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Google Professional-Data-Engineer exam is a certification exam offered by Google for professionals who work with data engineering. Professional-Data-Engineer exam is designed to test the individual's knowledge and skills in using Google Cloud Platform tools and services for data engineering. Professional-Data-Engineer Exam is intended to validate the candidate's ability to design, build, operationalize, and secure data processing systems using Google Cloud Platform technologies.

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Google Professional-Data-Engineer Exam Syllabus Topics:

TopicDetails
Topic 1
  • Storing the data: This topic explains how to select storage systems and how to plan using a data warehouse. Additionally, it discusses how to design for a data mesh.
Topic 2
  • Maintaining and automating data workloads: It discusses optimizing resources, automation and repeatability design, and organization of workloads as per business requirements. Lastly, the topic explains monitoring and troubleshooting processes and maintaining awareness of failures.
Topic 3
  • Ingesting and processing the data: The topic discusses planning of the data pipelines, building the pipelines, acquisition and import of data, and deploying and operationalizing the pipelines.
Topic 4
  • Preparing and using data for analysis: Questions about data for visualization, data sharing, and assessment of data may appear.
Topic 5
  • Designing data processing systems: It delves into designing for security and compliance, reliability and fidelity, flexibility and portability, and data migrations.

Google Professional-Data-Engineer: Google Certified Professional Data Engineer Exam is a highly-revered certification exam that is designed to test individuals' ability to design, build, and manage data processing systems. Professionals who Pass Professional-Data-Engineer Exam are recognized as experts in the field of data engineering and are highly sought after by leading tech companies worldwide. Professional-Data-Engineer exam is intended for individuals who have a deep understanding of data processing systems and possess the skills to design and manage them.

Google Certified Professional Data Engineer Exam Sample Questions (Q368-Q373):

NEW QUESTION # 368
Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by
10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.
You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (Choose two.)

Answer: D,E

Explanation:
Explanation/Reference:


NEW QUESTION # 369
The Development and External teams nave the project viewer Identity and Access Management (1AM) role m a folder named Visualization. You want the Development Team to be able to read data from both Cloud Storage and BigQuery, but the External Team should only be able to read data from BigQuery. What should you do?

Answer: B


NEW QUESTION # 370
Your financial services company is moving to cloud technology and wants to store 50 TB of financial time- series data in the cloud. This data is updated frequently and new data will be streaming in all the time. Your company also wants to move their existing Apache Hadoop jobs to the cloud to get insights into this data.
Which product should they use to store the data?

Answer: C

Explanation:
Explanation/Reference: https://cloud.google.com/bigtable/docs/schema-design-time-series


NEW QUESTION # 371
If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?

Answer: D

Explanation:
If you're running a performance test that depends upon Cloud Bigtable, be sure to follow these steps as you plan and execute your test:
Use a production instance. A development instance will not give you an accurate sense of how a production instance performs under load.
Use at least 300 GB of data. Cloud Bigtable performs best with 1 TB or more of data.
However, 300 GB of data is enough to provide reasonable results in a performance test on a 3-node cluster. On larger clusters, use 100 GB of data per node.
Before you test, run a heavy pre-test for several minutes. This step gives Cloud Bigtable a chance to balance data across your nodes based on the access patterns it observes.
Run your test for at least 10 minutes. This step lets Cloud Bigtable further optimize your data, and it helps ensure that you will test reads from disk as well as cached reads from memory.
Reference: https://cloud.google.com/bigtable/docs/performance


NEW QUESTION # 372
Cloud Bigtable is Google's ______ Big Data database service.

Answer: B

Explanation:
Cloud Bigtable is Google's NoSQL Big Data database service. It is the same database that Google uses for services, such as Search, Analytics, Maps, and Gmail. It is used for requirements that are low latency and high throughput including Internet of Things (IoT), user analytics, and financial data analysis.
Reference: https://cloud.google.com/bigtable/


NEW QUESTION # 373
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