GOOGLE - FANTASTIC PDF PROFESSIONAL-DATA-ENGINEER BRAINDUMPS

Google - Fantastic Pdf Professional-Data-Engineer Braindumps

Google - Fantastic Pdf Professional-Data-Engineer Braindumps

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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.

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Valid Professional-Data-Engineer Vce, Professional-Data-Engineer Reliable Exam Blueprint

With the development of the times, the pace of the society is getting faster and faster. If we don't try to improve our value, we're likely to be eliminated by society. Under the circumstances, we must find ways to prove our abilities. For example, getting the Professional-Data-Engineer Certification is a good way. If we had it, the chances of getting a good job would be greatly improved. However, obtaining the Professional-Data-Engineer certification is not an easy task.

Google Certified Professional Data Engineer Exam Sample Questions (Q28-Q33):

NEW QUESTION # 28
You are designing a cloud-native historical data processing system to meet the following conditions:
* The data being analyzed is in CSV, Avro, and PDF formats and will be accessed by multiple analysis tools including Cloud Dataproc, BigQuery, and Compute Engine.
* A streaming data pipeline stores new data daily.
* Peformance is not a factor in the solution.
* The solution design should maximize availability.
How should you design data storage for this solution?

  • A. Store the data in a regional Cloud Storage bucket. Access the bucket directly using Cloud Dataproc, BigQuery, and Compute Engine.
  • B. Store the data in BigQuery. Access the data using the BigQuery Connector on Cloud Dataproc and Compute Engine.
  • C. Create a Cloud Dataproc cluster with high availability. Store the data in HDFS, and peform analysis as needed.
  • D. Store the data in a multi-regional Cloud Storage bucket. Access the data directly using Cloud Dataproc, BigQuery, and Compute Engine.

Answer: A


NEW QUESTION # 29
You create an important report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. You notice that visualizations are not showing data that is less than 1 hour old. What should you do?

  • A. Disable caching by editing the report settings.
  • B. Disable caching in BigQuery by editing table details.
  • C. Clear your browser history for the past hour then reload the tab showing the virtualizations.
  • D. Refresh your browser tab showing the visualizations.

Answer: A


NEW QUESTION # 30
Your company needs to upload their historic data to Cloud Storage. The security rules don't allow access from external IPs to their on-premises resources. After an initial upload, they will add new data from existing on-premises applications every day. What should they do?

  • A. Use Cloud Dataflow and write the data to Cloud Storage.
  • B. Write a job template in Cloud Dataproc to perform the data transfer.
  • C. Install an FTP server on a Compute Engine VM to receive the files and move them to Cloud Storage.
  • D. Execute gsutil rsync from the on-premises servers.

Answer: D


NEW QUESTION # 31
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.
MJTelco is building a custom interface to share data. They have these requirements:
1. They need to do aggregations over their petabyte-scale datasets.
2. They need to scan specific time range rows with a very fast response time (milliseconds).
Which combination of Google Cloud Platform products should you recommend?

  • A. Cloud Bigtable and Cloud SQL
  • B. BigQuery and Cloud Storage
  • C. BigQuery and Cloud Bigtable
  • D. Cloud Datastore and Cloud Bigtable

Answer: C


NEW QUESTION # 32
You use BigQuery as your centralized analytics platform. New data is loaded every day, and an ETL pipeline modifies the original data and prepares it for the final users. This ETL pipeline is regularly modified and can generate errors, but sometimes the errors are detected only after 2 weeks. You need to provide a method to recover from these errors, and your backups should be optimized for storage costs. How should you organize your data in BigQuery and store your backups?

  • A. Organize your data in separate tables for each month, and use snapshot decorators to restore the table to a time prior to the corruption.
  • B. Organize your data in separate tables for each month, and duplicate your data on a separate dataset in BigQuery.
  • C. Organize your data in a single table, export, and compress and store the BigQuery data in Cloud Storage.
  • D. Organize your data in separate tables for each month, and export, compress, and store the data in Cloud Storage.

Answer: A

Explanation:
Explanation


NEW QUESTION # 33
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