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Google Certified Professional Data Engineer Exam Sample Questions (Q52-Q57):

NEW QUESTION # 52
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 dat a. 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 update your application to write the output on both buckets.
D. Adopt two regional Cloud Storage buckets, and create a daily task to copy from one bucket to the other.

Answer: A

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:
Google Cloud Storage Dual-Region
Turbo Replication in Google Cloud Storage

NEW QUESTION # 53
You have thousands of Apache Spark jobs running in your on-premises Apache Hadoop cluster. You want to migrate the jobs to Google Cloud. You want to use managed services to run your jobs instead of maintaining a long-lived Hadoop cluster yourself. You have a tight timeline and want to keep code changes to a minimum. What should you do?

A. Move your data to Cloud Storage. Run your jobs on Dataproc.
B. Move your data to BigQuery. Convert your Spark scripts to a SQL-based processing approach.
C. Copy your data to Compute Engine disks. Manage and run your jobs directly on those instances.
D. Rewrite your jobs in Apache Beam. Run your jobs in Dataflow.

Answer: A

Explanation:
Dataproc's Compatibility with Apache Spark: Dataproc is a managed service for running Hadoop and Spark clusters on Google Cloud. This means it is designed to seamlessly run Apache Spark jobs with minimal code changes. Your existing Spark jobs should run on Dataproc with little to no modification.
Cloud Storage as a Scalable Data Lake: Cloud Storage provides a highly scalable and durable storage solution for your data. It's designed to handle large volumes of data that Spark jobs typically process.
Minimizing Operational Overhead: By using Dataproc, you eliminate the need to manage and maintain a Hadoop cluster yourself. Google Cloud handles the infrastructure, allowing you to focus on your data processing tasks.
Tight Timeline and Minimal Code Changes: This option directly addresses the requirements of the question. It offers a quick and easy way to migrate your Spark jobs to Google Cloud with minimal disruption to your existing codebase.
Why other options are not suitable:
A . Copy your data to Compute Engine disks. Manage and run your jobs directly on those instances: This option requires you to manage the underlying infrastructure yourself, which contradicts the requirement of using managed services.
C . Move your data to BigQuery. Convert your Spark scripts to a SQL-based processing approach: While BigQuery is a powerful data warehouse, converting Spark scripts to SQL would require substantial code changes and might not be feasible within a tight timeline.
D . Rewrite your jobs in Apache Beam. Run your jobs in Dataflow: Rewriting jobs in Apache Beam would be a significant undertaking and not suitable for a quick migration with minimal code changes.

NEW QUESTION # 54
Which of the following are feature engineering techniques? (Select 2 answers)

A. Crossed feature columns
B. Bucketization of a continuous feature
C. Hidden feature layers
D. Feature prioritization

Answer: A,B

Explanation:
Selecting and crafting the right set of feature columns is key to learning an effective model.
Bucketization is a process of dividing the entire range of a continuous feature into a set of consecutive bins/buckets, and then converting the original numerical feature into a bucket ID (as a categorical feature) depending on which bucket that value falls into.
Using each base feature column separately may not be enough to explain the data. To learn the differences between different feature combinations, we can add crossed feature columns to the model.
Reference:
https://www.tensorflow.org/tutorials/wide#selecting_and_engineering_features_for_the_mo del

NEW QUESTION # 55
You are designing a basket abandonment system for an ecommerce company. The system will send a message to a user based on these rules:
No interaction by the user on the site for 1 hour

Has added more than $30 worth of products to the basket Has not completed a

transaction
You use Google Cloud Dataflow to process the data and decide if a message should be sent. How should you design the pipeline?

A. Use a global window with a time based trigger with a delay of 60 minutes.
B. Use a session window with a gap time duration of 60 minutes.
C. Use a fixed-time window with a duration of 60 minutes.
D. Use a sliding time window with a duration of 60 minutes.

Answer: A

NEW QUESTION # 56
A live TV show asks viewers to cast votes using their mobile phones. The event generates a large volume of data during a 3 minute period. You are in charge of the Voting restructure* and must ensure that the platform can handle the load and Hal all votes are processed. You must display partial results write voting is open.
After voting doses you need to count the votes exactly once white optimizing cost. What should you do?

A. Write votes to a Pub/Sub tope and toad into both Bigtable and BigQuery via a Dataflow pipeline Query Bigtable for real-time results and BigQuery for later analysis Shutdown the Bigtable instance when voting concludes D Create a Cloud SQL for PostgreSQL database with high availability (HA) configuration and multiple read replicas
B. Write votes to a Pub Sub tope and have Cloud Functions subscribe to it and write voles to BigQuery
C. Create a Memorystore instance with a high availability (HA) configuration

Answer: A

NEW QUESTION # 57
......

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Google Professional-Data-Engineer Questions - Quick Tips To Pass [2025]

Free PDF Google - Trustable Latest Professional-Data-Engineer Test Guide

Google Certified Professional Data Engineer Exam Sample Questions (Q52-Q57):

Latest Professional-Data-Engineer Test Guide : Free PDF Quiz 2025 Realistic Google Latest Google Certified Professional Data Engineer Exam Test Guide

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