PROFESSIONAL-DATA-ENGINEER NEW DUMPS PPT, PROFESSIONAL-DATA-ENGINEER LATEST EXAM QUESTION

Professional-Data-Engineer New Dumps Ppt, Professional-Data-Engineer Latest Exam Question

Professional-Data-Engineer New Dumps Ppt, Professional-Data-Engineer Latest Exam Question

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Getting a Google Professional-Data-Engineer trusted certification is a way to prove your expertise and show you that you are ready all the time to take the additional responsibilities. The ITExamSimulator Professional-Data-Engineer certification exam assists you to climb the corporate ladder easily and helps you to achieve your professional career objectives. With the ITExamSimulator Professional-Data-Engineer Certification Exam you can get industry prestige and a significant competitive advantage.

Google Certified Professional Data Engineer exam is a certification that validates the skills and knowledge of data engineers in designing and managing data processing systems on the Google Cloud Platform. Google Certified Professional Data Engineer Exam certification is designed for individuals with experience in data processing, analysis, and transformation, who are seeking to demonstrate their proficiency in Google Cloud technologies and data engineering best practices.

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Professional-Data-Engineer Latest Exam Question & Dumps Professional-Data-Engineer Questions

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Google Certified Professional Data Engineer Exam Sample Questions (Q129-Q134):

NEW QUESTION # 129
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. Implement clustering in BigQuery on the ingest date column.
  • B. Re-create the table using data partitioning on the package delivery date.
  • C. Tier older data onto Cloud Storage files, and leverage extended tables.
  • D. Implement clustering in BigQuery on the package-tracking ID column.

Answer: A


NEW QUESTION # 130
You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt.
You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

  • A. Add a SideInput that returns a Boolean if the element is corrupt.
  • B. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
  • C. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
  • D. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.

Answer: D

Explanation:
Explanation


NEW QUESTION # 131
You need to copy millions of sensitive patient records from a relational database to BigQuery. The total size of the database is 10 TB. You need to design a solution that is secure and time-efficient. What should you do?

  • A. Export the records from the database as an Avro file. Create a public URL for the Avro file, and then use Storage Transfer Service to move the file to Cloud Storage. Load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.
  • B. Export the records from the database as an Avro file. Copy the file onto a Transfer Appliance and send it to Google, and then load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.
  • C. Export the records from the database as an Avro file. Upload the file to GCS using gsutil, and then load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.
  • D. Export the records from the database into a CSV file. Create a public URL for the CSV file, and then use Storage Transfer Service to move the file to Cloud Storage. Load the CSV file into BigQuery using the BigQuery web UI in the GCP Console.

Answer: C


NEW QUESTION # 132
Scaling a Cloud Dataproc cluster typically involves ____.

  • A. increasing or decreasing the number of worker nodes
  • B. deleting applications from unused nodes periodically
  • C. increasing or decreasing the number of master nodes
  • D. moving memory to run more applications on a single node

Answer: A

Explanation:
After creating a Cloud Dataproc cluster, you can scale the cluster by increasing or decreasing the number of worker nodes in the cluster at any time, even when jobs are running on the cluster. Cloud Dataproc clusters are typically scaled to:
1) increase the number of workers to make a job run faster
2) decrease the number of workers to save money
3) increase the number of nodes to expand available Hadoop Distributed Filesystem (HDFS) storage


NEW QUESTION # 133
An online retailer has built their current application on Google App Engine. A new initiative at the company
mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using
a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google
Cloud database should they choose?

  • A. Cloud BigTable
  • B. BigQuery
  • C. Cloud SQL
  • D. Cloud Datastore

Answer: A

Explanation:
Explanation/Reference:
Reference: https://cloud.google.com/solutions/business-intelligence/


NEW QUESTION # 134
......

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