Become Google Certified with updated Professional-Data-Engineer exam questions and correct answers
You want to schedule a number of sequential load and transformation jobs Data files will be added to a Cloud
Storage bucket by an upstream process There is no fixed schedule for when the new data arrives Next, a
Dataproc job is triggered to perform some transformations and write the data to BigQuery. You then need to
run additional transformation jobs in BigQuery The transformation jobs are different for every table These
jobs might take hours to complete You need to determine the most efficient and maintainable workflow to
process hundreds of tables and provide the freshest data to your end users. What should you do?
Your company is currently setting up data pipelines for their campaign. For all the Google Cloud Pub/Sub streaming data, one of the important business requirements is to be able to periodically identify the inputs and their timings during their campaign. Engineers have decided to use windowing and transformation in Google Cloud Dataflow for this purpose. However, when testing this feature, they find that the Cloud Dataflow job fails for the all streaming insert. What is the most likely cause of this problem?
You have several Spark jobs that run on a Cloud Dataproc cluster on a schedule. Some of the jobs run in sequence, and some of the jobs run concurrently. You need to automate this process. What should you do?
If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?
You are developing a software application using Google's Dataflow SDK, and want to use conditional, for loops and other complex programming structures to create a branching pipeline. Which component will be used for the data processing operation?
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