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

Section	Weight	Objectives
Topic 1: Structured Streaming	10%	- Defining streaming queries - Output modes and triggers - Streaming concepts and architecture - Fault tolerance and state management
Topic 2: Using Pandas API on Apache Spark	5%	- Overview of Pandas API on Spark - Converting between Pandas and Spark structures - Key differences and limitations
Topic 3: Using Spark SQL	20%	- Using catalog and metadata APIs - Integrating Spark SQL with DataFrames - Working with functions and expressions - Running SQL queries
Topic 4: Developing Apache Spark DataFrame API Applications	30%	- Selecting, renaming, and modifying columns - Filtering, sorting, and aggregating data - Creating DataFrames and defining schemas - Reading and writing data in various formats - Handling missing values and data quality - User-defined functions (UDFs) - Partitioning and bucketing data - Joining and combining datasets
Topic 5: Apache Spark Architecture and Components	20%	- Spark architecture overview - Fault tolerance and garbage collection - Execution and deployment modes - Execution hierarchy and lazy evaluation - Shuffling, actions, and broadcasting
Topic 6: Using Spark Connect to Deploy Applications	5%	- Running applications via Spark Connect - Connecting to remote Spark clusters - Spark Connect architecture
Topic 7: Troubleshooting and Tuning Apache Spark DataFrame API Applications	10%	- Optimizing transformations and actions - Debugging and logging - Managing memory and resource usage - Identifying performance bottlenecks

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

1. 12 of 55.
A data scientist has been investigating user profile data to build features for their model. After some exploratory data analysis, the data scientist identified that some records in the user profiles contain NULL values in too many fields to be useful.
The schema of the user profile table looks like this:
user_id STRING,
username STRING,
date_of_birth DATE,
country STRING,
created_at TIMESTAMP
The data scientist decided that if any record contains a NULL value in any field, they want to remove that record from the output before further processing.
Which block of Spark code can be used to achieve these requirements?

A) filtered_users = raw_users.na.drop("all")
B) filtered_users = raw_users.na.drop("any")
C) filtered_users = raw_users.dropna(how="all")
D) filtered_users = raw_users.dropna(how="any")

2. 15 of 55.
A data engineer is working on a Streaming DataFrame (streaming_df) with the following streaming data:
id
name
count
timestamp
1
Delhi
20
2024-09-19T10:11
1
Delhi
50
2024-09-19T10:12
2
London
50
2024-09-19T10:15
3
Paris
30
2024-09-19T10:18
3
Paris
20
2024-09-19T10:20
4
Washington
10
2024-09-19T10:22
Which operation is supported with streaming_df?

A) streaming_df.show()
B) streaming_df.filter("count < 30")
C) streaming_df.select(countDistinct("name"))
D) streaming_df.count()

3. A Spark developer wants to improve the performance of an existing PySpark UDF that runs a hash function that is not available in the standard Spark functions library. The existing UDF code is:

import hashlib
import pyspark.sql.functions as sf
from pyspark.sql.types import StringType
def shake_256(raw):
return hashlib.shake_256(raw.encode()).hexdigest(20)
shake_256_udf = sf.udf(shake_256, StringType())
The developer wants to replace this existing UDF with a Pandas UDF to improve performance. The developer changes the definition of shake_256_udf to this:CopyEdit shake_256_udf = sf.pandas_udf(shake_256, StringType()) However, the developer receives the error:
What should the signature of the shake_256() function be changed to in order to fix this error?

A) def shake_256(df: pd.Series) -> pd.Series:
B) def shake_256(df: pd.Series) -> str:
C) def shake_256(raw: str) -> str:
D) def shake_256(df: Iterator[pd.Series]) -> Iterator[pd.Series]:

4. A data engineer is streaming data from Kafka and requires:
Minimal latency
Exactly-once processing guarantees
Which trigger mode should be used?

A) .trigger(availableNow=True)
B) .trigger(processingTime='1 second')
C) .trigger(continuous='1 second')
D) .trigger(continuous=True)

5. In the code block below, aggDF contains aggregations on a streaming DataFrame:

Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?