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To prepare for the Google Professional-Data-Engineer Certification Exam, candidates must have a solid understanding of data engineering concepts and techniques. They must also have experience working with the Google Cloud Platform and be familiar with the tools and services offered by Google. There are many resources available to help candidates prepare for the exam, including online courses, study guides, practice exams, and hands-on training.

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Google Certified Professional Data Engineer Exam Sample Questions (Q100-Q105):

NEW QUESTION # 100
Your startup has a web application that currently serves customers out of a single region in Asia.
You are targeting funding that will allow your startup to serve customers globally. Your current goal is to optimize for cost, and your post-funding goal is to optimize for global presence and performance. You must use a native JDBC driver. What should you do?

A. Use a Cloud SQL for PostgreSQL highly available instance first, and Bigtable with US, Europe, and Asia replication after securing funding.
B. Use Cloud Spanner to configure a single region instance initially, and then configure multi-region Cloud Spanner instances after securing funding.
C. Use a Cloud SQL for PostgreSQL zonal instance first, and Bigtable with US, Europe, and Asia after securing funding.
D. Use a Cloud SQL for PostgreSQL zonal instance first, and Cloud SQL for PostgreSQL with highly available configuration after securing funding.

Answer: B

Explanation:
When you create a Cloud Spanner instance, you must configure it as either regional (that is, all the resources are contained within a single Google Cloud region) or multi-region (that is, the resources span more than one region).
You can change the instance configuration to multi-regional (or global) at anytime.

NEW QUESTION # 101
Cloud Bigtable is Google's ______ Big Data database service.

A. NoSQL
B. SQL Server
C. Relational
D. mySQL

Answer: A

Explanation:
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 # 102
Your company is migrating its on-premises data warehousing solution to BigQuery. The existing data warehouse uses trigger-based change data capture (CDC) to apply daily updates from transactional database sources Your company wants to use BigQuery to improve its handling of CDC and to optimize the performance of the data warehouse Source system changes must be available for query m near-real time using tog-based CDC streams You need to ensure that changes in the BigQuery reporting table are available with minimal latency and reduced overhead. What should you do? Choose 2 answers

A. Periodically DELETE outdated records from the reporting tablePeriodically use a DML MERGE to simultaneously perform DML INSERT. UPDATE, and DELETE operations in the reporting table
B. Insert each new CDC record and corresponding operation type into a staging table in real time
C. Insert each new CDC record and corresponding operation type into the reporting table in real time and use a materialized view to expose only the current version of each unique record.
D. Perform a DML INSERT UPDATE, or DELETE to replicate each CDC record in the reporting table m real time.

Answer: A,C

NEW QUESTION # 103
Your United States-based company has created an application for assessing and responding to user actions. The primary table's data volume grows by 250,000 records per second. Many third parties use your application's APIs to build the functionality into their own frontend applications.
Your application's APIs should comply with the following requirements:
- Single global endpoint
- ANSI SQL support
- Consistent access to the most up-to-date data
What should you do?

A. Implement Cloud Spanner with the leader in North America and read-only replicas in Asia and Europe.
B. Implement Cloud SQL for PostgreSQL with the master in Norht America and read replicas in Asia and Europe.
C. Implement BigQuery with no region selected for storage or processing.
D. Implement Cloud Bigtable with the primary cluster in North America and secondary clusters in Asia and Europe.

Answer: A

NEW QUESTION # 104
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 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.
B. 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.
C. 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.
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 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.

Answer: C

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 # 105
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