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Google Professional-Data-Engineer Certification is aimed at data engineers who have experience working with large-scale data processing systems. Candidates who pass the exam will have demonstrated their ability to design and implement highly scalable data processing systems on the Google Cloud Platform. Google Certified Professional Data Engineer Exam certification is highly regarded by employers in the industry and is a valuable asset for data engineers looking to advance their careers.

Google Professional-Data-Engineer certification exam is designed for individuals who want to demonstrate their expertise in designing and building data processing systems on the Google Cloud Platform. As a Google Certified Professional Data Engineer, you will be recognized as an expert in the field and have the skills to create scalable and efficient data processing systems that leverage Google Cloud technologies. Google Certified Professional Data Engineer Exam certification is ideal for data engineers, data architects, solutions architects, and anyone who wants to validate their skills in building and managing data processing systems in the cloud.

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Google Certified Professional Data Engineer Exam Sample Questions (Q321-Q326):

NEW QUESTION # 321
You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?

A. Cloud SQL
B. Cloud Datastore
C. Cloud Bigtable
D. BigQuery

Answer: A

NEW QUESTION # 322
What are the minimum permissions needed for a service account used with Google Dataproc?

A. Execute to Google Cloud Storage; execute to Google Cloud Logging
B. Write to Google Cloud Storage; read to Google Cloud Logging
C. Read and write to Google Cloud Storage; write to Google Cloud Logging
D. Execute to Google Cloud Storage; write to Google Cloud Logging

Answer: C

Explanation:
Explanation
Service accounts authenticate applications running on your virtual machine instances to other Google Cloud Platform services. For example, if you write an application that reads and writes files on Google Cloud Storage, it must first authenticate to the Google Cloud Storage API. At a minimum, service accounts used with Cloud Dataproc need permissions to read and write to Google Cloud Storage, and to write to Google Cloud Logging.
Reference: https://cloud.google.com/dataproc/docs/concepts/service-accounts#important_notes

NEW QUESTION # 323
You're training a model to predict housing prices based on an available dataset with real estate properties.
Your plan is to train a fully connected neural net, and you've discovered that the dataset contains latitude and longitude of the property. Real estate professionals have told you that the location of the property is highly influential on price, so you'd like to engineer a feature that incorporates this physical dependency.
What should you do?

A. Create a feature cross of latitude and longitude, bucketize at the minute level and use L1 regularization during optimization.
B. Create a numeric column from a feature cross of latitude and longitude.
C. Provide latitude and longitude as input vectors to your neural net.
D. Create a feature cross of latitude and longitude, bucketize it at the minute level and use L2 regularization during optimization.

Answer: B

Explanation:
Explanation/Reference:
Reference https://cloud.google.com/bigquery/docs/gis-data

NEW QUESTION # 324
Your organization is modernizing their IT services and migrating to Google Cloud. You need to organize the data that will be stored in Cloud Storage and BigQuery. You need to enable a data mesh approach to share the data between sales, product design, and marketing departments What should you do?

A. 1 Create multiple projects for storage of the data for each of your departments' applications.2 Enable each department to create Cloud Storage buckets and BigQuery datasets.3 In Dataplex, map each department to a data lake and the Cloud Storage buckets, and map the BigQuery datasets to zones.4 Enable each department to own and share the data of their data lakes.
B. 1Create a project for storage of the data for each of your departments.2 Enable each department to create Cloud Storage buckets and BigQuery datasets.3. Create user groups for authorized readers for each bucket and dataset.4 Enable the IT team to administer the user groups to add or remove users as the departments' request.
C. 1Create a project for storage of the data for your organization.2 Create a central Cloud Storage bucket with three folders to store the files for each department.3. Create a central BigQuery dataset with tables prefixed with the department name.4 Give viewer rights for the storage project for the users of your departments.
D. 1 Create multiple projects for storage of the data for each of your departments' applications.2 Enable each department to create Cloud Storage buckets and BigQuery datasets.3. Publish the data that each department shared in Analytics Hub.4 Enable all departments to discover and subscribe to the data they need in Analytics Hub.

Answer: D

Explanation:
Implementing a data mesh approach involves treating data as a product and enabling decentralized data ownership and architecture. The steps outlined in option C support this approach by creating separate projects for each department, which aligns with the principle of domain-oriented decentralized data ownership. By allowing departments to create their own Cloud Storage buckets and BigQuery datasets, it promotes autonomy and self-service. Publishing the data in Analytics Hub facilitates data sharing and discovery across departments, enabling a collaborative environment where data can be easily accessed and utilized by different parts of the organization.
References:
Architecture and functions in a data mesh - Google Cloud
Professional Data Engineer Certification Exam Guide | Learn - Google Cloud Build a Data Mesh with Dataplex | Google Cloud Skills Boost

NEW QUESTION # 325
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. 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.
B. Use managed export, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export.
C. 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.
D. Use managed export, and then import the data into a BigQuery table created just for that export, and delete temporary export files.
E. Use managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class.

Answer: B,E

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

NEW QUESTION # 326
......

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