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1. 46 of 55.
A data engineer is implementing a streaming pipeline with watermarking to handle late-arriving records.
The engineer has written the following code:
inputStream \
.withWatermark("event_time", "10 minutes") \
.groupBy(window("event_time", "15 minutes"))
What happens to data that arrives after the watermark threshold?

A) 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.
B) 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.
C) Any data arriving more than 10 minutes after the watermark threshold will be ignored and not included in the aggregation.
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.

2. A data engineer is running a batch processing job on a Spark cluster with the following configuration:
10 worker nodes
16 CPU cores per worker node
64 GB RAM per node
The data engineer wants to allocate four executors per node, each executor using four cores.
What is the total number of CPU cores used by the application?

A) 64
B) 160
C) 80
D) 40

3. 14 of 55.
A developer created a DataFrame with columns color, fruit, and taste, and wrote the data to a Parquet directory using:
df.write.partitionBy("color", "taste").parquet("/path/to/output")
What is the result of this code?

A) It stores all data in a single Parquet file.
B) It throws an error if there are null values in either partition column.
C) It appends new partitions to an existing Parquet file.
D) It creates separate directories for each unique combination of color and taste.

4. 4 of 55.
A developer is working on a Spark application that processes a large dataset using SQL queries. Despite having a large cluster, the developer notices that the job is underutilizing the available resources. Executors remain idle for most of the time, and logs reveal that the number of tasks per stage is very low. The developer suspects that this is causing suboptimal cluster performance.
Which action should the developer take to improve cluster utilization?

A) Increase the size of the dataset to create more partitions
B) Reduce the value of spark.sql.shuffle.partitions
C) Enable dynamic resource allocation to scale resources as needed
D) Increase the value of spark.sql.shuffle.partitions

5. 54 of 55.
What is the benefit of Adaptive Query Execution (AQE)?

A) It enables the adjustment of the query plan during runtime, handling skewed data, optimizing join strategies, and improving overall query performance.
B) It automatically distributes tasks across nodes in the clusters and does not perform runtime adjustments to the query plan.
C) It allows Spark to optimize the query plan before execution but does not adapt during runtime.
D) It optimizes query execution by parallelizing tasks and does not adjust strategies based on runtime metrics like data skew.

問題與答案：