Google examstrust google Professional-data-engineer Online Access by Arnie q161 vce pdf

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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:	arnie

Question 40

Flowlogistic wants to use Google BigQuery as their primary analysis system, but they still have Apache Hadoop and Spark workloads that they cannot move to BigQuery. Flowlogistic does not know how to store the data that is common to both workloads. What should they do?

Options:

Store the common data in BigQuery as partitioned tables.

Store the common data in BigQuery and expose authorized views.

Store the common data encoded as Avro in Google Cloud Storage.

Store he common data in the HDFS storage for a Google Cloud Dataproc cluster.

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Question 41

Flowlogistic is rolling out their real-time inventory tracking system. The tracking devices will all send package-tracking messages, which will now go to a single Google Cloud Pub/Sub topic instead of the Apache Kafka cluster. A subscriber application will then process the messages for real-time reporting and store them in Google BigQuery for historical analysis. You want to ensure the package data can be analyzed over time.

Which approach should you take?

Options:

Attach the timestamp on each message in the Cloud Pub/Sub subscriber application as they are received.

Attach the timestamp and Package ID on the outbound message from each publisher device as they are sent to Clod Pub/Sub.

Use the NOW () function in BigQuery to record the event’s time.

Use the automatically generated timestamp from Cloud Pub/Sub to order the data.

Discussion

Question 42

Flowlogistic’s CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they’ve purchased a visualization tool to simplify the creation of BigQuery reports. However, they’ve been overwhelmed by all thedata in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

Options:

Export the data into a Google Sheet for virtualization.

Create an additional table with only the necessary columns.

Create a view on the table to present to the virtualization tool.

Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.

Discussion

Question 43

Why do you need to split a machine learning dataset into training data and test data?

Options:

So you can try two different sets of features

To make sure your model is generalized for more than just the training data

To allow you to create unit tests in your code