Free Preparation Discussions
MENU
Google Professional Data Engineer Exam Questions
- Topic 1: Designing data processing systems: It delves into designing for security and compliance, reliability and fidelity, flexibility and portability, and data migrations.
- Topic 2: Ingesting and processing the data: The topic discusses planning of the data pipelines, building the pipelines, acquisition and import of data, and deploying and operationalizing the pipelines.
- Topic 3: Storing the data: This topic explains how to select storage systems and how to plan using a data warehouse. Additionally, it discusses how to design for a data mesh.
- Topic 4: Preparing and using data for analysis: Questions about data for visualization, data sharing, and assessment of data may appear.
- Topic 5: Maintaining and automating data workloads: It discusses optimizing resources, automation and repeatability design, and organization of workloads as per business requirements. Lastly, the topic explains monitoring and troubleshooting processes and maintaining awareness of failures.
Free Google Professional Data Engineer Exam Actual Questions
Note: Premium Questions for Professional Data Engineer were last updated On Mar. 12, 2026 (see below)
What is the HBase Shell for Cloud Bigtable?
The HBase shell is a command-line tool that performs administrative tasks, such as creating and deleting tables. The Cloud Bigtable HBase client for Java makes it possible to use the HBase shell to connect to Cloud Bigtable.
How can you get a neural network to learn about relationships between categories in a categorical feature?
There are two problems with one-hot encoding. First, it has high dimensionality, meaning that instead of having just one value, like a continuous feature, it has many values, or dimensions. This makes computation more time-consuming, especially if a feature has a very large number of categories. The second problem is that it doesn't encode any relationships between the categories. They are completely independent from each other, so the network has no way of knowing which ones are similar to each other.
Both of these problems can be solved by representing a categorical feature with an embedding
column. The idea is that each category has a smaller vector with, let's say, 5 values in it. But unlike a one-hot vector, the values are not usually 0. The values are weights, similar to the weights that are used for basic features in a neural network. The difference is that each category has a set of weights (5 of them in this case).
You can think of each value in the embedding vector as a feature of the category. So, if two categories are very similar to each other, then their embedding vectors should be very similar too.
Your organization has been collecting and analyzing data in Google BigQuery for 6 months. The majority of the data analyzed is placed in a time-partitioned table named events_partitioned. To reduce the cost of queries, your organization created a view called events, which queries only the last 14 days of dat
a. The view is described in legacy SQL. Next month, existing applications will be connecting to BigQuery to read the events data via an ODBC connection. You need to ensure the applications can connect. Which two actions should you take? (Choose two.)
You have data pipelines running on BigQuery, Cloud Dataflow, and Cloud Dataproc. You need to perform health checks and monitor their behavior, and then notify the team managing the pipelines if they fail. You also need to be able to work across multiple projects. Your preference is to use managed products of features of the platform. What should you do?
You have a data pipeline with a Dataflow job that aggregates and writes time series metrics to Bigtable. You notice that data is slow to update in Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the dat
a. What should you do?
Choose 2 answers
- Select Question Types you want
- Set your Desired Pass Percentage
- Allocate Time (Hours : Minutes)
- Create Multiple Practice tests with Limited Questions
- Customer Support
Clemencia
6 days agoDiane
14 days agoMelvin
21 days agoGregoria
28 days agoDiane
1 month agoMerlyn
1 month agoSharen
2 months agoLeota
2 months agoTrinidad
2 months agoLacresha
2 months agoTimmy
3 months agoLashaun
3 months agoOllie
3 months agoEdison
3 months agoTawna
4 months agoCoral
4 months agoBrendan
4 months agoRicarda
4 months agoVirgie
5 months agoAnnmarie
5 months agoGolda
5 months agoFranchesca
5 months agoElliott
6 months agoBreana
6 months agoKing
6 months agoCarma
6 months agoJustine
8 months agoLoise
8 months agoStanton
9 months agoFrederica
11 months agoMaia
11 months agoCarolann
11 months agoWinfred
12 months agoTennie
12 months agoJoye
1 year agoSarina
1 year agoOctavio
1 year agoHermila
1 year agoCordelia
1 year agoStanton
1 year agoDetra
1 year agoMaynard
1 year agoDeangelo
1 year agoChristene
1 year agoGilma
1 year agoGwenn
1 year agoRonald
1 year agoShawn
1 year agoDonte
1 year agoAntonette
1 year agoSon
1 year agoDouglass
1 year agoAliza
1 year agoJavier
1 year agoShannon
1 year agoTheron
1 year agoKristofer
1 year agoLauna
1 year agoDerick
1 year agoVerdell
2 years agoFreida
2 years agoVesta
2 years agoLashaunda
2 years agoLon
2 years agoEric
2 years agoErasmo
2 years agoDierdre
2 years agoZack
2 years agosaqib
2 years agoanderson
2 years ago