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The candidates must develop practical skills in the exam topics to succeed. These objectives are highlighted below:

Design Data Processing Systems

Select the Relevant Storage Technologies: The considerations for this area include mapping storage systems to the business needs, data modeling, distributed systems, as well as tradeoffs, involving transactions, throughput, and latency;
Design Data Pipeline: The focus for this subsection includes data visualization & publishing and batch & streaming data (Cloud Dataproc, Cloud Dataflow, Cloud Sub/Pub, Hadoop ecosystem, Apache Spark, Apache Beam, and Apache Kafka). It also focuses on online versus batch prediction and job orchestration & automation;
Design Data Processing Solutions: This topic includes the individuals’ expertise in planning, distributed systems usage, choice of infrastructure, hybrid Cloud & edge computing, system availability & fault tolerance. You should also know about the architecture options, including message queues, message brokers, service-oriented architecture, middleware, and serverless function;
Migrate Data Processing & Data Warehousing: This section includes validating migrations, migration from on-premises to Cloud, and awareness of the current state & how to migrate designs to the future state.

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PDFDumps has one of the most comprehensive and top-notch Google Professional-Data-Engineer Exam Questions. We eliminated the filler and simplified the Google Certified Professional Data Engineer Exam preparation process so you can ace the Google certification exam on your first try. Our Google Professional-Data-Engineer Questions include real-world examples to help you learn the fundamentals of the subject not only for the Google exam but also for your future job.

Earning the Google Professional-Data-Engineer Certification demonstrates that an individual has the skills and knowledge required to design and maintain robust data processing systems using Google Cloud Platform services. Google Certified Professional Data Engineer Exam certification is recognized globally and is highly valued by organizations that use Google Cloud Platform for their data processing needs. With the increasing demand for data engineering professionals in the industry, earning this certification can help individuals stand out and advance their careers in this field.

Google Certified Professional Data Engineer Exam Sample Questions (Q140-Q145):

NEW QUESTION # 140
You are designing a real-time system for a ride hailing app that identifies areas with high demand for rides to effectively reroute available drivers to meet the demand. The system ingests data from multiple sources to Pub
/Sub. processes the data, and stores the results for visualization and analysis in real-time dashboards. The data sources include driver location updates every 5 seconds and app-based booking events from riders. The data processing involves real-time aggregation of supply and demand data for the last 30 seconds, every 2 seconds, and storing the results in a low-latency system for visualization. What should you do?

A. Group the data by using a session window in a Dataflow pipeline, and write the aggregated data to BigQuery.
B. Group the data by using a tumbling window in a Dataflow pipeline, and write the aggregated data to Memorystore
C. Group the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to BigQuery.
D. Group the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to Memorystore

Answer: D

Explanation:
A hopping window is a type of sliding window that advances by a fixed period of time, producing overlapping windows. This is suitable for the scenario where the system needs to aggregate data for the last
30 seconds, every 2 seconds, and provide real-time updates. A Dataflow pipeline can implement the hopping window logic using Apache Beam, and process both streaming and batch data sources. Memorystore is a low- latency, in-memory data store that can serve the aggregated data to the visualization layer. BigQuery is not a good choice for this scenario, as it is not optimized for low-latency queries and frequent updates.

NEW QUESTION # 141
What are all of the BigQuery operations that Google charges for?

A. Queries and streaming inserts
B. Storage, queries, and loading data from a file
C. Storage, queries, and streaming inserts
D. Storage, queries, and exporting data

Answer: C

Explanation:
Google charges for storage, queries, and streaming inserts. Loading data from a file and exporting data are free operations.
Reference:
https://cloud.google.com/bigquery/pricing

NEW QUESTION # 142
Your United States-based company has created an application for assessing and responding to user actions. The primary table's data volume grows by 250,000 records per second. Many third parties use your application's APIs to build the functionality into their own frontend applications. Your application's APIs should comply with the following requirements:
* Single global endpoint
* ANSI SQL support
* Consistent access to the most up-to-date data
What should you do?

A. Implement BigQuery with no region selected for storage or processing.
B. Implement Cloud Bigtable with the primary cluster in North America and secondary clusters in Asia and Europe.
C. Implement Cloud Spanner with the leader in North America and read-only replicas in Asia and Europe.
D. Implement Cloud SQL for PostgreSQL with the master in Norht America and read replicas in Asia and Europe.

Answer: C

NEW QUESTION # 143
Which of the following statements is NOT true regarding Bigtable access roles?

A. To give a user access to only one table in a project, you must configure access through your application.
B. You can configure access control only at the project level.
C. Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.
D. To give a user access to only one table in a project, grant the user the Bigtable Editor role for
that table.

Answer: D

Explanation:
For Cloud Bigtable, you can configure access control at the project level. For example, you can grant the ability to:
Read from, but not write to, any table within the project.
Read from and write to any table within the project, but not manage instances.
Read from and write to any table within the project, and manage instances.

NEW QUESTION # 144
You are configuring networking for a Dataflow job. The data pipeline uses custom container images with the libraries that are required for the transformation logic preinstalled. The data pipeline reads the data from Cloud Storage and writes the data to BigQuery. You need to ensure cost-effective and secure communication between the pipeline and Google APIs and services. What should you do?

A. Enable Cloud NAT to provide outbound internet connectivity while enforcing firewall rules.
B. Disable external IP addresses and establish a Private Service Connect endpoint IP address.
C. Disable external IP addresses from worker VMs and enable Private Google Access.
D. Leave external IP addresses assigned to worker VMs while enforcing firewall rules.

Answer: C

Explanation:
Private Google Access allows VMs without external IP addresses to communicate with Google APIs and services over internal routes. This reduces the cost and increases the security of the data pipeline. Custom container images can be stored in Container Registry, which supports Private Google Access. Dataflow supports Private Google Access for both batch and streaming jobs. References:
* Private Google Access overview
* Using Private Google Access and Cloud NAT
* Using custom containers with Dataflow

NEW QUESTION # 145
......

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