Crucial Exams

AWS Certified Data Engineer Associate DEA-C01 Practice Question

An AWS Glue Spark job ingests click-stream data stored in Amazon S3 as Parquet files partitioned by the column event_date (YYYY-MM-DD). The job is run daily with a job parameter DATE=2025-10-01, but the code currently executes:

path = 's3://analytics/clicks/*'
df = spark.read.parquet(path)
df_filtered = df.filter(df.event_date == DATE)

The team reports that the job scans several terabytes and exceeds its 15-minute SLA. Which change will MOST effectively reduce the job's runtime with minimal additional cost?

  • Insert df = df.repartition(1) immediately after the filter to minimize the number of output files.

  • Double the executor and driver memory in the Glue job's Spark configuration.

  • Change the read path to s3://analytics/clicks/event_date=${DATE}/ (or pass a push_down_predicate for event_date) so Spark loads only the matching partition.

  • Call df.cache() before all downstream transformations to keep the dataset in memory.

AWS Certified Data Engineer Associate DEA-C01
Data Ingestion and Transformation
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