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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Data Ingestion and Storage | - Delta Lake basics - Reading and writing data (Parquet, JSON, CSV) |
| DataFrame API with PySpark | - Built-in functions and expressions - Transformations and actions - DataFrame creation and schema management |
| Spark SQL | - Window functions and aggregations - SQL queries on DataFrames and tables |
| Apache Spark Fundamentals | - Spark architecture and execution model - RDD vs DataFrame vs Dataset concepts |
| Data Processing and Performance | - Caching and persistence strategies - Optimization techniques - Joins and data partitioning |
| Structured Streaming Basics | - Streaming DataFrames - Windowed aggregations in streaming |
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_pandas()
- B. psdf.to_dataframe()
- C. psdf.to_spark()
- D. psdf.to_pyspark()
Correct Answer: C 🗳️
Explanation: Only visible for Prep4sureExam members. You can sign-up / login (it's free).
36 of 55.
What is the main advantage of partitioning the data when persisting tables?
- A. It automatically cleans up unused partitions to optimize storage.
- B. It optimizes by reading only the relevant subset of data from fewer partitions.
- C. It compresses the data to save disk space.
- D. It ensures that data is loaded into memory all at once for faster query execution.
Correct Answer: B 🗳️
Explanation: Only visible for Prep4sureExam members. You can sign-up / login (it's free).
47 of 55.
A data engineer has written the following code to join two DataFrames df1 and df2:
df1 = spark.read.csv("sales_data.csv")
df2 = spark.read.csv("product_data.csv")
df_joined = df1.join(df2, df1.product_id == df2.product_id)
The DataFrame df1 contains ~10 GB of sales data, and df2 contains ~8 MB of product data.
Which join strategy will Spark use?
- A. Broadcast join, as df2 is smaller than the default broadcast threshold.
- B. Shuffle join, because AQE is not enabled, and Spark uses a static query plan.
- C. Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently.
- D. Shuffle join because no broadcast hints were provided.
Correct Answer: A 🗳️
Explanation: Only visible for Prep4sureExam members. You can sign-up / login (it's free).
A data engineer is building a Structured Streaming pipeline and wants the pipeline to recover from failures or intentional shutdowns by continuing where the pipeline left off.
How can this be achieved?
- A. By configuring the option checkpointLocation during writeStream
- B. By configuring the option recoveryLocation during writeStream
- C. By configuring the option checkpointLocation during readStream
- D. By configuring the option recoveryLocation during the SparkSession initialization
Correct Answer: A 🗳️
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43 of 55.
An organization has been running a Spark application in production and is considering disabling the Spark History Server to reduce resource usage.
What will be the impact of disabling the Spark History Server in production?
- A. Loss of access to past job logs and reduced debugging capability for completed jobs
- B. Prevention of driver log accumulation during long-running jobs
- C. Enhanced executor performance due to reduced log size
- D. Improved job execution speed due to reduced logging overhead
Correct Answer: A 🗳️
Explanation: Only visible for Prep4sureExam members. You can sign-up / login (it's free).
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