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

Section	Objectives
Topic 1: Structured Streaming Basics	- Windowed aggregations in streaming - Streaming DataFrames
Topic 2: DataFrame API with PySpark	- Built-in functions and expressions - Transformations and actions - DataFrame creation and schema management
Topic 3: Data Ingestion and Storage	- Delta Lake basics - Reading and writing data (Parquet, JSON, CSV)
Topic 4: Data Processing and Performance	- Optimization techniques - Caching and persistence strategies - Joins and data partitioning
Topic 5: Apache Spark Fundamentals	- Spark architecture and execution model - RDD vs DataFrame vs Dataset concepts
Topic 6: Spark SQL	- Window functions and aggregations - SQL queries on DataFrames and tables

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

1. 30 of 55.
A data engineer is working on a num_df DataFrame and has a Python UDF defined as:
def cube_func(val):
return val * val * val
Which code fragment registers and uses this UDF as a Spark SQL function to work with the DataFrame num_df?

A) num_df.register("cube_func").select("num").show()
B) spark.createDataFrame(cube_func("num")).show()
C) spark.udf.register("cube_func", cube_func)
num_df.selectExpr("cube_func(num)").show()
D) num_df.select(cube_func("num")).show()

2. A data engineer is working on the DataFrame:

(Referring to the table image: it has columns Id, Name, count, and timestamp.) Which code fragment should the engineer use to extract the unique values in the Name column into an alphabetically ordered list?

A) df.select("Name").orderBy(df["Name"].asc())
B) df.select("Name").distinct()
C) df.select("Name").distinct().orderBy(df["Name"])
D) df.select("Name").distinct().orderBy(df["Name"].desc())

3. Given this code:

.withWatermark("event_time", "10 minutes")
.groupBy(window("event_time", "15 minutes"))
.count()
What happens to data that arrives after the watermark threshold?
Options:

A) Records that arrive later than the watermark threshold (10 minutes) will automatically be included in the aggregation if they fall within the 15-minute window.
B) The watermark ensures that late data arriving within 10 minutes of the latest event_time will be processed and included in the windowed aggregation.
C) Data arriving more than 10 minutes after the latest watermark will still be included in the aggregation but will be placed into the next window.
D) Any data arriving more than 10 minutes after the watermark threshold will be ignored and not included in the aggregation.

4. How can a Spark developer ensure optimal resource utilization when running Spark jobs in Local Mode for testing?
Options:

A) Set the spark.executor.memory property to a large value.
B) Increase the number of local threads based on the number of CPU cores.
C) Configure the application to run in cluster mode instead of local mode.
D) Use the spark.dynamicAllocation.enabled property to scale resources dynamically.

5. 38 of 55.
A data engineer is working with Spark SQL and has a large JSON file stored at /data/input.json.
The file contains records with varying schemas, and the engineer wants to create an external table in Spark SQL that:
Reads directly from /data/input.json.
Infers the schema automatically.
Merges differing schemas.
Which code snippet should the engineer use?

A) CREATE EXTERNAL TABLE users
USING json
OPTIONS (path '/data/input.json', mergeAll 'true');
B) CREATE EXTERNAL TABLE users
USING json
OPTIONS (path '/data/input.json', mergeSchema 'true');
C) CREATE TABLE users
USING json
OPTIONS (path '/data/input.json');
D) CREATE EXTERNAL TABLE users
USING json
OPTIONS (path '/data/input.json', inferSchema 'true');