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2025 Professional-Data-Engineer–100% Free Test Practice | Professional-Data-Engineer Test Papers

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Professionals who pass the Google Professional-Data-Engineer: Google Certified Professional Data Engineer Exam are considered to be highly skilled data engineers who can solve complex data problems. They possess the skills to design, implement, and manage large-scale data processing systems and are capable of analyzing and interpreting data to make informed business decisions. Moreover, they have an in-depth understanding of cloud-based data processing systems and can leverage them to achieve business objectives.

Google Professional-Data-Engineer certification is a highly respected and sought-after credential in the field of data engineering. Google Certified Professional Data Engineer Exam certification is offered by Google Cloud and is designed for professionals who are skilled in designing and building data processing systems on the Google Cloud Platform. Professional-Data-Engineer Exam Tests candidates' knowledge of data engineering principles, including data collection, transformation, storage, and analysis.

Google Professional-Data-Engineer exam is one of the most prestigious and sought-after certifications in the field of data engineering. It is designed to test the knowledge, skills, and expertise of professionals in designing, building, and maintaining data processing systems on the Google Cloud Platform. Professional-Data-Engineer exam is intended for those who have experience in data engineering and are looking to validate their skills and knowledge in this field.

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Google Certified Professional Data Engineer Exam Sample Questions (Q237-Q242):

NEW QUESTION # 237
You are implementing security best practices on your data pipeline. Currently, you are manually executing jobs as the Project Owner. You want to automate these jobs by taking nightly batch files containing non- public information from Google Cloud Storage, processing them with a Spark Scala job on a Google Cloud Dataproc cluster, and depositing the results into Google BigQuery.
How should you securely run this workload?

A. Use a user account with the Project Viewer role on the Cloud Dataproc cluster to read the batch files and write to BigQuery
B. Restrict the Google Cloud Storage bucket so only you can see the files
C. Use a service account with the ability to read the batch files and to write to BigQuery
D. Grant the Project Owner role to a service account, and run the job with it

Answer: C

NEW QUESTION # 238
Your company is migrating their 30-node Apache Hadoop cluster to the cloud. They want to re-use Hadoop jobs they have already created and minimize the management of the cluster as much as possible.
They also want to be able to persist data beyond the life of the cluster. What should you do?

A. Create a Google Cloud Dataproc cluster that uses persistent disks for HDFS.
B. Create a Google Cloud Dataflow job to process the data.
C. Create a Cloud Dataproc cluster that uses the Google Cloud Storage connector.
D. Create a Hadoop cluster on Google Compute Engine that uses persistent disks.
E. Create a Hadoop cluster on Google Compute Engine that uses Local SSD disks.

Answer: B

NEW QUESTION # 239
Which of these statements about BigQuery caching is true?

A. By default, a query's results are not cached.
B. There is no charge for a query that retrieves its results from cache.
C. BigQuery caches query results for 48 hours.
D. Query results are cached even if you specify a destination table.

Answer: B

Explanation:
When query results are retrieved from a cached results table, you are not charged for the query.
BigQuery caches query results for 24 hours, not 48 hours.
Query results are not cached if you specify a destination table.
A query's results are always cached except under certain conditions, such as if you specify a destination table.

NEW QUESTION # 240
You decided to use Cloud Datastore to ingest vehicle telemetry data in real time. You want to build a storage system that will account for the long-term data growth, while keeping the costs low. You also want to create snapshots of the data periodically, so that you can make a point-in-time (PIT) recovery, or clone a copy of the data for Cloud Datastore in a different environment. You want to archive these snapshots for a long time. Which two methods can accomplish this? (Choose two.)

A. Use managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class.
B. Write an application that uses Cloud Datastore client libraries to read all the entities. Format the exported data into a JSON file. Apply compression before storing the data in Cloud Source Repositories.
C. Use managed export, and then import the data into a BigQuery table created just for that export, and delete temporary export files.
D. Use managed export, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export.
E. Write an application that uses Cloud Datastore client libraries to read all the entities. Treat each entity as a BigQuery table row via BigQuery streaming insert. Assign an export timestamp for each export, and attach it as an extra column for each row. Make sure that the BigQuery table is partitioned using the export timestamp column.

Answer: A,D

Explanation:
https://cloud.google.com/datastore/docs/export-import-entities

NEW QUESTION # 241
You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

A. Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
B. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
C. Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
D. Load the data every 30 minutes into a new partitioned table in BigQuery.

Answer: A

Explanation:
Explanation

NEW QUESTION # 242
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