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What is the duration, language, and format of Google Professional Data Engineer Exam

Number of Questions: 50-60
Format: Multiple choices, multiple answers
Cost: $200

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No doubt the Google Certified Professional Data Engineer Exam (Professional-Data-Engineer) certification is one of the most challenging certification exams in the market. This Google Certified Professional Data Engineer Exam (Professional-Data-Engineer) certification exam gives always a tough time to Google Certified Professional Data Engineer Exam (Professional-Data-Engineer) exam candidates. The TestkingPDF understands this hurdle and offers recommended and real Google Professional-Data-Engineer exam practice questions in three different formats.

The Google Professional-Data-Engineer Exam is designed for data engineers, data analysts, and data scientists who work with data processing systems and data analytics solutions on the Google Cloud Platform. Google Certified Professional Data Engineer Exam certification demonstrates their ability to design, build, and manage scalable, reliable, and cost-effective data solutions on Google Cloud.

Google Certified Professional Data Engineer Exam Sample Questions (Q322-Q327):

NEW QUESTION # 322
By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

A. Windows at every 10 minutes
B. Windows at every 100 MB of data
C. Windows at every 1 minute
D. Single, Global Window

Answer: D

Explanation:
Dataflow's default windowing behavior is to assign all elements of a PCollection to a single, global window, even for unbounded PCollections

NEW QUESTION # 323
You operate a logistics company, and you want to improve event delivery reliability for vehicle-based sensors.
You operate small data centers around the world to capture these events, but leased lines that provide connectivity from your event collection infrastructure to your event processing infrastructure are unreliable, with unpredictable latency. You want to address this issue in the most cost-effective way. What should you do?

A. Write a Cloud Dataflow pipeline that aggregates all data in session windows.
B. Establish a Cloud Interconnect between all remote data centers and Google.
C. Have the data acquisition devices publish data to Cloud Pub/Sub.
D. Deploy small Kafka clusters in your data centers to buffer events.

Answer: C

NEW QUESTION # 324
You have a BigQuery dataset named "customers". All tables will be tagged by using a Data Catalog tag template named "gdpr". The template contains one mandatory field,
"has_sensitive_data", with a boolean value. All employees must be able to do a simple search and find tables in the dataset that have either true or false in the "has_sensitive_data' field.
However, only the Human Resources (HR) group should be able to see the data inside the tables for which "has_sensitive data" is true. You give the all employees group the bigquery.metadataViewer and bigquery.connectionUser roles on the dataset. You want to minimize configuration overhead. What should you do next?

A. Create the "gdpr" tag template with public visibility. Assign the datacatalog.tagTemplateViewer role on this tag to the all employees group, and assign the bigquery.dataViewer role to the HR group on the tables that contain sensitive data.
B. Create the "gdpr" tag template with private visibility. Assign the datacatalog.tagTemplateViewer role on this tag to the all employees group, and assign the bigquery.dataViewer role to the HR group on the tables that contain sensitive data.
C. Create the "gdpr" tag template with private visibility. Assign the bigquery.dataViewer role to the HR group on the tables that contain sensitive data.
D. Create the "gdpr" tag template with public visibility. Assign the bigquery.dataViewer role to the HR group on the tables that contain sensitive data.

Answer: D

NEW QUESTION # 325
You need to modernize your existing on-premises data strategy. Your organization currently uses.
* Apache Hadoop clusters for processing multiple large data sets, including on-premises Hadoop Distributed File System (HDFS) for data replication.
* Apache Airflow to orchestrate hundreds of ETL pipelines with thousands of job steps.
You need to set up a new architecture in Google Cloud that can handle your Hadoop workloads and requires minimal changes to your existing orchestration processes. What should you do?

A. Use Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Orchestrate your pipelines with Cloud Composer..
B. Use Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases Convert your ETL pipelines to Dataflow.
C. Use Dataproc to migrate your Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Use Cloud Data Fusion to visually design and deploy your ETL pipelines.
D. Use Bigtable for your large workloads, with connections to Cloud Storage to handle any HDFS use cases Orchestrate your pipelines with Cloud Composer.

Answer: A

Explanation:
Dataproc is a fully managed service that allows you to run Apache Hadoop and Spark workloads on Google Cloud. It is compatible with the open source ecosystem, so you can migrate your existing Hadoop clusters to Dataproc with minimal changes. Cloud Storage is a scalable, durable, and cost-effective object storage service that can replace HDFS for storing and accessing data. Cloud Storage offers interoperability with Hadoop through connectors, so you can use it as a data source or sink for your Dataproc jobs. Cloud Composer is a fully managed service that allows you to create, schedule, and monitor workflows using Apache Airflow. It is integrated with Google Cloud services, such as Dataproc, BigQuery, Dataflow, and Pub/Sub, so you can orchestrate your ETL pipelines across different platforms. Cloud Composer is compatible with your existing Airflow code, so you can migrate your existing orchestration processes to Cloud Composer with minimal changes.
The other options are not as suitable as Dataproc and Cloud Composer for this use case, because they either require more changes to your existing code, or do not meet your requirements. Dataflow is a fully managed service that allows you to create and run scalable data processing pipelines using Apache Beam. However, Dataflow is not compatible with your existing Hadoop code, so you would need to rewrite your ETL pipelines using Beam. Bigtable is a fully managed NoSQL database service that can handle large and complex data sets. However, Bigtable is not compatible with your existing Hadoop code, so you would need to rewrite your queries and applications using Bigtable APIs. Cloud Data Fusion is a fully managed service that allows you to visually design and deploy data integration pipelines using a graphical interface. However, Cloud Data Fusion is not compatible with your existing Airflow code, so you would need to recreate your orchestration processes using Cloud Data Fusion UI. Reference:
Dataproc overview
Cloud Storage connector for Hadoop
Cloud Composer overview

NEW QUESTION # 326
You are running a streaming pipeline with Dataflow and are using hopping windows to group the data as the data arrives. You noticed that some data is arriving late but is not being marked as late data, which is resulting in inaccurate aggregations downstream. You need to find a solution that allows you to capture the late data in the appropriate window. What should you do?

A. Use watermarks to define the expected data arrival window. Allow late data as it arrives.
B. Change your windowing function to session windows to define your windows based on certain activity.
C. Expand your hopping window so that the late data has more time to arrive within the grouping.
D. Change your windowing function to tumbling windows to avoid overlapping window periods.

Answer: A

NEW QUESTION # 327
......

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