Google braindumps2go professional-data-engineer Leak Questions by Iman q80 vce pdf

Page: 6 / 18

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:	iman

Question 24

You have a requirement to insert minute-resolution data from 50,000 sensors into a BigQuery table. You expect significant growth in data volume and need the data to be available within 1 minute of ingestion for real-time analysis of aggregated trends. What should you do?

Options:

Use bq load to load a batch of sensor data every 60 seconds.

Use a Cloud Dataflow pipeline to stream data into the BigQuery table.

Use the INSERT statement to insert a batch of data every 60 seconds.

Use the MERGE statement to apply updates in batch every 60 seconds.

Discussion

Ilyas

Definitely. I felt much more confident and prepared because of the Cramkey Dumps. I was able to answer most of the questions with ease and I think that helped me to score well on the exam.

Saoirse Jul 17, 2025

That's amazing. I'm glad you found something that worked for you. Maybe I should try them out for my next exam.

Cecilia

Yes, I passed my certification exam using Cramkey Dumps.

Helena Jul 13, 2025

Great. Yes they are really effective

Pippa

I was so happy to see that almost all the questions on the exam were exactly what I found in their Dumps.

Anastasia Jul 11, 2025

You are right…It was amazing! The Cramkey Dumps were so comprehensive and well-organized, it made studying for the exam a breeze.

Addison

Want to tell everybody through this platform that I passed my exam with excellent score. All credit goes to Cramkey Exam Dumps.

Libby Jul 11, 2025

That's good to know. I might check it out for my next IT certification exam. Thanks for the info.

Question 25

Your organization has been collecting and analyzing data in Google BigQuery for 6 months. The majority of the data analyzed is placed in a time-partitioned table namedevents_partitioned. To reduce the cost of queries, your organization created a view calledevents, which queries only the last 14 days of data. The view is described in legacy SQL. Next month, existing applications will be connecting to BigQuery to read theeventsdata via an ODBC connection. You need to ensure the applications can connect. Which two actions should you take? (Choose two.)

Options:

Create a new view over events using standard SQL

Create a new partitioned table using a standard SQL query

Create a new view over events_partitioned using standard SQL

Create a service account for the ODBC connection to use for authentication

Create a Google Cloud Identity and Access Management (Cloud IAM) role for the ODBC connection and shared “events”

Discussion

Question 26

You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You’ve collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?

Options:

Use Cloud Vision AutoML with the existing dataset.

Use Cloud Vision AutoML, but reduce your dataset twice.

Use Cloud Vision API by providing custom labels as recognition hints.

Train your own image recognition model leveraging transfer learning techniques.

Discussion

Question 27

You’re training a model to predict housing prices based on an available dataset with real estate properties. Your plan is to train a fully connected neural net, and you’ve discovered that the dataset contains latitude and longtitude of the property. Real estate professionals have told you that the location of the property is highly influential on price, so you’d like to engineer a feature that incorporates this physical dependency.

What should you do?

Options:

Provide latitude and longtitude as input vectors to your neural net.

Create a numeric column from a feature cross of latitude and longtitude.

Create a feature cross of latitude and longtitude, bucketize at the minute level and use L1 regularization during optimization.

Create a feature cross of latitude and longtitude, bucketize it at the minute level and use L2 regularization during optimization.