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Professional-Data-Engineer Dumps - Google Professional Data Engineer Exam Practice Exam Questions

Google Professional-Data-Engineer - Google Professional Data Engineer Exam Braindumps

Google Professional-Data-Engineer - Google Cloud Certified Practice Exam

  • Certification Provider:Google
  • Exam Code:Professional-Data-Engineer
  • Exam Name:Google Professional Data Engineer Exam Exam
  • Total Questions:374 Questions and Answers
  • Updated on:Feb 2, 2025
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Professional-Data-Engineer Question and Answers

Question # 1

Your company’s on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for-like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage. You want to minimize the storage cost of the migration. What should you do?

Options:

A.  

Put the data into Google Cloud Storage.

B.  

Use preemptible virtual machines (VMs) for the Cloud Dataproc cluster.

C.  

Tune the Cloud Dataproc cluster so that there is just enough disk for all data.

D.  

Migrate some of the cold data into Google Cloud Storage, and keep only the hot data in Persistent Disk.

Discussion 0
Question # 2

You are designing a basket abandonment system for an ecommerce company. The system will send a message to a user based on these rules:

    No interaction by the user on the site for 1 hour

    Has added more than $30 worth of products to the basket

    Has not completed a transaction

You use Google Cloud Dataflow to process the data and decide if a message should be sent. How should you design the pipeline?

Options:

A.  

Use a fixed-time window with a duration of 60 minutes.

B.  

Use a sliding time window with a duration of 60 minutes.

C.  

Use a session window with a gap time duration of 60 minutes.

D.  

Use a global window with a time based trigger with a delay of 60 minutes.

Discussion 0
Question # 3

An external customer provides you with a daily dump of data from their database. The data flows into Google Cloud Storage GCS as comma-separated values (CSV) files. You want to analyze this data in Google BigQuery, but the data could have rows that are formatted incorrectly or corrupted. How should you build this pipeline?

Options:

A.  

Use federated data sources, and check data in the SQL query.

B.  

Enable BigQuery monitoring in Google Stackdriver and create an alert.

C.  

Import the data into BigQuery using the gcloud CLI and set max_bad_records to 0.

D.  

Run a Google Cloud Dataflow batch pipeline to import the data into BigQuery, and push errors to another dead-letter table for analysis.

Discussion 0

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Professional-Data-Engineer FAQs

The role of a Professional Data Engineer in Google Cloud involves designing, building, and maintaining data processing systems that are scalable, efficient, and reliable. They work with large datasets to extract insights, drive business decisions, and optimize data-driven processes.

A Professional Data Engineer makes data usable and valuable for others by designing data processing pipelines that transform raw data into meaningful insights. They ensure data quality, implement data governance practices, and create intuitive interfaces for accessing and analyzing data.

The key responsibilities of a Professional Data Engineer in data processing include designing and implementing data pipelines, integrating data from various sources, cleaning and transforming data, optimizing data storage and retrieval, and ensuring data security and compliance.

The exam assesses the ability to design data processing systems by testing candidates on their understanding of data processing concepts, their knowledge of Google Cloud Platform (GCP) data processing services, their proficiency in designing scalable and efficient data pipelines, and their ability to optimize data processing workflows for performance and cost efficiency.

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4 weeks ago

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