Pass your actual test at first attempt with Databricks Associate-Developer-Apache-Spark-3.5 training material
Updated: Jun 03, 2026
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1. What is a feature of Spark Connect?
A) Supports DataFrame, Functions, Column, SparkContext PySpark APIs
B) It supports DataStreamReader, DataStreamWriter, StreamingQuery, and Streaming APIs
C) It has built-in authentication
D) It supports only PySpark applications
2. A Spark DataFrame df is cached using the MEMORY_AND_DISK storage level, but the DataFrame is too large to fit entirely in memory.
What is the likely behavior when Spark runs out of memory to store the DataFrame?
A) Spark will store as much data as possible in memory and spill the rest to disk when memory is full, continuing processing with performance overhead.
B) Spark splits the DataFrame evenly between memory and disk, ensuring balanced storage utilization.
C) Spark duplicates the DataFrame in both memory and disk. If it doesn't fit in memory, the DataFrame is stored and retrieved from the disk entirely.
D) Spark stores the frequently accessed rows in memory and less frequently accessed rows on disk, utilizing both resources to offer balanced performance.
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) Broadcast a variable to share the maximum time among workers.
B) Use an RDD action like reduce() to compute the maximum time.
C) Configure the Spark UI to automatically collect maximum times.
D) Use an accumulator to record the maximum time on the driver.
4. An engineer has two DataFrames: df1 (small) and df2 (large). A broadcast join is used:
python
CopyEdit
from pyspark.sql.functions import broadcast
result = df2.join(broadcast(df1), on='id', how='inner')
What is the purpose of using broadcast() in this scenario?
Options:
A) It ensures that the join happens only when the id values are identical.
B) It reduces the number of shuffle operations by replicating the smaller DataFrame to all nodes.
C) It increases the partition size for df1 and df2.
D) It filters the id values before performing the join.
5. 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 recoveryLocation during writeStream.
B) By configuring the option recoveryLocation during SparkSession initialization.
C) By configuring the option checkpointLocation during writeStream.
D) By configuring the option checkpointLocation during readStream.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: A | Question # 3 Answer: B | Question # 4 Answer: B | Question # 5 Answer: C |
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