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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 write a DataFrame df to a Parquet file, partitioned by the column country, and overwrite any existing data at the destination path.
Which code should the data engineer use to accomplish this task in Apache Spark?

A) df.write.mode("append").partitionBy("country").parquet("/data/output")
B) df.write.mode("overwrite").parquet("/data/output")
C) df.write.mode("overwrite").partitionBy("country").parquet("/data/output")
D) df.write.partitionBy("country").parquet("/data/output")

2. 2 of 55. Which command overwrites an existing JSON file when writing a DataFrame?

A) df.write.option("overwrite").json("path/to/file")
B) df.write.mode("append").json("path/to/file")
C) df.write.json("path/to/file")
D) df.write.mode("overwrite").json("path/to/file")

3. A data engineer uses a broadcast variable to share a DataFrame containing millions of rows across executors for lookup purposes. What will be the outcome?

A) The job may fail if the memory on each executor is not large enough to accommodate the DataFrame being broadcasted
B) The job will hang indefinitely as Spark will struggle to distribute and serialize such a large broadcast variable to all executors
C) The job may fail if the executors do not have enough CPU cores to process the broadcasted dataset
D) The job may fail because the driver does not have enough CPU cores to serialize the large DataFrame

4. 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 stores the frequently accessed rows in memory and less frequently accessed rows on disk, utilizing both resources to offer balanced performance.
B) 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.
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 splits the DataFrame evenly between memory and disk, ensuring balanced storage utilization.

5. 16 of 55.
A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately.
Which two characteristics of Apache Spark's execution model explain this behavior? (Choose 2 answers)

A) Only actions trigger the execution of the transformation pipeline.
B) The Spark engine optimizes the execution plan during the transformations, causing delays.
C) Transformations are evaluated lazily.
D) Transformations are executed immediately to build the lineage graph.
E) The Spark engine requires manual intervention to start executing transformations.

Solutions: