Databricks Associate-Developer-Apache-Spark-3.5 Valid Test : Databricks Certified Associate Developer for Apache Spark 3.5 - Python

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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. 44 of 55.
A data engineer is working on a real-time analytics pipeline using Spark Structured Streaming.
They want the system to process incoming data in micro-batches at a fixed interval of 5 seconds.
Which code snippet fulfills this requirement?

A) query = df.writeStream \
.outputMode("append") \
.start()
B) query = df.writeStream \
.outputMode("append") \
.trigger(continuous="5 seconds") \
.start()
C) query = df.writeStream \
.outputMode("append") \
.trigger(once=True) \
.start()
D) query = df.writeStream \
.outputMode("append") \
.trigger(processingTime="5 seconds") \
.start()

2. An application architect has been investigating Spark Connect as a way to modernize existing Spark applications running in their organization.
Which requirement blocks the adoption of Spark Connect in this organization?

A) Stability: isolation of application code and dependencies from each other and the Spark driver
B) Debuggability: the ability to perform interactive debugging directly from the application code
C) Upgradability: the ability to upgrade the Spark applications independently from the Spark driver itself
D) Complete Spark API support: the ability to migrate all existing code to Spark Connect without modification, including the RDD APIs

3. A developer wants to refactor some older Spark code to leverage built-in functions introduced in Spark 3.5.0. The existing code performs array manipulations manually. Which of the following code snippets utilizes new built-in functions in Spark 3.5.0 for array operations?

A)

result_df = prices_df \
.agg(F.count("spot_price").alias("spot_price")) \
.filter(F.col("spot_price") > F.lit("min_price"))
B)

result_df = prices_df \
.agg(F.min("spot_price"), F.max("spot_price"))
C)

result_df = prices_df \
.agg(F.count_if(F.col("spot_price") >= F.lit(min_price)))
D)

result_df = prices_df \
.withColumn("valid_price", F.when(F.col("spot_price") > F.lit(min_price), 1).otherwise(0))

4. 32 of 55.
A developer is creating a Spark application that performs multiple DataFrame transformations and actions. The developer wants to maintain optimal performance by properly managing the SparkSession.
How should the developer handle the SparkSession throughout the application?

A) Create a new SparkSession instance before each transformation.
B) Avoid using a SparkSession and rely on SparkContext only.
C) Use a single SparkSession instance for the entire application.
D) Stop and restart the SparkSession after each action.

5. What is the relationship between jobs, stages, and tasks during execution in Apache Spark?
Options:

A) A job contains multiple stages, and each stage contains multiple tasks.
B) A stage contains multiple tasks, and each task contains multiple jobs.
C) A stage contains multiple jobs, and each job contains multiple tasks.
D) A job contains multiple tasks, and each task contains multiple stages.

Solutions: