Professional-Data-Engineer Exam Dumps - Google Professional Data Engineer Exam

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Question # 25

Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow. Numerous data logs are being are being generated during this step, and the team wants to analyze them. Due to the dynamic nature of the campaign, the data is growing exponentially every hour.

The data scientists have written the following code to read the data for a new key features in the logs.

BigQueryIO.Read

.named(â€œReadLogDataâ€)

.from(â€œclouddataflow-readonly:samples.log_dataâ€)

You want to improve the performance of this data read. What should you do?

Specify the TableReference object in the code.

Use .fromQuery operation to read specific fields from the table.

Use of both the Google BigQuery TableSchema and TableFieldSchema classes.

Call a transform that returns TableRow objects, where each element in the PCollexction represents a single row in the table.

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Question # 26

You have several Spark jobs that run on a Cloud Dataproc cluster on a schedule. Some of the jobs run in sequence, and some of the jobs run concurrently. You need to automate this process. What should you do?

Create a Cloud Dataproc Workflow Template

Create an initialization action to execute the jobs

Create a Directed Acyclic Graph in Cloud Composer

Create a Bash script that uses the Cloud SDK to create a cluster, execute jobs, and then tear down the cluster

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Question # 27

You need to compose visualization for operations teams with the following requirements:

Telemetry must include data from all 50,000 installations for the most recent 6 weeks (sampling once every minute)

The report must not be more than 3 hours delayed from live data.

The actionable report should only show suboptimal links.

Most suboptimal links should be sorted to the top.

Suboptimal links can be grouped and filtered by regional geography.

User response time to load the report must be <5 seconds.

You create a data source to store the last 6 weeks of data, and create visualizations that allow viewers to see multiple date ranges, distinct geographic regions, and unique installation types. You always show the latest data without any changes to your visualizations. You want to avoid creating and updating new visualizations each month. What should you do?

Look through the current data and compose a series of charts and tables, one for each possible

combination of criteria.

Look through the current data and compose a small set of generalized charts and tables bound to criteria filters that allow value selection.

Export the data to a spreadsheet, compose a series of charts and tables, one for each possible

combination of criteria, and spread them across multiple tabs.

Load the data into relational database tables, write a Google App Engine application that queries all rows, summarizes the data across each criteria, and then renders results using the Google Charts and visualization API.

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Question # 28

You are managing a Cloud Dataproc cluster. You need to make a job run faster while minimizing costs, without losing work in progress on your clusters. What should you do?

Increase the cluster size with more non-preemptible workers.

Increase the cluster size with preemptible worker nodes, and configure them to forcefully decommission.

Increase the cluster size with preemptible worker nodes, and use Cloud Stackdriver to trigger a script to preserve work.

Increase the cluster size with preemptible worker nodes, and configure them to use graceful decommissioning.

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Question # 29

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?

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.

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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 # 30

Your car factory is pushing machine measurements as messages into a Pub/Sub topic in your Google Cloud project. A Dataflow streaming job. that you wrote with the Apache Beam SDK, reads these messages, sends acknowledgment lo Pub/Sub. applies some custom business logic in a Doffs instance, and writes the result to BigQuery. You want to ensure that if your business logic fails on a message, the message will be sent to a Pub/Sub topic that you want to monitor for alerting purposes. What should you do?

Use an exception handling block in your Data Flowâ€™s Doffs code to push the messages that failed to be transformed through a side output

and to a new Pub/Sub topic. Use Cloud Monitoring to monitor the topic/num_jnacked_messages_by_region metric on this new topic.

Enable retaining of acknowledged messages in your Pub/Sub pull subscription. Use Cloud Monitoring to monitor the

subscription/num_retained_acked_messages metric on this subscription.

Enable dead lettering in your Pub/Sub pull subscription, and specify a new Pub/Sub topic as the dead letter topic. Use Cloud Monitoring to

monitor the subscription/dead_letter_message_count metric on your pull subscription.

Create a snapshot of your Pub/Sub pull subscription. Use Cloud Monitoring to monitor the snapshot/numessages metric on this

snapshot.

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Question # 31

You need to create a near real-time inventory dashboard that reads the main inventory tables in your BigQuery data warehouse. Historical inventory data is stored as inventory balances by item and location. You have several thousand updates to inventory every hour. You want to maximize performance of the dashboard and ensure that the data is accurate. What should you do?

Leverage BigQuery UPDATE statements to update the inventory balances as they are changing.

Partition the inventory balance table by item to reduce the amount of data scanned with each inventory update.

Use the BigQuery streaming the stream changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.

Use the BigQuery bulk loader to batch load inventory changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.

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Question # 32

The marketing team at your organization provides regular updates of a segment of your customer dataset. The marketing team has given you a CSV with 1 million records that must be updated in BigQuery. When you use the UPDATE statement in BigQuery, you receive a quotaExceeded error. What should you do?

Reduce the number of records updated each day to stay within the BigQuery UPDATE DML statement limit.

Increase the BigQuery UPDATE DML statement limit in the Quota management section of the Google Cloud Platform Console.

Split the source CSV file into smaller CSV files in Cloud Storage to reduce the number of BigQuery UPDATE DML statements per BigQuery job.

Import the new records from the CSV file into a new BigQuery table. Create a BigQuery job that merges the new records with the existing records and writes the results to a new BigQuery table.

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