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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Apache Spark Architecture and Components | 20% | - Spark architecture overview - Execution and deployment modes - Execution hierarchy and lazy evaluation - Shuffling, actions, and broadcasting - Fault tolerance and garbage collection |
| Using Spark SQL | 20% | - Using catalog and metadata APIs - Running SQL queries - Working with functions and expressions - Integrating Spark SQL with DataFrames |
| Structured Streaming | 10% | - Output modes and triggers - Streaming concepts and architecture - Fault tolerance and state management - Defining streaming queries |
| Using Spark Connect to Deploy Applications | 5% | - Running applications via Spark Connect - Spark Connect architecture - Connecting to remote Spark clusters |
| Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Managing memory and resource usage - Optimizing transformations and actions - Debugging and logging - Identifying performance bottlenecks |
| Using Pandas API on Apache Spark | 5% | - Overview of Pandas API on Spark - Key differences and limitations - Converting between Pandas and Spark structures |
| Developing Apache Spark DataFrame API Applications | 30% | - Reading and writing data in various formats - Handling missing values and data quality - Creating DataFrames and defining schemas - Partitioning and bucketing data - User-defined functions (UDFs) - Filtering, sorting, and aggregating data - Joining and combining datasets - Selecting, renaming, and modifying columns |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
Given the code fragment:
import pyspark.pandas as ps
psdf = ps.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?
- A. psdf.to_dataframe()
- B. psdf.to_spark()
- C. psdf.to_pyspark()
- D. psdf.to_pandas()
Explanation: Only visible for TestInsides members. You can sign-up / login (it's free).
3 of 55. A data engineer observes that the upstream streaming source feeds the event table frequently and sends duplicate records. Upon analyzing the current production table, the data engineer found that the time difference in the event_timestamp column of the duplicate records is, at most, 30 minutes.
To remove the duplicates, the engineer adds the code:
df = df.withWatermark("event_timestamp", "30 minutes")
What is the result?
- A. It accepts watermarks in seconds and the code results in an error.
- B. It removes all duplicates regardless of when they arrive.
- C. It removes duplicates that arrive within the 30-minute window specified by the watermark.
- D. It is not able to handle deduplication in this scenario.
Explanation: Only visible for TestInsides members. You can sign-up / login (it's free).
13 of 55.
A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:
region_id
region_name
10
North
12
East
14
West
The resulting Python dictionary must contain a mapping of region_id to region_name, containing the smallest 3 region_id values.
Which code fragment meets the requirements?
- A. regions_dict = regions.select("region_id", "region_name").take(3)
- B. regions_dict = dict(regions.select("region_id", "region_name").rdd.collect())
- C. regions_dict = dict(regions.orderBy("region_id").limit(3).rdd.map(lambda x: (x.region_id, x.region_name)).collect())
- D. regions_dict = dict(regions.take(3))
Explanation: Only visible for TestInsides members. You can sign-up / login (it's free).
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_dataframe()
- B. pdf.to_spark()
- C. pdf.to_pandas()
- D. pdf.spark()
Explanation: Only visible for TestInsides members. You can sign-up / login (it's free).
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="any")
- D. filtered_users = raw_users.dropna(how="all")
Explanation: Only visible for TestInsides members. You can sign-up / login (it's free).
Reginald 
Lucien 
Donna 
Kennedy 
Frank 
Odelia 
Marico 
Brady 
Jacob 
Edith 
Leo 
Morgan 
Modesty 
Bevis 
Letitia 
