Weekend Sale Limited Time 65% Discount Offer - Ends in 0d 00h 00m 00s - Coupon code: get65

Google Updated Professional-Data-Engineer Exam Questions and Answers by adnan

Page: 4 / 18

Google Professional-Data-Engineer Exam Overview :

Exam Name: Google Professional Data Engineer Exam
Exam Code: Professional-Data-Engineer Dumps
Vendor: Google Certification: Google Cloud Certified
Questions: 376 Q&A's Shared By: adnan
Question 16

You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.

Which Google database service should you use?

Options:

A.

Cloud SQL

B.

BigQuery

C.

Cloud Bigtable

D.

Cloud Datastore

Discussion
Victoria
Hey, guess what? I passed the certification exam! I couldn't have done it without Cramkey Dumps.
Isabel Sep 21, 2024
Same here! I was so surprised when I saw that almost all the questions on the exam were exactly what I found in their study materials.
Aliza
I used these dumps for my recent certification exam and I can say with certainty that they're absolutely valid dumps. The questions were very similar to what came up in the actual exam.
Jakub Sep 22, 2024
That's great to hear. I am going to try them soon.
Aryan
Absolutely rocked! They are an excellent investment for anyone who wants to pass the exam on the first try. They save you time and effort by providing a comprehensive overview of the exam content, and they give you a competitive edge by giving you access to the latest information. So, I definitely recommend them to new students.
Jessie Sep 28, 2024
did you use PDF or Engine? Which one is most useful?
Erik
Hey, I have passed my exam using Cramkey Dumps?
Freyja Oct 17, 2024
Really, what are they? All come in your pool? Please give me more details, I am going to have access their subscription. Please brother, give me more details.
Question 17

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 patientrecords. 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?

Options:

A.

Add capacity (memory and disk space) to the database server by the order of 200.

B.

Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.

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.

Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.

Discussion
Question 18

Your company is using WHILECARD tables to query data across multiple tables with similar names. The SQL statement is currently failing with the following error:

# Syntax error : Expected end of statement but got “-“ at [4:11]

SELECT age

FROM

bigquery-public-data.noaa_gsod.gsod

WHERE

age != 99

AND_TABLE_SUFFIX = ‘1929’

ORDER BY

age DESC

Which table name will make the SQL statement work correctly?

Options:

A.

‘bigquery-public-data.noaa_gsod.gsod‘

B.

bigquery-public-data.noaa_gsod.gsod*

C.

‘bigquery-public-data.noaa_gsod.gsod’*

D.

‘bigquery-public-data.noaa_gsod.gsod*`

Discussion
Question 19

You are deploying 10,000 new Internet of Things devices to collect temperature data in your warehouses globally. You need to process, store and analyze these very large datasets in real time. What should you do?

Options:

A.

Send the data to Google Cloud Datastore and then export to BigQuery.

B.

Send the data to Google Cloud Pub/Sub, stream Cloud Pub/Sub to Google Cloud Dataflow, and store the data in Google BigQuery.

C.

Send the data to Cloud Storage and then spin up an Apache Hadoop cluster as needed in Google Cloud Dataproc whenever analysis is required.

D.

Export logs in batch to Google Cloud Storage and then spin up a Google Cloud SQL instance, import the data from Cloud Storage, and run an analysis as needed.

Discussion
Page: 4 / 18
Title
Questions
Posted

Professional-Data-Engineer
PDF

$36.75  $104.99

Professional-Data-Engineer Testing Engine

$43.75  $124.99

Professional-Data-Engineer PDF + Testing Engine

$57.75  $164.99