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

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Question # 49

For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.

Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.

Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.

Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.

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Question # 50

When creating a new Cloud Dataproc cluster with the projects.regions.clusters.create operation, these four values are required: project, region, name, and ____.

zone

node

label

type

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Question # 51

What are two of the benefits of using denormalized data structures in BigQuery?

Reduces the amount of data processed, reduces the amount of storage required

Increases query speed, makes queries simpler

Reduces the amount of storage required, increases query speed

Reduces the amount of data processed, increases query speed

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Question # 52

You want to use Google Stackdriver Logging to monitor Google BigQuery usage. You need an instant notification to be sent to your monitoring tool when new data is appended to a certain table using an insert job, but you do not want to receive notifications for other tables. What should you do?

Make a call to the Stackdriver API to list all logs, and apply an advanced filter.

In the Stackdriver logging admin interface, and enable a log sink export to BigQuery.

In the Stackdriver logging admin interface, enable a log sink export to Google Cloud Pub/Sub, and subscribe to the topic from your monitoring tool.

Using the Stackdriver API, create a project sink with advanced log filter to export to Pub/Sub, and subscribe to the topic from your monitoring tool.

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Question # 53

Given the record streams MJTelco is interested in ingesting per day, they are concerned about the cost of Google BigQuery increasing. MJTelco asks you to provide a design solution. They require a single large data table called tracking_table. Additionally, they want to minimize the cost of daily queries while performing fine-grained analysis of each dayâ€™s events. They also want to use streaming ingestion. What should you do?

Create a table called tracking_table and include a DATE column.

Create a partitioned table called tracking_table and include a TIMESTAMP column.

Create sharded tables for each day following the pattern tracking_table_YYYYMMDD.

Create a table called tracking_table with a TIMESTAMP column to represent the day.

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Question # 54

MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?

Rowkey: date#device_idColumn data: data_point

Rowkey: dateColumn data: device_id, data_point

Rowkey: device_idColumn data: date, data_point

Rowkey: data_pointColumn data: device_id, date

Rowkey: date#data_pointColumn data: device_id

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Question # 55

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

Which approach meets the requirements?

Load the data into Google Sheets, use formulas to calculate a metric, and use filters/sorting to show only suboptimal links in a table.

Load the data into Google BigQuery tables, write Google Apps Script that queries the data, calculates the metric, and shows only suboptimal rows in a table in Google Sheets.

Load the data into Google Cloud Datastore tables, write a Google App Engine Application that queries all rows, applies a function to derive the metric, and then renders results in a table using the Google charts and visualization API.

Load the data into Google BigQuery tables, write a Google Data Studio 360 report that connects to your data, calculates a metric, and then uses a filter expression to show only suboptimal rows in a table.

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Question # 56

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 possiblecombination 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 possiblecombination 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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