Google certkiller professional-data-engineer Exam Questions Tutorials by Adnan 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:	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:

Users are running too many concurrent queries in the system

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

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

NULL values

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:

Create a BigQuery reservation for the job.

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

Create a BigQuery reservation for the dataset.

Create a BigQuery reservation for the project.

Discussion

Answer:

Explanation:

https://cloud.google.com/blog/products/data-analytics/manage-bigquery-costs-with-custom-quotas .

Here's why creating a BigQuery reservation for the project is the most suitable solution:

Project-Level Reservation: BigQuery reservations are applied at the project level. This means that the reserved slots (processing capacity) are shared across all jobs and queries running within that project. Since your CDC process is a significant contributor to your BigQuery usage, reserving slots for the entire project ensures that your CDC process always has access to the necessary resources, regardless of other activities in the project.

Predictable Cost Model: Reservations provide a fixed, predictable cost model. Instead of paying the on-demand price for each query, you pay a fixed monthly fee for the reserved slots. This eliminates the variability of costs associated with on-demand billing, making it easier to budget and forecast your BigQuery expenses.

BigQuery Monitoring: You can use BigQuery Monitoring to analyze the historical usage patterns of your CDC process and other queries within your project. This information helps you determine the appropriate amount of slots to reserve, ensuring that you have enough capacity to handle your workload while optimizing costs.

Why other options are not suitable:

A. Create a BigQuery reservation for the job: BigQuery does not support reservations at the individual job level. Reservations are applied at the project or assignment level.

B. Create a BigQuery reservation for the service account running the job: While you can create reservations for assignments (groups of users or service accounts), it's less efficient than a project-level reservation in this scenario. A project-level reservation covers all jobs within the project, regardless of the service account used.

C. Create a BigQuery reservation for the dataset: BigQuery does not support reservations at the dataset level.

By creating a BigQuery reservation for your project based on your utilization analysis, you can achieve a predictable cost model while ensuring that your CDC process and other queries have the necessary resources to run smoothly.

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:

Store and process the entire dataset in BigQuery.

Store and process the entire dataset in Cloud Bigtable.

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

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.

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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:

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

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

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

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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