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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. An engineer notices a significant increase in the job execution time during the execution of a Spark job. After some investigation, the engineer decides to check the logs produced by the Executors.
How should the engineer retrieve the Executor logs to diagnose performance issues in the Spark application?

A) Use the command spark-submit with the -verbose flag to print the logs to the console.
B) Locate the executor logs on the Spark master node, typically under the /tmp directory.
C) Fetch the logs by running a Spark job with the spark-sql CLI tool.
D) Use the Spark UI to select the stage and view the executor logs directly from the stages tab.

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

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

3. A data engineer writes the following code to join two DataFrames df1 and df2:
df1 = spark.read.csv("sales_data.csv") # ~10 GB
df2 = spark.read.csv("product_data.csv") # ~8 MB
result = df1.join(df2, df1.product_id == df2.product_id)

Which join strategy will Spark use?

A) Shuffle join, because AQE is not enabled, and Spark uses a static query plan
B) Shuffle join because no broadcast hints were provided
C) Broadcast join, as df2 is smaller than the default broadcast threshold
D) Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently

4. 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")

5. A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:

The resulting Python dictionary must contain a mapping of region -> region id containing the smallest 3 region_id values.
Which code fragment meets the requirements?
A)

B)

C)

D)

The resulting Python dictionary must contain a mapping of region -> region_id for the smallest 3 region_id values.
Which code fragment meets the requirements?

A) regions = dict(
regions_df
.select('region_id', 'region')
.sort('region_id')
.take(3)
)
B) regions = dict(
regions_df
.select('region', 'region_id')
.sort('region_id')
.take(3)
)
C) regions = dict(
regions_df
.select('region', 'region_id')
.sort(desc('region_id'))
.take(3)
)
D) regions = dict(
regions_df
.select('region_id', 'region')
.limit(3)
.collect()
)

Solutions: