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 Professional-Data-Engineer Dumps with Practice Exam Questions Answers

Questions: 250 questions

Last Update: Nov 25, 2021

Google Certification Exam Professional-Data-Engineer has been designed to measure your skills in handling the technical tasks mentioned in the certification syllabus

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Professional-Data-Engineer Questions and Answers

Question # 1

Your financial services company is moving to cloud technology and wants to store 50 TB of financial timeseries data in the cloud. This data is updated frequently and new data will be streaming in all the time. Your company also wants to move their existing Apache Hadoop jobs to the cloud to get insights into this data.

Which product should they use to store the data?

A.

Cloud Bigtable

B.

Google BigQuery

C.

Google Cloud Storage

D.

Google Cloud Datastore

Question # 2

You have an Apache Kafka Cluster on-prem with topics containing web application logs. You need to replicate the data to Google Cloud for analysis in BigQuery and Cloud Storage. The preferred replication method is mirroring to avoid deployment of Kafka Connect plugins.

What should you do?

A.

Deploy a Kafka cluster on GCE VM Instances. Configure your on-prem cluster to mirror your topics to the cluster running in GCE. Use a Dataproc cluster or Dataflow job to read from Kafka and write to GCS.

B.

Deploy a Kafka cluster on GCE VM Instances with the PubSub Kafka connector configured as a Sink connector. Use a Dataproc cluster or Dataflow job to read from Kafka and write to GCS.

C.

Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a Source connector. Use a Dataflow job to read fron PubSub and write to GCS.

D.

Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a Sink connector. Use a Dataflow job to read fron PubSub and write to GCS.

Question # 3

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.

BigQuery

B.

Cloud SQL

C.

Cloud BigTable

D.

Cloud Datastore

Question # 4

You are using Google BigQuery as your data warehouse. Your users report that the following simple query is running very slowly, no matter when they run the query:

SELECT country, state, city FROM [myproject:mydataset.mytable] GROUP BY country

You check the query plan for the query and see the following output in the Read section of Stage:1:

What is the most likely cause of the delay for this query?

A.

Users are running too many concurrent queries in the system

B.

The [myproject:mydataset.mytable] table has too many partitions

C.

Either the state or the city columns in the [myproject:mydataset.mytable] table have too many

NULL values

D.

Most rows in the [myproject:mydataset.mytable] table have the same value in the country column, causing data skew

Question # 5

You architect a system to analyze seismic data. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

A.

Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.

B.

Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.

C.

Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.

D.

Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.

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