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

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

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

1. Given the code:

df = spark.read.csv("large_dataset.csv")
filtered_df = df.filter(col("error_column").contains("error"))
mapped_df = filtered_df.select(split(col("timestamp"), " ").getItem(0).alias("date"), lit(1).alias("count")) reduced_df = mapped_df.groupBy("date").sum("count") reduced_df.count() reduced_df.show() At which point will Spark actually begin processing the data?

A) When the filter transformation is applied
B) When the groupBy transformation is applied
C) When the count action is applied
D) When the show action is applied

2. A developer is trying to join two tables, sales.purchases_fct and sales.customer_dim, using the following code:

fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid')) The developer has discovered that customers in the purchases_fct table that do not exist in the customer_dim table are being dropped from the joined table.
Which change should be made to the code to stop these customer records from being dropped?

A) fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'right_outer')
B) fact_df = cust_df.join(purch_df, F.col('customer_id') == F.col('custid'))
C) fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'left')
D) fact_df = purch_df.join(cust_df, F.col('cust_id') == F.col('customer_id'))

3. 39 of 55.
A Spark developer is developing a Spark application to monitor task performance across a cluster.
One requirement is to track the maximum processing time for tasks on each worker node and consolidate this information on the driver for further analysis.
Which technique should the developer use?

A) Use an RDD action like reduce() to compute the maximum time.
B) Use an accumulator to record the maximum time on the driver.
C) Configure the Spark UI to automatically collect maximum times.
D) Broadcast a variable to share the maximum time among workers.

4. 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) 'sep' ','
D) 'header' 'true'

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

Solutions:

Question # 1
Answer: C

Question # 2
Answer: C

Question # 3
Answer: A

Question # 4
Answer: D

Question # 5
Answer: C