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Google Professional-Data-Engineer certification exam is a comprehensive assessment of an individual’s skills and knowledge in designing, building, and managing data processing systems on the Google Cloud Platform. Google Certified Professional Data Engineer Exam certification validates an individual's ability to use Google Cloud technologies to solve complex data processing challenges and create data-driven solutions.

To become a Google Certified Professional Data Engineer, candidates need to pass the Professional-Data-Engineer Exam. Professional-Data-Engineer exam is designed to test the knowledge and skills of professionals in designing, building, and maintaining data processing systems. Professional-Data-Engineer exam consists of multiple-choice and scenario-based questions that test the candidate's ability to analyze and solve real-world problems related to data engineering.

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Career Path

Completing the exam associated with the Google Professional Data Engineer certification provides you with a great validation of your skills in designing, building, operationalizing, securing, and monitoring data processing systems. The job roles that you can take up after getting certified include a Google Cloud Data Engineer, an Operations Engineer, a Cloud Infrastructure Engineer, a DevOps Infrastructure Engineer, a Cloud Database Engineer, a Google Cloud IAM Engineer, a DataOps Engineer, a Big Data Engineer, a Google Cloud Platform Data Architect, and more. The average salary that you can expect to earn with this certificate is around $125,550 per year. However, the real remuneration will depend on a specific job title, location of an individual, and his/her working experience.

Google Certified Professional Data Engineer Exam Sample Questions (Q178-Q183):

NEW QUESTION # 178
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. Add ", UNNEST(city)" before the WHERE clause.
B. Add ", UNNEST(person)" before the WHERE clause.
C. Change "person" to "person.city".
D. Change "person" to "city.person".

Answer: B

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 # 179
When creating a new Cloud Dataproc cluster with the projects.regions.clusters.create operation, these four values are required: project, region, name, and ____.

A. zone
B. node
C. label
D. type

Answer: A

Explanation:
At a minimum, you must specify four values when creating a new cluster with the projects.regions.clusters.create operation:
The project in which the cluster will be created
The region to use
The name of the cluster
The zone in which the cluster will be created
You can specify many more details beyond these minimum requirements. For example, you can also specify the number of workers, whether preemptible compute should be used, and the network settings.

NEW QUESTION # 180
You are designing a fault-tolerant architecture to store data in a regional BigOuery dataset. You need to ensure that your application is able to recover from a corruption event in your tables that occurred within the past seven days. You want to adopt managed services with the lowest RPO and most cost-effective solution. What should you do?

A. Access historical data by using time travel in BigQuery.
B. Export the data from BigQuery into a new table that excludes the corrupted data.
C. Migrate your data to multi-region BigQuery buckets.
D. Create a BigQuery table snapshot on a daily basis.

Answer: A

Explanation:
Time travel is a feature of BigQuery that allows you to query and recover data from any point within the past seven days. You can use the FOR SYSTEM_TIME AS OF clause in your SQL query to specify the timestamp of the data you want to access. This way, you can restore your tables to a previous state before the corruption event occurred. Time travel is automatically enabled for all datasets and does not incur any additional cost or storage.
Reference:
Data retention with time travel and fail-safe | BigQuery | Google Cloud BigQuery Time Travel: How to access Historical Data? | Easy Steps

NEW QUESTION # 181
Your business users need a way to clean and prepare data before using the data for analysis. Your business users are less technically savvy and prefer to work with graphical user interfaces to define their transformations. After the data has been transformed, the business users want to perform their analysis directly in a spreadsheet. You need to recommend a solution that they can use. What should you do?

A. Use Dataflow to clean the data, and write the results to BigQuery. Analyze the data by using Connected Sheets.
B. Use Dataprep to clean the data, and write the results to BigQuery Analyze the data by using Connected Sheets.
C. Use Dataflow to clean the data, and write the results to BigQuery. Analyze the data by using Looker Studio.
D. Use Dataprep to clean the data, and write the results to BigQuery Analyze the data by using Looker Studio.

Answer: B

Explanation:
For business users who are less technically savvy and prefer graphical user interfaces, Dataprep is an ideal tool for cleaning and preparing data, as it offers a user-friendly interface for defining data transformations without the need for coding. Once the data is cleaned and prepared, writing the results to BigQuery allows for the storage and management of large datasets. Analyzing the data using Connected Sheets enables business users to work within the familiar environment of a spreadsheet, leveraging the power of BigQuery directly within Google Sheets. This solution aligns with the needs of the users and follows Google's recommended practices for data cleaning, preparation, and analysis.
References:
* Connected Sheets | Google Sheets | Google for Developers
* Professional Data Engineer Certification Exam Guide | Learn - Google Cloud
* Engineer Data in Google Cloud | Google Cloud Skills Boost - Qwiklabs

NEW QUESTION # 182
You create an important report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. You notice that visualizations are not showing data that is less than 1 hour old. What should you do?

A. Disable caching by editing the report settings.
B. Disable caching in BigQuery by editing table details.
C. Refresh your browser tab showing the visualizations.
D. Clear your browser history for the past hour then reload the tab showing the virtualizations.

Answer: A

Explanation:
Reference https://support.google.com/datastudio/answer/7020039?hl=en

NEW QUESTION # 183
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