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Career Opportunities

The certified individuals can explore a variety of job opportunities. Some of the positions that they can take up include a Software Engineer, a Cloud Architect, a Data Engineer, a Sales Engineer, a Data Scientist, a Cloud Developer, and a Kubernetes Architect, among others. The salary outlook for these job roles is an average of $128,500 per annum.

The Google Professional-Data-Engineer exam consists of multiple-choice questions and requires candidates to demonstrate their knowledge of various data engineering concepts and technologies. The questions are designed to test the candidate's ability to analyze data, transform data, and implement data processing solutions using Google Cloud Platform's tools and services. Professional-Data-Engineer Exam is two hours long, and candidates must achieve a passing score of 70% or higher to earn the certification.

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Prerequisites

There are no formal requirements that the candidates need to meet to qualify for the Google Professional Data Engineer certification. However, without some level of professional experience, it will be difficult for the students to ace the qualifying test. The target individuals are recommended to have three or more years of industry experience, including one or more years of experience in designing and managing solutions with the help of Google Cloud Platform. It is preferable that the applicants also possess some basic database knowledge.

Google Certified Professional Data Engineer Exam Sample Questions (Q392-Q397):

NEW QUESTION # 392
All Google Cloud Bigtable client requests go through a front-end server ______ they are sent to a Cloud Bigtable node.

A. before
B. after
C. once
D. only if

Answer: A

Explanation:
In a Cloud Bigtable architecture all client requests go through a front-end server before they are sent to a Cloud Bigtable node.
The nodes are organized into a Cloud Bigtable cluster, which belongs to a Cloud Bigtable instance, which is a container for the cluster. Each node in the cluster handles a subset of the requests to the cluster.
When additional nodes are added to a cluster, you can increase the number of simultaneous requests that the cluster can handle, as well as the maximum throughput for the entire cluster.
Reference: https://cloud.google.com/bigtable/docs/overview

NEW QUESTION # 393
What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

A. Keep the row keep as an 8 bit integer
B. Keep your row key reasonably short
C. Keep your row key as long as the field permits
D. Include multiple time series values within the row key

Answer: B

Explanation:
A general guide is to, keep your row keys reasonably short. Long row keys take up additional memory and storage and increase the time it takes to get responses from the Cloud Bigtable server. Reference: https:// cloud.google.com/bigtable/docs/schema-design#row-keys

NEW QUESTION # 394
To give a user read permission for only the first three columns of a table, which access control method would you use?

A. It's not possible to give access to only the first three columns of a table.
B. Primitive role
C. Authorized view
D. Predefined role

Answer: C

Explanation:
An authorized view allows you to share query results with particular users and groups without giving them read access to the underlying tables. Authorized views can only be created in a dataset that does not contain the tables queried by the view.
When you create an authorized view, you use the view's SQL query to restrict access to only the rows and columns you want the users to see.

NEW QUESTION # 395
You work for a farming company. You have one BigQuery table named sensors, which is about
500 MB and contains the list of your 5000 sensors, with columns for id, name, and location. This table is updated every hour. Each sensor generates one metric every 30 seconds along with a timestamp, which you want to store in BigQuery. You want to run an analytical query on the data once a week for monitoring purposes. You also want to minimize costs. What data model should you use?

A. 1. Create a metrics table partitioned by timestamp.
2. Create a sensorId column in the metrics table, which points to the id column in the sensors table.
3. Use an UPDATE statement every 30 seconds to append new metrics to the metrics table.
4. Join the two tables, if needed, when running the analytical query.
B. 1. Create a metrics column in the sensors table.
2. Set RECORD type and REPEATED mode for the metrics column.
3. Use an INSERT statement every 30 seconds to add new metrics.
C. 1. Create a metrics table partitioned by timestamp.
2. Create a sensorId column in the metrics table, that points to the id column in the sensors table.
3. Use an INSERT statement every 30 seconds to append new metrics to the metrics table.
4. Join the two tables, if needed, when running the analytical query.
D. 1. Create a metrics column in the sensors table.
2. Set RECORD type and REPEATED mode for the metrics column.
3. Use an UPDATE statement every 30 seconds to add new metrics.

Answer: C

NEW QUESTION # 396
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. Change "person" to "person.city".
C. Add ", UNNEST(city)" before the WHERE clause.
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 # 397
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