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Google Professional-Data-Engineer (Google Certified Professional Data Engineer) certification exam is designed for individuals who have expertise in designing, building, and managing data processing systems on the Google Cloud Platform. Professional-Data-Engineer Exam is intended for professionals who want to validate their skills and knowledge in data engineering, including the design and implementation of scalable and robust data processing systems.

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Google Professional-Data-Engineer Certification Exam is designed for professionals who want to demonstrate their skills in designing and building data processing systems on the Google Cloud Platform. Google Certified Professional Data Engineer Exam certification is ideal for data engineers, data analysts, and database administrators who work with big data and want to enhance their skills. Professional-Data-Engineer Exam covers a broad range of topics, including data processing, data storage, data analysis, and machine learning.

Google Certified Professional Data Engineer Exam Sample Questions (Q53-Q58):

NEW QUESTION # 53
You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones.
What should you do?

A. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
B. Create a table on Cloud SQL, and insert and delete rows with the job information
C. Create a table on Cloud Spanner, and insert and delete rows with the job information
D. Create an API using App Engine to receive and send messages to the applications

Answer: D

Explanation:
References:

NEW QUESTION # 54
You are planning to use Cloud Storage as pad of your data lake solution. The Cloud Storage bucket will contain objects ingested from external systems. Each object will be ingested once, and the access patterns of individual objects will be random. You want to minimize the cost of storing and retrieving these objects. You want to ensure that any cost optimization efforts are transparent to the users and applications. What should you do?

A. Create a Cloud Storage bucket with an Object Lifecycle Management policy to transition objects from Standard to Coldline storage class if an object age reaches 30 days.
B. Create two Cloud Storage buckets. Use the Standard storage class for the first bucket, and use the Coldline storage class for the second bucket. Migrate objects from the first bucket to the second bucket after 30 days.
C. Create a Cloud Storage bucket with an Object Lifecycle Management policy to transition objects from Standard to Coldline storage class if an object is not live.
D. Create a Cloud Storage bucket with Autoclass enabled.

Answer: A

Explanation:
To minimize the cost of storing and retrieving objects in a Cloud Storage bucket while ensuring that cost optimization efforts are transparent to the users and applications, enabling Autoclass is the best approach.
Here's why:
* Autoclass Feature:
* Autoclassautomatically transitions objects between different storage classes (Standard, Nearline, Coldline, and Archive) based on their access patterns.
* It ensures that frequently accessed data is kept in lower-latency, higher-cost storage classes and infrequently accessed data is moved to higher-latency, lower-cost storage classes.
* Cost Optimization:
* Autoclass optimizes storage costs by automatically moving objects to the most cost-effective storage class based on actual usage patterns, without manual intervention.
* This feature ensures that objects are stored in the most economical class appropriate for their access frequency, reducing storage costs over time.
* Transparency to Users:
* The transition of objects between storage classes is handled automatically by Cloud Storage, making the process transparent to users and applications.
* Users and applications interact with the objects in the same way, regardless of the underlying storage class, ensuring seamless access.
Steps to Implement:
* Create a Cloud Storage Bucket:
* When creating a new Cloud Storage bucket, enable the Autoclass feature.
* Configure Autoclass:
* Autoclass configuration is typically a straightforward process in the Google Cloud Console, where you enable it during bucket creation.
* Monitor and Adjust:
* Monitor the storage and access patterns through the Google Cloud Console to ensure that Autoclass is optimizing costs as expected.
Reference Links:
* Google Cloud Storage Autoclass
* Optimizing Storage Costs with Autoclass

NEW QUESTION # 55
Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?

A. Change "person" to "city.person".
B. Add ", UNNEST(city)" before the WHERE clause.
C. Change "person" to "person.city".
D. Add ", UNNEST(person)" before the WHERE clause.

Answer: D

Explanation:
To access the person.city column, you need to "UNNEST(person)" and JOIN it to table1 using a comma.
Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql#nested_repeated_results

NEW QUESTION # 56
Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file.
What is the most likely cause of this problem?

A. The CSV data has invalid rows that were skipped on import.
B. The CSV data loaded in BigQuery is not flagged as CSV.
C. The CSV data loaded in BigQuery is not using BigQuery's default encoding.
D. The CSV data has not gone through an ETL phase before loading into BigQuery.

Answer: C

Explanation:
Bigquery understands UTF-8 encoding anything other than that will result in data issues with schema.

NEW QUESTION # 57
Which Google Cloud Platform service is an alternative to Hadoop with Hive?

A. Cloud Dataflow
B. BigQuery
C. Cloud Datastore
D. Cloud Bigtable

Answer: B

NEW QUESTION # 58
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