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Google Professional-Data-Engineer: Google Certified Professional Data Engineer Exam is an essential certification exam for professionals looking to advance their careers in the field of data engineering. Passing Professional-Data-Engineer exam validates a candidate's expertise in designing, building, and managing data processing systems. It also demonstrates their ability to analyze and interpret data, make informed business decisions, and leverage cloud-based data processing systems to achieve business objectives.

The Google Professional-Data-Engineer Exam is designed for professionals who have experience working with data processing systems in a business environment. Candidates should have a thorough understanding of data modeling, data warehousing, and data transformation. Additionally, they should have experience working with cloud-based data storage and processing systems such as Google Cloud Platform, AWS, or Microsoft Azure.

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Google Certified Professional Data Engineer Exam Sample Questions (Q224-Q229):

NEW QUESTION # 224
An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

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

Answer: C

Explanation:
Explanation/Reference: https://cloud.google.com/solutions/business-intelligence/

NEW QUESTION # 225
As your organization expands its usage of GCP, many teams have started to create their own projects.
Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects.
Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies.
Which two steps should you take? Choose 2 answers.

A. Use Cloud Deployment Manager to automate access provision.
B. Introduce resource hierarchy to leverage access control policy inheritance.
C. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
D. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
E. For each Cloud Storage bucket or BigQuery dataset, decide which projects need access. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.

Answer: A,D

NEW QUESTION # 226
You are deploying a new storage system for your mobile application, which is a media streaming service. You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity 'Movie'the property 'actors'and the property 'tags' have multiple values but the property 'date released' does not. A typical query would ask for all movies with actor=<actorname>ordered by date_releasedor all movies with tag=Comedyordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?

A. Manually configure the index in your index config as follows:
B. Set the following in your entity options: exclude_from_indexes = 'actors, tags'
C. Set the following in your entity options: exclude_from_indexes = 'date_published'
D. Manually configure the index in your index config as follows:

Answer: A

NEW QUESTION # 227
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. Use managed export, and then import the data into a BigQuery table created just for that export, and delete temporary export files.
C. Use managed export, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export.
D. 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.
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,C

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

NEW QUESTION # 228
You are configuring networking for a Dataflow job. The data pipeline uses custom container images with the libraries that are required for the transformation logic preinstalled. The data pipeline reads the data from Cloud Storage and writes the data to BigQuery. You need to ensure cost-effective and secure communication between the pipeline and Google APIs and services.
What should you do?

A. Leave external IP addresses assigned to worker VMs while enforcing firewall rules.
B. Disable external IP addresses and establish a Private Service Connect endpoint IP address.
C. Disable external IP addresses from worker VMs and enable Private Google Access.
D. Enable Cloud NAT to provide outbound internet connectivity while enforcing firewall rules.

Answer: C

Explanation:
Private Google Access allows VMs without external IP addresses to communicate with Google APIs and services over internal routes. This reduces the cost and increases the security of the data pipeline. Custom container images can be stored in Container Registry, which supports Private Google Access. Dataflow supports Private Google Access for both batch and streaming jobs.

NEW QUESTION # 229
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