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Google Certified Professional Data Engineer Exam Sample Questions (Q347-Q352):

NEW QUESTION # 347
You maintain ETL pipelines. You notice that a streaming pipeline running on Dataflow is taking a long time to process incoming data, which causes output delays. You also noticed that the pipeline graph was automatically optimized by Dataflow and merged into one step. You want to identify where the potential bottleneck is occurring. What should you do?

Answer: D

Explanation:
A Reshuffle operation is a way to force Dataflow to split the pipeline into multiple stages, which can help isolate the performance of each step and identify bottlenecks. By monitoring the execution details in the Dataflow console, you can see the time, CPU, memory, and disk usage of each stage, as well as the number of elements and bytes processed. This can help you diagnose where the pipeline is slowing down and optimize it accordingly. References:
* 1: Reshuffling your data
* 2: Monitoring pipeline performance using the Dataflow monitoring interface
* 3: Optimizing pipeline performance


NEW QUESTION # 348
Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

Answer: A,C,D

Explanation:
Cloud Dataproc provides out-of-the box and end-to-end support for many of the most popular job types, including Spark, Spark SQL, PySpark, MapReduce, Hive, and Pig jobs.


NEW QUESTION # 349
You are building a model to make clothing recommendations. You know a user's fashion preference is
likely to change over time, so you build a data pipeline to stream new data back to the model as it
becomes available. How should you use this data to train the model?

Answer: C


NEW QUESTION # 350
Which of these is NOT a way to customize the software on Dataproc cluster instances?

Answer: B

Explanation:
You can access the master node of the cluster by clicking the SSH button next to it in the Cloud Console.
You can easily use the --properties option of the dataproc command in the Google Cloud SDK to modify many common configuration files when creating a cluster. When creating a Cloud Dataproc cluster, you can specify initialization actions in executables and/or scripts that Cloud Dataproc will run on all nodes in your Cloud Dataproc cluster immediately after the cluster is set up. [https://cloud.google.com/dataproc/ docs/concepts/configuring-clusters/init-actions] Reference: https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/cluster-properties


NEW QUESTION # 351
Your company has recently grown rapidly and now ingesting data at a significantly higher rate than it was previously. You manage the daily batch MapReduce analytics jobs in Apache Hadoop. However, the recent increase in data has meant the batch jobs are falling behind. You were asked to recommend ways the development team could increase the responsiveness of the analytics without increasing costs. What should you recommend they do?

Answer: B

Explanation:
Spark performs in-memory processing and faster, which results in optimization of job's processing time.


NEW QUESTION # 352
......

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