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

Section	Weight	Objectives
Topic 1: Developing Apache Spark DataFrame API Applications	30%	- DataFrame Operations 1. Working with complex data types 2. Creating and transforming DataFrames 3. Reading and writing data 4. User Defined Functions 5. Selecting and renaming columns 6. Partitioning data 7. Handling null values
Topic 2: Using Pandas API on Spark	5%	- Pandas API 1. Interoperability with PySpark 2. Pandas on Spark DataFrames 3. Pandas transformations
Topic 3: Troubleshooting and Tuning	10%	- Performance Optimization 1. Shuffle optimization 2. Execution plan analysis 3. Caching and persistence 4. Broadcast joins
Topic 4: Apache Spark Architecture and Components	20%	- Spark Architecture 1. Adaptive Query Execution 2. Driver and Executor roles 3. Lazy evaluation 4. Cluster managers
Topic 5: Structured Streaming	10%	- Streaming Applications 1. Streaming sources and sinks 2. Structured Streaming concepts 3. Output modes 4. Triggers and checkpoints
Topic 6: Using Spark Connect to Deploy Applications	5%	- Spark Connect 1. Client-server architecture 2. Application deployment 3. Remote Spark sessions
Topic 7: Using Spark SQL	20%	- Spark SQL Operations 1. Built-in SQL functions 2. Joins and subqueries 3. Filtering and sorting data 4. Aggregations and grouping 5. Window functions

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

1. A DataFrame df has columns name, age, and salary. The developer needs to sort the DataFrame by age in ascending order and salary in descending order.
Which code snippet meets the requirement of the developer?

A) df.orderBy(col("age").asc(), col("salary").asc()).show()
B) df.orderBy("age", "salary", ascending=[True, False]).show()
C) df.sort("age", "salary", ascending=[True, True]).show()
D) df.sort("age", "salary", ascending=[False, True]).show()

2. Which Spark configuration controls the number of tasks that can run in parallel on the executor?
Options:

A) spark.executor.memory
B) spark.driver.cores
C) spark.task.maxFailures
D) spark.executor.cores

3. 9 of 55.
Given the code fragment:
import pyspark.pandas as ps
pdf = ps.DataFrame(data)
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?

A) pdf.to_pandas()
B) pdf.to_dataframe()
C) pdf.spark()
D) pdf.to_spark()

4. Which UDF implementation calculates the length of strings in a Spark DataFrame?

A) df.withColumn("length", spark.udf("len", StringType()))
B) df.withColumn("length", udf(lambda s: len(s), StringType()))
C) spark.udf.register("stringLength", lambda s: len(s))
D) df.select(length(col("stringColumn")).alias("length"))

5. 7 of 55.
A developer has been asked to debug an issue with a Spark application. The developer identified that the data being loaded from a CSV file is being read incorrectly into a DataFrame.
The CSV file has been read using the following Spark SQL statement:
CREATE TABLE locations
USING csv
OPTIONS (path '/data/locations.csv')
The first lines of the command SELECT * FROM locations look like this:
| city | lat | long |
| ALTI Sydney | -33... | ... |
Which parameter can the developer add to the OPTIONS clause in the CREATE TABLE statement to read the CSV data correctly again?

A) 'header' 'false'
B) 'sep' ','
C) 'header' 'true'
D) 'sep' '|'

Solutions:

Question # 1
Answer: B

Question # 2
Answer: D

Question # 3
Answer: D

Question # 4
Answer: D

Question # 5
Answer: C