You work for a manufacturing plant that batches application log files together into a single log file once a day at 2:00 AM. You have written a Google Cloud Dataflow job to process that log file. You

Question

Options

• AChange the processing job to use Google Cloud Dataproc instead.
• BManually start the Cloud Dataflow job each morning when you get into the office.
• CCreate a cron job with Google App Engine Cron Service to run the Cloud Dataflow job.
• DConfigure the Cloud Dataflow job as a streaming job so that it processes the log data immediately.

• A
  8% (3)
• B
  16% (6)
• C
  70% (26)
• D
  5% (2)

Explanation

Option C is correct because App Engine Cron Service provides a managed, serverless scheduling mechanism that can trigger the Dataflow job automatically at a fixed time each day (e.g., shortly after 2:00 AM), requiring no infrastructure to maintain and incurring minimal cost - you only pay when the job actually runs.

Why the others are wrong:

• A (Dataproc) is a cluster-based Hadoop/Spark service; switching to it adds complexity and cost without solving the scheduling problem, and clusters often sit idle between jobs.
• B (Manual start) is not automated, is error-prone, depends on a person being available, and is the opposite of "inexpensive" in operational terms.
• D (Streaming job) keeps the pipeline running continuously 24/7, which is far more expensive than a batch job that runs once a day - it's the wrong tool when data only arrives once daily.

Memory tip: Think "batch data = batch job = scheduled trigger." When you see "once per day" + "as inexpensively as possible," map that to a cron-style scheduler (App Engine Cron) launching a bounded Dataflow batch job - streaming is for continuous data, not daily dumps.

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