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

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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: 374 Q&A's Shared By: adnan
Question 16

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:

Questions 16

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

Options:

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

Discussion
Question 17

You are running your BigQuery project in the on-demand billing model and are executing a change data capture (CDC) process that ingests data. The CDC process loads 1 GB of data every 10 minutes into a temporary table, and then performs a merge into a 10 TB target table. This process is very scan intensive and you want to explore options to enable a predictable cost model. You need to create a BigQuery reservation based on utilization information gathered from BigQuery Monitoring and apply the reservation to the CDC process. What should you do?

Options:

A.

Create a BigQuery reservation for the job.

B.

Create a BigQuery reservation for the service account running the job.

C.

Create a BigQuery reservation for the dataset.

D.

Create a BigQuery reservation for the project.

Discussion
Question 18

You have a petabyte of analytics data and need to design a storage and processing platform for it. You must be able to perform data warehouse-style analytics on the data in Google Cloud and expose the dataset as files for batch analysis tools in other cloud providers. What should you do?

Options:

A.

Store and process the entire dataset in BigQuery.

B.

Store and process the entire dataset in Cloud Bigtable.

C.

Store the full dataset in BigQuery, and store a compressed copy of the data in a Cloud Storage bucket.

D.

Store the warm data as files in Cloud Storage, and store the active data in BigQuery. Keep this ratio as 80% warm and 20% active.

Discussion
Question 19

You are designing a cloud-native historical data processing system to meet the following conditions:

    The data being analyzed is in CSV, Avro, and PDF formats and will be accessed by multiple analysis tools including Cloud Dataproc, BigQuery, and Compute Engine.

    A streaming data pipeline stores new data daily.

    Peformance is not a factor in the solution.

    The solution design should maximize availability.

How should you design data storage for this solution?

Options:

A.

Create a Cloud Dataproc cluster with high availability. Store the data in HDFS, and peform analysis as needed.

B.

Store the data in BigQuery. Access the data using the BigQuery Connector or Cloud Dataproc and Compute Engine.

C.

Store the data in a regional Cloud Storage bucket. Aceess the bucket directly using Cloud Dataproc, BigQuery, and Compute Engine.

D.

Store the data in a multi-regional Cloud Storage bucket. Access the data directly using Cloud Dataproc, BigQuery, and Compute Engine.

Discussion
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