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Google Professional Data Engineer Certified Professional salary

The average salary of a Google Professional Data Engineer Certified Expert in

United State - 151,247 USD
Europe - 135,347 EURO
England - 115,632 POUND
India - 25,42,327 INR

Preparation Process

To perform well in the Google Professional Data Engineer certification exam, the candidates must be ready to devote ample time to preparation. There is a host of study materials available on the Internet, but if you want to be confident in the authenticity of the resources that you use, it is best to refer to the official platform. Google recommends that the applicants follow the Professional Data Engineer learning path, which is a comprehensive option involving in-person classes, online training, hands-on labs, and other resources from Google Cloud.

Besides that, it is recommended that the students use the official sample questions to familiarize themselves with the question formats that they will encounter during the actual exam. The official webpage also contains additional resources such as Google Cloud documentation and Google Cloud solutions. There is also an option of joining the subject-related webinar to get valuable preparation tips from the Google experts.

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To become a Google Professional-Data-Engineer, candidates need to pass the certification exam. Professional-Data-Engineer exam consists of multiple-choice and scenario-based questions that assess a candidate's ability to design, build, and manage data processing systems on the Google Cloud Platform. Professional-Data-Engineer Exam can be taken online or in-person at a proctored testing center. Candidates have two hours to complete the exam, and they must score at least 70% to pass.

Google Certified Professional Data Engineer Exam Sample Questions (Q222-Q227):

NEW QUESTION # 222
Which of the following statements about Legacy SQL and Standard SQL is not true?

A. One difference between the two query languages is how you specify fully-qualified table names (i.e.
table names that include their associated project name).
B. Standard SQL is the preferred query language for BigQuery.
C. If you write a query in Legacy SQL, it might generate an error if you try to run it with Standard SQL.
D. You need to set a query language for each dataset and the default is Standard SQL.

Answer: D

Explanation:
Explanation
You do not set a query language for each dataset. It is set each time you run a query and the default query language is Legacy SQL.
Standard SQL has been the preferred query language since BigQuery 2.0 was released.
In legacy SQL, to query a table with a project-qualified name, you use a colon, :, as a separator. In standard SQL, you use a period, ., instead.
Due to the differences in syntax between the two query languages (such as with project-qualified table names), if you write a query in Legacy SQL, it might generate an error if you try to run it with Standard SQL.
Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql

NEW QUESTION # 223

A. Use tumbling windows with a 15-mmute window and a fifteen-minute. withAllowedLateness operator.
B. Use session windows with a 30-mmute gap duration.
C. Use session windows with a 15-minute gap duration.
D. You need to detect the average noise level from a sensor when data is received for a duration of more than 30 minutes, but the window ends when no data has been received for 15 minutes What should you do?
E. Use hopping windows with a 15-mmute window, and a thirty-minute period.

Answer: A

Explanation:
Session windows are dynamic windows that group elements based on the periods of activity. They are useful for streaming data that is irregularly distributed with respect to time. In this case, the noise level data from the sensors is only sent when it exceeds a certain threshold, and the duration of the noise events may vary. Therefore, session windows can capture the average noise level for each sensor during the periods of high noise, and end the window when there is no data for a specified gap duration. The gap duration should be 15 minutes, as the requirement is to end the window when no data has been received for 15 minutes. A 30-minute gap duration would be too long and may miss some noise events that are shorter than 30 minutes. Tumbling windows and hopping windows are fixed windows that group elements based on a fixed time interval. They are not suitable for this use case, as they may split or overlap the noise events from the sensors, and do not account for the periods of inactivity. Reference:
Windowing concepts
Session windows
Windowing in Dataflow

NEW QUESTION # 224
You have spent a few days loading data from comma-separated values (CSV) files into the Google
BigQuery table CLICK_STREAM. The column DTstores the epoch time of click events. For convenience,
you chose a simple schema where every field is treated as the STRINGtype. Now, you want to compute
web session durations of users who visit your site, and you want to change its data type to the
TIMESTAMP. You want to minimize the migration effort without making future queries computationally
expensive. What should you do?

A. Delete the table CLICK_STREAM, and then re-create it such that the column DTis of the TIMESTAMP
type. Reload the data.
B. Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to
cast strings from the column DTinto TIMESTAMPvalues. Run the query into a destination table
NEW_CLICK_STREAM, in which the column TSis the TIMESTAMPtype. Reference the table
NEW_CLICK_STREAMinstead of the table CLICK_STREAMfrom now on. In the future, new data is
loaded into the table NEW_CLICK_STREAM.
C. Add a column TSof the TIMESTAMPtype to the table CLICK_STREAM, and populate the numeric
values from the column TSfor each row. Reference the column TSinstead of the column DTfrom now
on.
D. Add two columns to the table CLICK STREAM: TSof the TIMESTAMPtype and IS_NEWof the
BOOLEANtype. Reload all data in append mode. For each appended row, set the value of IS_NEWto
true. For future queries, reference the column TSinstead of the column DT, with the WHEREclause
ensuring that the value of IS_NEWmust be true.
E. Create a view CLICK_STREAM_V, where strings from the column DTare cast into TIMESTAMPvalues.
Reference the view CLICK_STREAM_Vinstead of the table CLICK_STREAMfrom now on.

Answer: D

NEW QUESTION # 225
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 Dataprep to clean the data, and write the results to BigQuery Analyze the data by using Looker Studio.
B. Use Dataflow to clean the data, and write the results to BigQuery. Analyze the data by using Looker Studio.
C. Use Dataflow to clean the data, and write the results to BigQuery. Analyze the data by using Connected Sheets.
D. Use Dataprep to clean the data, and write the results to BigQuery Analyze the data by using Connected Sheets.

Answer: D

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 # 226
You are deploying a MySQL database workload onto Cloud SQL. The database must be able to scale up to support several readers from various geographic regions. The database must be highly available and meet low RTO and RPO requirements, even in the event of a regional outage. You need to ensure that interruptions to the readers are minimal during a database failover. What should you do?

A. Create a highly available Cloud SQL instance in region Create a highly available read replica in region B. Scale up read workloads by creating cascading read replicas in multiple regions.
Backup the Cloud SQL instances to a multi-regional Cloud Storage bucket. Restore the Cloud SQL backup to a new instance in another region when Region A is down.
B. Create a highly available Cloud SQL instance in region A. Scale up read workloads by creating read replicas in multiple regions. Promote one of the read replicas when region A is down.
C. Create a highly available Cloud SQL instance in region A. Scale up read workloads by creating read replicas in the same region. Failover to the standby Cloud SQL instance when the primary instance fails.
D. Create a highly available Cloud SQL instance in region A. Create a highly available read replica in region B. Scale up read workloads by creating cascading read replicas in multiple regions.
Promote the read replica in region B when region A is down.

Answer: D

NEW QUESTION # 227
......

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