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

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

1. 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) Support for SQL queries on structured data
B) Ability to process small datasets efficiently
C) Built-in machine learning libraries
D) In-memory computation and parallel processing capabilities

2. 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 the second join condition (df2.id == df3.id) is incorrect.
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 only one broadcast join can be performed at a time.

3. A data engineer wants to process a streaming DataFrame that receives sensor readings every second with columns sensor_id, temperature, and timestamp. The engineer needs to calculate the average temperature for each sensor over the last 5 minutes while the data is streaming.
Which code implementation achieves the requirement?
Options from the images provided:

A)

B)

C)

D)

4. 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) Broadcast a variable to share the maximum time among workers.
D) Configure the Spark UI to automatically collect maximum times.

5. 24 of 55.
Which code should be used to display the schema of the Parquet file stored in the location events.parquet?

A) spark.sql("SELECT * FROM events.parquet").show()
B) spark.read.format("parquet").load("events.parquet").show()
C) spark.read.parquet("events.parquet").printSchema()
D) spark.sql("SELECT schema FROM events.parquet").show()

Solutions: