GOOGLE PROFESSIONAL-DATA-ENGINEER EXAM DUMPS [2025] TO ACHIEVE HIGHER RESULTS

Google Professional-Data-Engineer exam Dumps [2025] to Achieve Higher Results

Google Professional-Data-Engineer exam Dumps [2025] to Achieve Higher Results

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Google Certified Professional Data Engineer Exam Sample Questions (Q26-Q31):

NEW QUESTION # 26
You are designing the architecture of your application to store data in Cloud Storage. Your application consists of pipelines that read data from a Cloud Storage bucket that contains raw data, and write the data to a second bucket after processing. You want to design an architecture with Cloud Storage resources that are capable of being resilient if a Google Cloud regional failure occurs. You want to minimize the recovery point objective (RPO) if a failure occurs, with no impact on applications that use the stored data. What should you do?

  • A. Adopt a dual-region Cloud Storage bucket, and enable turbo replication in your architecture.
  • B. Adopt multi-regional Cloud Storage buckets in your architecture.
  • C. Adopt two regional Cloud Storage buckets, and create a daily task to copy from one bucket to the other.
  • D. Adopt two regional Cloud Storage buckets, and update your application to write the output on both buckets.

Answer: B

Explanation:
To ensure resilience and minimize the recovery point objective (RPO) with no impact on applications, using a dual-region bucket with turbo replication is the best approach. Here's why option D is the best choice:
* Dual-Region Buckets:
* Dual-region buckets store data redundantly across two distinct geographic regions, providing high availability and durability.
* This setup ensures that data remains available even if one region experiences a failure.
* Turbo Replication:
* Turbo replication ensures that data is replicated between the two regions within 15 minutes, aligning with the requirement to minimize the recovery point objective (RPO).
* This feature provides near real-time replication, significantly reducing the risk of data loss.
* No Impact on Applications:
* Applications continue to access the dual-region bucket without any changes, ensuring seamless operation even during a regional failure.
* The dual-region setup transparently handles failover, providing uninterrupted access to data.
Steps to Implement:
* Create a Dual-Region Bucket:
* Create a dual-region Cloud Storage bucket in the Google Cloud Console, selecting appropriate regions (e.g., us-central1 and us-east1).
* Enable Turbo Replication:
* Enable turbo replication to ensure rapid data replication between the selected regions.
* Configure Applications:
* Ensure that applications read and write to the dual-region bucket, benefiting from its high availability and durability.
* Test Failover:
* Simulate a regional failure to verify that the dual-region bucket and turbo replication meet the required RPO and ensure data resilience.
Reference Links:
* Google Cloud Storage Dual-Region
* Turbo Replication in Google Cloud Storage


NEW QUESTION # 27
You operate a logistics company, and you want to improve event delivery reliability for vehicle-based sensors.
You operate small data centers around the world to capture these events, but leased lines that provide connectivity from your event collection infrastructure to your event processing infrastructure are unreliable, with unpredictable latency. You want to address this issue in the most cost-effective way. What should you do?

  • A. Establish a Cloud Interconnect between all remote data centers and Google.
  • B. Write a Cloud Dataflow pipeline that aggregates all data in session windows.
  • C. Have the data acquisition devices publish data to Cloud Pub/Sub.
  • D. Deploy small Kafka clusters in your data centers to buffer events.

Answer: D


NEW QUESTION # 28
Your globally distributed auction application allows users to bid on items. Occasionally, users place identical bids at nearly identical times, and different application servers process those bids. Each bid event contains the item, amount, user, and timestamp. You want to collate those bid events into a single location in real time to determine which user bid first. What should you do?

  • A. Create a file on a shared file and have the application servers write all bid events to that file. Process the file with Apache Hadoop to identify which user bid first.
  • B. Have each application server write the bid events to Google Cloud Pub/Sub as they occur. Use a pull
  • C. Set up a MySQL database for each application server to write bid events into. Periodically query each of those distributed MySQL databases and update a master MySQL database with bid event information.
  • D. Have each application server write the bid events to Cloud Pub/Sub as they occur. Push the events from Cloud Pub/Sub to a custom endpoint that writes the bid event information into Cloud SQL.

Answer: C

Explanation:
subscription to pull the bid events using Google Cloud Dataflow. Give the bid for each item to the user in the bid event that is processed first.


NEW QUESTION # 29

  • A. Use hopping windows with a 15-mmute window, and a thirty-minute period.
  • B. Use tumbling windows with a 15-mmute window and a fifteen-minute. withAllowedLateness operator.
  • C. Use session windows with a 30-mmute gap duration.
  • D. You need to detect the average noise level from a sensor when data is received for a duration of more than 30 minutes, but the window ends when no data has been received for 15 minutes What should you do?
  • E. Use session windows with a 15-minute gap duration.

Answer: B

Explanation:
Session windows are dynamic windows that group elements based on the periods of activity. They are useful for streaming data that is irregularly distributed with respect to time. In this case, the noise level data from the sensors is only sent when it exceeds a certain threshold, and the duration of the noise events may vary. Therefore, session windows can capture the average noise level for each sensor during the periods of high noise, and end the window when there is no data for a specified gap duration. The gap duration should be 15 minutes, as the requirement is to end the window when no data has been received for 15 minutes. A 30-minute gap duration would be too long and may miss some noise events that are shorter than 30 minutes. Tumbling windows and hopping windows are fixed windows that group elements based on a fixed time interval. They are not suitable for this use case, as they may split or overlap the noise events from the sensors, and do not account for the periods of inactivity. Reference:
Windowing concepts
Session windows
Windowing in Dataflow


NEW QUESTION # 30
Case Study: 2 - MJTelco
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. Ensure each table is included in a dataset for a region.
  • B. Adjust the settings for each table to allow a related region-based security group view access.
  • C. Adjust the settings for each view to allow a related region-based security group view access.
  • D. Adjust the settings for each dataset to allow a related region-based security group view access.
  • E. Ensure all the tables are included in global dataset.

Answer: A,C


NEW QUESTION # 31
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