NEW PROFESSIONAL-DATA-ENGINEER EXAM PRICE & PROFESSIONAL-DATA-ENGINEER REAL TORRENT

New Professional-Data-Engineer Exam Price & Professional-Data-Engineer Real Torrent

New Professional-Data-Engineer Exam Price & Professional-Data-Engineer Real Torrent

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Exam Topics

The syllabus of the Google Professional Data Engineer exam is divided into 4 topics, each covering specific knowledge and skills that the candidates need to develop while preparing for the test. A full outline of the exam content can be viewed on the official website. The highlights of the domains covered in the test are as follows:

Topic 1. Designing Data Processing Systems

To answer the questions related to this first topic of the certification exam, the individuals need to demonstrate their proficiency in selecting the proper storage technologies. This includes their understanding of data modeling, schema design, distributed systems, as well as tradeoffs involving throughput, latency, and transactions. Moreover, the applicants need to have the ability to map storage systems to the business needs. It also measures one’s skills in designing data pipelines, designing a data processing solution, as well as migrating data warehousing & data processing.

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Google Certified Professional Data Engineer Exam Sample Questions (Q230-Q235):

NEW QUESTION # 230
You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud 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. Which two actions should you take? (Choose two.)

  • A. Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions
  • B. Modify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable
  • C. Configure your Cloud Dataflow pipeline to use local execution
  • D. Increase the number of nodes in the Cloud Bigtable cluster
  • E. Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable

Answer: A,D

Explanation:
References:


NEW QUESTION # 231
Which is the preferred method to use to avoid hotspotting in time series data in Bigtable?

  • A. Salting
  • B. Hashing
  • C. Randomization
  • D. Field promotion

Answer: D

Explanation:
By default, prefer field promotion. Field promotion avoids hotspotting in almost all cases, and it tends to make it easier to design a row key that facilitates queries.


NEW QUESTION # 232
You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics.
Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded.
The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?

  • A. Add capacity (memory and disk space) to the database server by the order of 200.
  • B. Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.
  • C. Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.
  • D. Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.

Answer: C


NEW QUESTION # 233
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:
Reference: https://cloud.google.com/solutions/business-intelligence/


NEW QUESTION # 234
MJTelco is building a custom interface to share dat
a. They have these requirements:
They need to do aggregations over their petabyte-scale datasets.
They need to scan specific time range rows with a very fast response time (milliseconds).
Which combination of Google Cloud Platform products should you recommend?

  • A. BigQuery and Cloud Storage
  • B. BigQuery and Cloud Bigtable
  • C. Cloud Bigtable and Cloud SQL
  • D. Cloud Datastore and Cloud Bigtable

Answer: B


NEW QUESTION # 235
......

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