RELIABLE GOOGLE VALID PROFESSIONAL-DATA-ENGINEER TEST PATTERN | TRY FREE DEMO BEFORE PURCHASE

Reliable Google Valid Professional-Data-Engineer Test Pattern | Try Free Demo before Purchase

Reliable Google Valid Professional-Data-Engineer Test Pattern | Try Free Demo before Purchase

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Google Professional-Data-Engineer Certification is highly respected in the industry, and it can open up new career opportunities for individuals who hold it. Google Cloud Platform is one of the leading cloud computing platforms, and companies across different industries are increasingly adopting it. Professionals who are certified in Google Cloud Platform technologies are in high demand, and they can earn competitive salaries. Therefore, passing the Professional-Data-Engineer exam is a worthwhile investment for individuals who want to advance their careers in the field of data engineering.

The Google Professional-Data-Engineer exam consists of multiple-choice questions and is divided into four sections. The first section covers designing data processing systems, including the ability to design and implement data storage systems, data processing workflows, and data pipelines. The second section covers designing machine learning models, including the ability to design and implement machine learning algorithms, and to use machine learning to solve business problems. The third section covers designing data analysis tools, including the ability to design and implement data visualization tools, and to use data analysis to solve business problems. The fourth and final section covers designing applications and services, including the ability to design and implement cloud-based applications and services that meet the needs of businesses.

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Google Professional Data Engineer exam covers a wide range of topics, including the understanding of the Google Cloud Platform for storing, processing, and analyzing data, designing data processing systems, data modeling, data security, and compliance. Additionally, the exam tests the candidate's knowledge of implementing data pipelines, data transformation and processing, and machine learning models on the Google Cloud Platform. Passing Professional-Data-Engineer Exam demonstrates that the candidate has the skills and knowledge required to design and build data processing systems that meet business requirements and scale efficiently on the Google Cloud Platform.

Google Certified Professional Data Engineer Exam Sample Questions (Q297-Q302):

NEW QUESTION # 297
You are designing the architecture to process your data from Cloud Storage to BigQuery by using Dataflow.
The network team provided you with the Shared VPC network and subnetwork to be used by your pipelines.
You need to enable the deployment of the pipeline on the Shared VPC network. What should you do?

  • A. Assign the compute. networkUser role to the Dataflow service agent.
  • B. Assign the dataflow, admin role to the Dataflow service agent.
  • C. Assign the dataflow, admin role to the service account that executes the Dataflow pipeline.
  • D. Assign the compute.networkUser role to the service account that executes the Dataflow pipeline.

Answer: D

Explanation:
To use a Shared VPC network for a Dataflow pipeline, you need to specify the subnetwork parameter with the full URL of the subnetwork, and grant the service account that executes the pipeline the compute.networkUser role in the host project. This role allows the service account to use the subnetworks in the Shared VPC network. The Dataflow service agent does not need this role, as it only creates and manages the resources for the pipeline, but does not execute it. The dataflow.admin role is not related to the network access, but to the permissions to create and delete Dataflow jobs and resources. References:
* Specify a network and subnetwork | Cloud Dataflow | Google Cloud
* How to config dataflow Pipeline to use a Shared VPC?


NEW QUESTION # 298
You are planning to load some of your existing on-premises data into BigQuery on Google Cloud. You want to either stream or batch-load data, depending on your use case. Additionally, you want to mask some sensitive data before loading into BigQuery. You need to do this in a programmatic way while keeping costs to a minimum. What should you do?

  • A. Create your pipeline with Dataflow through the Apache Beam SDK for Python, customizing separate options within your code for streaming.
    batch processing, and Cloud DLP Select BigQuery as your data sink.
  • B. Use the BigQuery Data Transfer Service to schedule your migration. After the data is populated in BigQuery. use the connection to the Cloud Data Loss Prevention {Cloud DLP} API to de-identify the necessary data.
  • C. Set up Datastream to replicate your on-premise data on BigQuery.
  • D. Use Cloud Data Fusion to design your pipeline, use the Cloud DLP plug-in to de-identify data within your pipeline, and then move the data into BigQuery.

Answer: A

Explanation:
To load on-premises data into BigQuery while masking sensitive data, we need a solution that offers flexibility for both streaming and batch processing, as well as data masking capabilities. Here's a detailed explanation of why option B is the best choice:
Apache Beam and Dataflow:
Apache Beam SDK provides a unified programming model for both batch and stream data processing.
Google Cloud Dataflow is a fully managed service for executing Apache Beam pipelines, offering scalability and ease of use.
Customization for Different Use Cases:
By using the Apache Beam SDK, you can write custom pipelines that can handle both streaming and batch processing within the same framework.
This allows you to switch between streaming and batch modes based on your use case without changing the core logic of your data pipeline.
Data Masking with Cloud DLP:
Google Cloud Data Loss Prevention (DLP) API can be integrated into your Apache Beam pipeline to de-identify and mask sensitive data programmatically before loading it into BigQuery.
This ensures that sensitive data is handled securely and complies with privacy requirements.
Cost Efficiency:
Using Dataflow can be cost-effective because it is a fully managed service, reducing the operational overhead associated with managing your own infrastructure.
The pay-as-you-go model ensures you only pay for the resources you consume, which can help keep costs under control.
Implementation Steps:
Set up Apache Beam Pipeline:
Write a pipeline using the Apache Beam SDK for Python that reads data from your on-premises storage.
Add transformations for data processing, including the integration with Cloud DLP for data masking.
Configure Dataflow:
Deploy the Apache Beam pipeline on Google Cloud Dataflow.
Customize the pipeline options for both streaming and batch use cases.
Load Data into BigQuery:
Set BigQuery as the sink for your data in the Apache Beam pipeline.
Ensure the processed and masked data is loaded into the appropriate BigQuery tables.
Reference:
Apache Beam Documentation
Google Cloud Dataflow Documentation
Google Cloud DLP Documentation
BigQuery Documentation


NEW QUESTION # 299
You have one BigQuery dataset which includes customers' street addresses. You want to retrieve all occurrences of street addresses from the dataset. What should you do?

  • A. Write a SQL query in BigQuery by using REGEXP_CONTAINS on all tables in your dataset to find rows where the word "street" appears.
  • B. Create a deep inspection job on each table in your dataset with Cloud Data Loss Prevention and create an inspection template that includes the STREET_ADDRESS infoType.
  • C. Create a de-identification job in Cloud Data Loss Prevention and use the masking transformation.
  • D. Create a discovery scan configuration on your organization with Cloud Data Loss Prevention and create an inspection template that

Answer: B

Explanation:
includes the STREET_ADDRESS infoType.
Explanation:
To retrieve all occurrences of street addresses from a BigQuery dataset, the most effective and comprehensive method is to use Cloud Data Loss Prevention (DLP). Here's why option A is the best choice:
Cloud Data Loss Prevention (DLP):
Cloud DLP is designed to discover, classify, and protect sensitive information. It includes pre-defined infoTypes for various kinds of sensitive data, including street addresses.
Using Cloud DLP ensures thorough and accurate detection of street addresses based on advanced pattern recognition and contextual analysis.
Deep Inspection Job:
A deep inspection job allows you to scan entire tables for sensitive information.
By creating an inspection template that includes the STREET_ADDRESS infoType, you can ensure that all instances of street addresses are detected across your dataset.
Scalability and Accuracy:
Cloud DLP is scalable and can handle large datasets efficiently.
It provides a high level of accuracy in identifying sensitive data, reducing the risk of missing any occurrences.
Steps to Implement:
Set Up Cloud DLP:
Enable the Cloud DLP API in your Google Cloud project.
Create an Inspection Template:
Create an inspection template in Cloud DLP that includes the STREET_ADDRESS infoType.
Run Deep Inspection Jobs:
Create and run a deep inspection job for each table in your dataset using the inspection template.
Review the inspection job results to retrieve all occurrences of street addresses.
Reference:
Cloud DLP Documentation
Creating Inspection Jobs
Topic 2, MJTelco Case Study
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.
Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.
Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.
MJTelco will also use three separate operating environments - development/test, staging, and production - to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community.
Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
Provide reliable and timely access to data for analysis from distributed research workers Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.
Technical Requirements
Ensure secure and efficient transport and storage of telemetry data
Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately 100m records/day Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis. Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.


NEW QUESTION # 300
MJTelco Case Study
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.
Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
Scale and harden their PoC to support significantly more data flows generated when they ramp to more

than 50,000 installations.
Refine their machine-learning cycles to verify and improve the dynamic models they use to control

topology definition.
MJTelco will also use three separate operating environments - development/test, staging, and production
- to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
Scale up their production environment with minimal cost, instantiating resources when and where

needed in an unpredictable, distributed telecom user community.
Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.

Provide reliable and timely access to data for analysis from distributed research workers

Maintain isolated environments that support rapid iteration of their machine-learning models without

affecting their customers.
Technical Requirements
Ensure secure and efficient transport and storage of telemetry data

Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows

each.
Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately

100m records/day
Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems

both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis.
Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.
You create a new report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. It is company policy to ensure employees can view only the data associated with their region, so you create and populate a table for each region. You need to enforce the regional access policy to the data.
Which two actions should you take? (Choose two.)

  • A. Adjust the settings for each dataset to allow a related region-based security group view access.
  • B. Adjust the settings for each table to allow a related region-based security group view access.
  • C. Ensure each table is included in a dataset for a region.
  • D. Ensure all the tables are included in global dataset.
  • E. Adjust the settings for each view to allow a related region-based security group view access.

Answer: C,E


NEW QUESTION # 301
You are a head of BI at a large enterprise company with multiple business units that each have different priorities and budgets. You use on-demand pricing for BigQuery with a quota of 2K concurrent on-demand slots per project. Users at your organization sometimes don't get slots to execute their query and you need to correct this. You'd like to avoid introducing new projects to your account.
What should you do?

  • A. Switch to flat-rate pricing and establish a hierarchical priority model for your projects.
  • B. Convert your batch BQ queries into interactive BQ queries.
  • C. Increase the amount of concurrent slots per project at the Quotas page at the Cloud Console.
  • D. Create an additional project to overcome the 2K on-demand per-project quota.

Answer: A

Explanation:
Explanation/Reference:
Reference https://cloud.google.com/blog/products/gcp/busting-12-myths-about-bigquery


NEW QUESTION # 302
......

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