Databricks Associate-Developer-Apache-Spark-3.5 Q&A - in .pdf
- Exam Code: Associate-Developer-Apache-Spark-3.5
- Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
- Updated: Oct 04, 2026
- Q & A: 135 Questions and Answers
- Printable Databricks Associate-Developer-Apache-Spark-3.5 PDF Format. It is an electronic file format regardless of the operating system platform.
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Databricks Associate-Developer-Apache-Spark-3.5 Q&A - Testing Engine
- Exam Code: Associate-Developer-Apache-Spark-3.5
- Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
- Updated: Oct 04, 2026
- Q & A: 135 Questions and Answers
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- PC Test Engine Price: $59.99
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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) |
| Apache Spark Fundamentals | - Spark architecture and execution model - RDD vs DataFrame vs Dataset concepts |
| Spark SQL | - SQL queries on DataFrames and tables - Window functions and aggregations |
| DataFrame API with PySpark | - Transformations and actions - Built-in functions and expressions - DataFrame creation and schema management |
| 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:
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. Use the spark.dynamicAllocation.enabled property to scale resources dynamically.
- C. Increase the number of local threads based on the number of CPU cores.
- D. Configure the application to run in cluster mode instead of local mode.
Correct Answer: C 🗳️
Explanation: Only visible for TorrentValid members. You can sign-up / login (it's free).
35 of 55.
A data engineer is building a Structured Streaming pipeline and wants it to recover from failures or intentional shutdowns by continuing where it left off.
How can this be achieved?
- A. By configuring the option checkpointLocation during readStream.
- B. By configuring the option recoveryLocation during SparkSession initialization.
- C. By configuring the option checkpointLocation during writeStream.
- D. By configuring the option recoveryLocation during writeStream.
Correct Answer: C 🗳️
Explanation: Only visible for TorrentValid members. You can sign-up / login (it's free).
A data engineer has been asked to produce a Parquet table which is overwritten every day with the latest data. The downstream consumer of this Parquet table has a hard requirement that the data in this table is produced with all records sorted by the market_time field.
Which line of Spark code will produce a Parquet table that meets these requirements?
- A. final_df \
.sortWithinPartitions("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events") - B. final_df \
.sort("market_time") \
.coalesce(1) \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events") - C. final_df \
.orderBy("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events") - D. final_df \
.sort("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
Correct Answer: A 🗳️
Explanation: Only visible for TorrentValid members. You can sign-up / login (it's free).
48 of 55.
A data engineer needs to join multiple DataFrames and has written the following code:
from pyspark.sql.functions import broadcast
data1 = [(1, "A"), (2, "B")]
data2 = [(1, "X"), (2, "Y")]
data3 = [(1, "M"), (2, "N")]
df1 = spark.createDataFrame(data1, ["id", "val1"])
df2 = spark.createDataFrame(data2, ["id", "val2"])
df3 = spark.createDataFrame(data3, ["id", "val3"])
df_joined = df1.join(broadcast(df2), "id", "inner") \
.join(broadcast(df3), "id", "inner")
What will be the output of this code?
- A. The code will fail because only one broadcast join can be performed at a time.
- B. The code will result in an error because broadcast() must be called before the joins, not inline.
- C. The code will work correctly and perform two broadcast joins simultaneously to join df1 with df2, and then the result with df3.
- D. The code will fail because the second join condition (df2.id == df3.id) is incorrect.
Correct Answer: C 🗳️
Explanation: Only visible for TorrentValid members. You can sign-up / login (it's free).
8 of 55.
A data scientist at a large e-commerce company needs to process and analyze 2 TB of daily customer transaction data. The company wants to implement real-time fraud detection and personalized product recommendations.
Currently, the company uses a traditional relational database system, which struggles with the increasing data volume and velocity.
Which feature of Apache Spark effectively addresses this challenge?
- A. Ability to process small datasets efficiently
- B. In-memory computation and parallel processing capabilities
- C. Built-in machine learning libraries
- D. Support for SQL queries on structured data
Correct Answer: B 🗳️
Explanation: Only visible for TorrentValid members. You can sign-up / login (it's free).
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