PROFESSIONAL-DATA-ENGINEER TEST COLLECTION PDF - PROFESSIONAL-DATA-ENGINEER RELIABLE EXAM PAPERS

Professional-Data-Engineer Test Collection Pdf - Professional-Data-Engineer Reliable Exam Papers

Professional-Data-Engineer Test Collection Pdf - Professional-Data-Engineer Reliable Exam Papers

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Google Professional-Data-Engineer Certification Exam is highly respected in the industry and is a valuable asset for professionals who want to advance their careers in big data. Holding this certification demonstrates that a candidate has the skills and knowledge needed to design and build data processing systems on the Google Cloud Platform. It is also a testament to a candidate's dedication to advancing their skills and staying up-to-date with the latest technologies in the field.

The Google Professional-Data-Engineer exam covers a wide range of topics, including data processing systems design, data modeling, data ingestion, data transformation, data storage, data analysis, and machine learning. You will be tested on your ability to design and implement data processing systems using Google Cloud technologies such as BigQuery, Cloud Dataflow, Cloud Dataproc, and Cloud Pub/Sub. Professional-Data-Engineer Exam also covers best practices for data security and compliance, as well as troubleshooting and optimization techniques. Passing Professional-Data-Engineer exam requires a strong understanding of cloud computing principles and a solid grasp of data engineering concepts, making it a challenging but rewarding certification to earn.

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Google Professional-Data-Engineer certification exam is designed to validate the skills and knowledge of individuals working in the field of data engineering. Google Certified Professional Data Engineer Exam certification is intended for those professionals who have expertise in designing, building, and maintaining data processing systems using Google Cloud Platform services. Professional-Data-Engineer Exam evaluates the candidates' ability to design, implement, and manage data processing systems, as well as their understanding of data analysis and machine learning concepts.

Google Certified Professional Data Engineer Exam Sample Questions (Q272-Q277):

NEW QUESTION # 272
Your chemical company needs to manually check documentation for customer order. You use a pull subscription in Pub/Sub so that sales agents get details from the order. You must ensure that you do not process orders twice with different sales agents and that you do not add more complexity to this workflow.
What should you do?

  • A. Use Pub/Sub exactly-once delivery in your pull subscription.
  • B. Create a transactional database that monitors the pending messages.
  • C. Use a Deduphcate PTransform in Dataflow before sending the messages to the sales agents.
  • D. Create a new Pub/Sub push subscription to monitor the orders processed in the agent's system.

Answer: A

Explanation:
Pub/Sub exactly-once delivery is a feature that guarantees that subscriptions do not receive duplicate deliveries of messages based on a Pub/Sub-defined unique message ID. This feature is only supported by the pull subscription type, which is what you are using in this scenario. By enabling exactly-once delivery, you can ensure that each order is processed only once by a sales agent, and that no order is lost or duplicated. This also simplifies your workflow, as you do not need to create a separate database or subscription to monitor the pending or processed messages. References:
* Exactly-once delivery | Cloud Pub/Sub Documentation
* Cloud Pub/Sub Exactly-once Delivery feature is now Generally Available (GA)


NEW QUESTION # 273
Which of the following is not true about Dataflow pipelines?

  • A. Pipelines represent a directed graph of steps
  • B. Pipelines represent a data processing job
  • C. Pipelines can share data between instances
  • D. Pipelines are a set of operations

Answer: C

Explanation:
Explanation
The data and transforms in a pipeline are unique to, and owned by, that pipeline. While your program can create multiple pipelines, pipelines cannot share data or transforms Reference: https://cloud.google.com/dataflow/model/pipelines


NEW QUESTION # 274
You have an upstream process that writes data to Cloud Storage. This data is then read by an Apache Spark job that runs on Dataproc. These jobs are run in the us-central1 region, but the data could be stored anywhere in the United States. You need to have a recovery process in place in case of a catastrophic single region failure. You need an approach with a maximum of 15 minutes of data loss (RPO=15 mins). You want to ensure that there is minimal latency when reading the dat a. What should you do?

  • A. 1. Create a dual-region Cloud Storage bucket in the us-central1 and us-south1 regions.
    2. Enable turbo replication.
    3. Run the Dataproc cluster in a zone in the us-central1 region, reading from the bucket in the us-south1 region.
    4. In case of a regional failure, redeploy your Dataproc duster to the us-south1 region and continue reading from the same bucket.
  • B. 1. Create a Cloud Storage bucket in the US multi-region.
    2. Run the Dataproc cluster in a zone in the ua-central1 region, reading data from the US multi-region bucket.
    3. In case of a regional failure, redeploy the Dataproc cluster to the us-central2 region and continue reading from the same bucket.
  • C. 1. Create two regional Cloud Storage buckets, one in the us-central1 region and one in the us-south1 region.
    2. Have the upstream process write data to the us-central1 bucket. Use the Storage Transfer Service to copy data hourly from the us-central1 bucket to the us-south1 bucket.
    3. Run the Dataproc cluster in a zone in the us-central1 region, reading from the bucket in that region.
    4. In case of regional failure, redeploy your Dataproc clusters to the us-south1 region and read from the bucket in that region instead.
  • D. 1. Create a dual-region Cloud Storage bucket in the us-central1 and us-south1 regions.
    2. Enable turbo replication.
    3. Run the Dataproc cluster in a zone in the us-central1 region, reading from the bucket in the same region.
    4. In case of a regional failure, redeploy the Dataproc clusters to the us-south1 region and read from the same bucket.

Answer: D

Explanation:
To ensure data recovery with minimal data loss and low latency in case of a single region failure, the best approach is to use a dual-region bucket with turbo replication. Here's why option B is the best choice:
Dual-Region Bucket:
A dual-region bucket provides geo-redundancy by replicating data across two regions, ensuring high availability and resilience against regional failures.
The chosen regions (us-central1 and us-south1) provide geographic diversity within the United States.
Turbo Replication:
Turbo replication ensures that data is replicated between the two regions within 15 minutes, meeting the Recovery Point Objective (RPO) of 15 minutes.
This minimizes data loss in case of a regional failure.
Running Dataproc Cluster:
Running the Dataproc cluster in the same region as the primary data storage (us-central1) ensures minimal latency for normal operations.
In case of a regional failure, redeploying the Dataproc cluster to the secondary region (us-south1) ensures continuity with minimal data loss.
Steps to Implement:
Create a Dual-Region Bucket:
Set up a dual-region bucket in the Google Cloud Console, selecting us-central1 and us-south1 regions.
Enable turbo replication to ensure rapid data replication between the regions.
Deploy Dataproc Cluster:
Deploy the Dataproc cluster in the us-central1 region to read data from the bucket located in the same region for optimal performance.
Set Up Failover Plan:
Plan for redeployment of the Dataproc cluster to the us-south1 region in case of a failure in the us-central1 region.
Ensure that the failover process is well-documented and tested to minimize downtime and data loss.
Reference:
Google Cloud Storage Dual-Region
Turbo Replication in Google Cloud Storage
Dataproc Documentation


NEW QUESTION # 275
You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component.
Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?

  • A. Use Cloud Vision AutoML, but reduce your dataset twice.
  • B. Train your own image recognition model leveraging transfer learning techniques.
  • C. Use Cloud Vision API by providing custom labels as recognition hints.
  • D. Use Cloud Vision AutoML with the existing dataset.

Answer: D


NEW QUESTION # 276
Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure?

  • A. Reduce the query frequency to once every hour until the database comes back online.
  • B. Retry the query every second until it comes back online to minimize staleness of data.
  • C. Issue a command to restart the database servers.
  • D. Retry the query with exponential backoff, up to a cap of 15 minutes.

Answer: D

Explanation:
Explanation
https://cloud.google.com/sql/docs/mysql/manage-connections#backoff


NEW QUESTION # 277
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