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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. A data engineer needs to persist a file-based data source to a specific location. However, by default, Spark writes to the warehouse directory (e.g., /user/hive/warehouse). To override this, the engineer must explicitly define the file path.
Which line of code ensures the data is saved to a specific location?
Options:

A) users.write(path="/some/path").saveAsTable("default_table")
B) users.write.saveAsTable("default_table", path="/some/path")
C) users.write.saveAsTable("default_table").option("path", "/some/path")
D) users.write.option("path", "/some/path").saveAsTable("default_table")

2. Which Spark configuration controls the number of tasks that can run in parallel on the executor?
Options:

A) spark.executor.memory
B) spark.task.maxFailures
C) spark.executor.cores
D) spark.driver.cores

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

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

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

5. 23 of 55.
A data scientist is working with a massive dataset that exceeds the memory capacity of a single machine. The data scientist is considering using Apache Spark™ instead of traditional single-machine languages like standard Python scripts.
Which two advantages does Apache Spark™ offer over a normal single-machine language in this scenario? (Choose 2 answers)

A) It can distribute data processing tasks across a cluster of machines, enabling horizontal scalability.
B) It eliminates the need to write any code, automatically handling all data processing.
C) It has built-in fault tolerance, allowing it to recover seamlessly from node failures during computation.
D) It requires specialized hardware to run, making it unsuitable for commodity hardware clusters.
E) It processes data solely on disk storage, reducing the need for memory resources.

Solutions: