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

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Question # 33

Your new customer has requested daily reports that show their net consumption of Google Cloud compute resources and who used the resources. You need to quickly and efficiently generate these daily reports. What should you do?

Do daily exports of Cloud Logging data to BigQuery. Create views filtering by project, log type, resource, and user.

Filter data in Cloud Logging by project, resource, and user; then export the data in CSV format.

Filter data in Cloud Logging by project, log type, resource, and user, then import the data into BigQuery.

Export Cloud Logging data to Cloud Storage in CSV format. Cleanse the data using Dataprep, filtering by project, resource, and user.

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Question # 34

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 # 35

You use a dataset in BigQuery for analysis. You want to provide third-party companies with access to the same dataset. You need to keep the costs of data sharing low and ensure that the data is current. Which solution should you choose?

Create an authorized view on the BigQuery table to control data access, and provide third-party companies with access to that view.

Use Cloud Scheduler to export the data on a regular basis to Cloud Storage, and provide third-party companies with access to the bucket.

Create a separate dataset in BigQuery that contains the relevant data to share, and provide third-party companies with access to the new dataset.

Create a Cloud Dataflow job that reads the data in frequent time intervals, and writes it to the relevant BigQuery dataset or Cloud Storage bucket for third-party companies to use.

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Question # 36

Different teams in your organization store customer and performance data in BigOuery. Each team needs to keep full control of their collected data, be able to query data within their projects, and be able to exchange their data with other teams. You need to implement an organization-wide solution, while minimizing operational tasks and costs. What should you do?

Create a BigQuery scheduled query to replicate all customer data into team projects.

Enable each team to create materialized views of the data they need to access in their projects.

Ask each team to publish their data in Analytics Hub. Direct the other teams to subscribe to them.

Ask each team to create authorized views of their data. Grant the biquery. jobUser role to each team.

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Question # 37

You are loading CSV files from Cloud Storage to BigQuery. The files have known data quality issues, including mismatched data types, such as STRINGS and INT64s in the same column, and inconsistent formatting of values such as phone numbers or addresses. You need to create the data pipeline to maintain data quality and perform the required cleansing and transformation. What should you do?

Use Data Fusion to transform the data before loading it into BigQuery.

Load the CSV files into a staging table with the desired schema, perform the transformations with SQL. and then write the results to the final destination table.

Create a table with the desired schema, toad the CSV files into the table, and perform the transformations in place using SQL.

Use Data Fusion to convert the CSV files lo a self-describing data formal, such as AVRO. before loading the data to BigOuery.

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Question # 38

You have a BigQuery dataset named "customers". All tables will be tagged by using a Data Catalog tag template named "gdpr". The template contains one mandatory field, "has sensitive data~. with a boolean value. All employees must be able to do a simple search and find tables in the dataset that have either true or false in the "has sensitive data" field. However, only the Human Resources (HR) group should be able to see the data inside the tables for which "hass-ensitive-data" is true. You give the all employees group the bigquery.metadataViewer and bigquery.connectionUser roles on the dataset. You want to minimize configuration overhead. What should you do next?

Create the "gdpr" tag template with private visibility. Assign the bigquery -dataViewer role to the HR group on the tables that contain sensitive data.

Create the ~gdpr" tag template with private visibility. Assign the datacatalog. tagTemplateViewer role on this tag to the all employees

group, and assign the bigquery.dataViewer role to the HR group on the tables that contain sensitive data.

Create the "gdpr" tag template with public visibility. Assign the bigquery. dataViewer role to the HR group on the tables that contain

sensitive data.

Create the "gdpr" tag template with public visibility. Assign the datacatalog. tagTemplateViewer role on this tag to the all employees.

group, and assign the bijquery.dataViewer role to the HR group on the tables that contain sensitive data.

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Question # 39

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 # 40

You create a new report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. It is company policy to ensure employees can view only the data associated with their region, so you create and populate a table for each region. You need to enforce the regional access policy to the data.

Which two actions should you take? (Choose two.)

Ensure all the tables are included in global dataset.

Ensure each table is included in a dataset for a region.

Adjust the settings for each table to allow a related region-based security group view access.

Adjust the settings for each view to allow a related region-based security group view access.

Adjust the settings for each dataset to allow a related region-based security group view access.

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