTAMP,
sensor_id STRING,
metric_value LONG,
ingest_ts TIMESTAMP,
source_file_path STRING
The goal is to deduplicate based on: event_ts, sensor_id, and metric_value.
Options:

A) dropDuplicates on the exact matching fields
B) groupBy without aggregation (invalid use)
C) dropDuplicates with no arguments (removes based on all columns)
D) dropDuplicates on all columns (wrong criteria)

2. A data scientist of an e-commerce company is working with user data obtained from its subscriber database and has stored the data in a DataFrame df_user. Before further processing the data, the data scientist wants to create another DataFrame df_user_non_pii and store only the non-PII columns in this DataFrame. The PII columns in df_user are first_name, last_name, email, and birthdate.
Which code snippet can be used to meet this requirement?

A) df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")
B) df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")
C) df_user_non_pii = df_user.dropfields("first_name, last_name, email, birthdate")
D) df_user_non_pii = df_user.dropfields("first_name", "last_name", "email", "birthdate")

3. The following code fragment results in an error:
@F.udf(T.IntegerType())
def simple_udf(t: str) -> str:
return answer * 3.14159
Which code fragment should be used instead?

A) @F.udf(T.DoubleType())
def simple_udf(t: int) -> int:
return t * 3.14159
B) @F.udf(T.IntegerType())
def simple_udf(t: int) -> int:
return t * 3.14159
C) @F.udf(T.DoubleType())
def simple_udf(t: float) -> float:
return t * 3.14159
D) @F.udf(T.IntegerType())
def simple_udf(t: float) -> float:
return t * 3.14159

4. 29 of 55.
A Spark application is experiencing performance issues in client mode due to the driver being resource-constrained.
How should this issue be resolved?

A) Switch the deployment mode to local mode.
B) Add more executor instances to the cluster.
C) Switch the deployment mode to cluster mode.
D) Increase the driver memory on the client machine.

5. 41 of 55.
A data engineer is working on the DataFrame df1 and wants the Name with the highest count to appear first (descending order by count), followed by the next highest, and so on.
The DataFrame has columns:
id | Name | count | timestamp
---------------------------------
1 | USA | 10
2 | India | 20
3 | England | 50
4 | India | 50
5 | France | 20
6 | India | 10
7 | USA | 30
8 | USA | 40
Which code fragment should the engineer use to sort the data in the Name and count columns?

A) df1.orderBy(col("Name").desc(), col("count").asc())
B) df1.sort("Name", "count")
C) df1.orderBy(col("count").desc(), col("Name").asc())
D) df1.orderBy("Name", "count")

Solutions: