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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

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;
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.
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 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;

Google Professional-Data-Engineer Certification Exam is designed to assess the skills and knowledge of candidates in various areas related to data engineering. Professional-Data-Engineer exam covers topics such as data processing architecture, data modeling, data ingestion, data transformation, and data storage. Candidates are also expected to have a strong understanding of Google Cloud technologies, including BigQuery, Cloud Storage, and Dataflow.

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Google Professional-Data-Engineer Certification Exam is one of the most sought-after certifications in the field of data engineering. Google Certified Professional Data Engineer Exam certification is ideal for individuals who are looking to take their career to the next level and want to demonstrate their expertise in cloud-based data engineering. By earning this certification, individuals can prove to employers that they have the necessary skills and knowledge to design and implement data processing systems using Google Cloud Platform technologies.

Google Certified Professional Data Engineer Exam Sample Questions (Q313-Q318):

NEW QUESTION # 313
You have several different unstructured data sources, within your on-premises data center as well as in the cloud. The data is in various formats, such as Apache Parquet and CSV. You want to centralize this data in Cloud Storage. You need to set up an object sink for your data that allows you to use your own encryption keys. You want to use a GUI-based solution. What should you do?

A. Use BigQuery Data Transfer Service to move files into BigQuery.
B. Use Dataflow to move files into Cloud Storage.
C. Use Cloud Data Fusion to move files into Cloud Storage.
D. Use Storage Transfer Service to move files into Cloud Storage.

Answer: C

Explanation:
To centralize unstructured data from various sources into Cloud Storage using a GUI-based solution while allowing the use of your own encryption keys, Cloud Data Fusion is the most suitable option. Here's why:
* Cloud Data Fusion:
* Cloud Data Fusionis a fully managed, cloud-native data integration service that helps in building and managing ETL pipelines with a visual interface.
* It supports a wide range of data sources and formats, including Apache Parquet and CSV, and provides a user-friendly GUI for pipeline creation and management.
* Custom Encryption Keys:
* Cloud Data Fusion allows the use of customer-managed encryption keys (CMEK) for data encryption, ensuring that your data is securely stored according to your encryption policies.
* Centralizing Data:
* Cloud Data Fusion simplifies the process of moving data from on-premises and cloud sources into Cloud Storage, providing a centralized repository for your unstructured data.
Steps to Implement:
* Set Up Cloud Data Fusion:
* Deploy a Cloud Data Fusion instance and configure it to connect to your various data sources.
* Create ETL Pipelines:
* Use the GUI to create data pipelines that extract data from your sources and load it into Cloud Storage. Configure the pipelines to use your custom encryption keys.
* Run and Monitor Pipelines:
* Execute the pipelines and monitor their performance and data movement through the Cloud Data Fusion dashboard.
Reference Links:
* Cloud Data Fusion Documentation
* Using Customer-Managed Encryption Keys (CMEK)

NEW QUESTION # 314
A shipping company has live package-tracking data that is sent to an Apache Kafka stream in real time. This is then loaded into BigQuery. Analysts in your company want to query the tracking data in BigQuery to analyze geospatial trends in the lifecycle of a package. The table was originally created with ingest-date partitioning.
Over time, the query processing time has increased. You need to implement a change that would improve query performance in BigQuery. What should you do?

A. Tier older data onto Google Cloud Storage files and create a BigQuery table using GCS as an external data source.
B. Re-create the table using data partitioning on the package delivery date.
C. Implement clustering in BigQuery on the package-tracking ID column.
D. Implement clustering in BigQuery on the ingest date column.

Answer: C

NEW QUESTION # 315
Which of the following is NOT true about Dataflow pipelines?

A. Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources
B. Dataflow pipelines can consume data from other Google Cloud services
C. Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner
D. Dataflow pipelines can be programmed in Java

Answer: C

Explanation:
Explanation
Dataflow pipelines can also run on alternate runtimes like Spark and Flink, as they are built using the Apache Beam SDKs Reference: https://cloud.google.com/dataflow/

NEW QUESTION # 316
Your company is selecting a system to centralize data ingestion and delivery. You are considering messaging and data integration systems to address the requirements. The key requirements are:
* The ability to seek to a particular offset in a topic, possibly back to the start of all data ever captured
* Support for publish/subscribe semantics on hundreds of topics
* Retain per-key ordering
Which system should you choose?

A. Cloud Pub/Sub
B. Cloud Storage
C. Apache Kafka
D. Firebase Cloud Messaging

Answer: C

Explanation:
These are the functionalities which are currently lagging/not-available with Pub/Sub.

NEW QUESTION # 317
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 in BigQuery by editing table details.
B. Disable caching by editing the report settings.
C. Clear your browser history for the past hour then reload the tab showing the virtualizations.
D. Refresh your browser tab showing the visualizations.

Answer: B

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

NEW QUESTION # 318
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