Databricks Associate-Developer-Apache-Spark-3.5 問題集

質問 1：
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_idvalues.
Which code fragment meets the requirements?
A)

B)

C)

D)

The resulting Python dictionary must contain a mapping ofregion -> region_idfor the smallest
3region_idvalues.
Which code fragment meets the requirements?
A. regions = dict(
regions_df
.select('region', 'region_id')
.sort(desc('region_id'))
.take(3)
)
B. regions = dict(
regions_df
.select('region_id', 'region')
.sort('region_id')
.take(3)
)
C. regions = dict(
regions_df
.select('region_id', 'region')
.limit(3)
.collect()
)
D. regions = dict(
regions_df
.select('region', 'region_id')
.sort('region_id')
.take(3)
)
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 2：
A data engineer wants to process a streaming DataFrame that receives sensor readings every second with columnssensor_id,temperature, andtimestamp. 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.

正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 3：
A Spark engineer is troubleshooting a Spark application that has been encountering out-of-memory errors during execution. By reviewing the Spark driver logs, the engineer notices multiple "GC overhead limit exceeded" messages.
Which action should the engineer take to resolve this issue?
A. Cache large DataFrames to persist them in memory.
B. Increase the memory allocated to the Spark Driver.
C. Optimize the data processing logic by repartitioning the DataFrame.
D. Modify the Spark configuration to disable garbage collection
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 4：
Given this code:

.withWatermark("event_time","10 minutes")
.groupBy(window("event_time","15 minutes"))
.count()
What happens to data that arrives after the watermark threshold?
Options:
A. Records that arrive later than the watermark threshold (10 minutes) will automatically be included in the aggregation if they fall within the 15-minute window.
B. Any data arriving more than 10 minutes after the watermark threshold will be ignored and not included in the aggregation.
C. Data arriving more than 10 minutes after the latest watermark will still be included in the aggregation but will be placed into the next window.
D. The watermark ensures that late data arriving within 10 minutes of the latest event_time will be processed and included in the windowed aggregation.
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 5：
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")
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 6：
Which feature of Spark Connect is considered when designing an application to enable remote interaction with the Spark cluster?
A. It is primarily used for data ingestion into Spark from external sources
B. It provides a way to run Spark applications remotely in any programming language
C. It can be used to interact with any remote cluster using the REST API
D. It allows for remote execution of Spark jobs
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 7：
Given the code fragment:

import pyspark.pandas as ps
psdf = ps.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?
A. psdf.to_dataframe()
B. psdf.to_spark()
C. psdf.to_pyspark()
D. psdf.to_pandas()
正解：B
解説: (Topexam メンバーにのみ表示されます)