Spring Sale Special Limited Time 70% Discount Offer - Ends in 0d 00h 00m 00s - Coupon code: xmas50

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

Google Professional-Data-Engineer Premium Access     Download Demo    
Page: 2 / 6
Total 400 questions

You work for a bank. You have a labelled dataset that contains information on already granted loan application and whether these applications have been defaulted. You have been asked to train a model to predict default rates for credit applicants.

What should you do?

A.

Increase the size of the dataset by collecting additional data.

B.

Train a linear regression to predict a credit default risk score.

C.

Remove the bias from the data and collect applications that have been declined loans.

D.

Match loan applicants with their social profiles to enable feature engineering.

You are working on a niche product in the image recognition domain. Your team has developed a model that is dominated by custom C++ TensorFlow ops your team has implemented. These ops are used inside your main training loop and are performing bulky matrix multiplications. It currently takes up to several days to train a model. You want to decrease this time significantly and keep the cost low by using an accelerator on Google Cloud. What should you do?

A.

Use Cloud TPUs without any additional adjustment to your code.

B.

Use Cloud TPUs after implementing GPU kernel support for your customs ops.

C.

Use Cloud GPUs after implementing GPU kernel support for your customs ops.

D.

Stay on CPUs, and increase the size of the cluster you’re training your model on.

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?

A.

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

B.

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

C.

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.

D.

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.

Flowlogistic is rolling out their real-time inventory tracking system. The tracking devices will all send package-tracking messages, which will now go to a single Google Cloud Pub/Sub topic instead of the Apache Kafka cluster. A subscriber application will then process the messages for real-time reporting and store them in Google BigQuery for historical analysis. You want to ensure the package data can be analyzed over time.

Which approach should you take?

A.

Attach the timestamp on each message in the Cloud Pub/Sub subscriber application as they are received.

B.

Attach the timestamp and Package ID on the outbound message from each publisher device as they are sent to Clod Pub/Sub.

C.

Use the NOW () function in BigQuery to record the event’s time.

D.

Use the automatically generated timestamp from Cloud Pub/Sub to order the data.

Flowlogistic’s CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they’ve purchased a visualization tool to simplify the creation of BigQuery reports. However, they’ve been overwhelmed by all thedata in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

A.

Export the data into a Google Sheet for virtualization.

B.

Create an additional table with only the necessary columns.

C.

Create a view on the table to present to the virtualization tool.

D.

Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.

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.)

A.

Ensure all the tables are included in global dataset.

B.

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

C.

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

D.

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

E.

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

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

Which approach meets the requirements?

A.

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

B.

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.

C.

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.

D.

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.

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 day’s events. They also want to use streaming ingestion. What should you do?

A.

Create a table called tracking_table and include a DATE column.

B.

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

C.

Create sharded tables for each day following the pattern tracking_table_YYYYMMDD.

D.

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

MJTelco is building a custom interface to share data. They have these requirements:

They need to do aggregations over their petabyte-scale datasets.

They need to scan specific time range rows with a very fast response time (milliseconds).

Which combination of Google Cloud Platform products should you recommend?

A.

Cloud Datastore and Cloud Bigtable

B.

Cloud Bigtable and Cloud SQL

C.

BigQuery and Cloud Bigtable

D.

BigQuery and Cloud Storage

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?

A.

Rowkey: date#device_idColumn data: data_point

B.

Rowkey: dateColumn data: device_id, data_point

C.

Rowkey: device_idColumn data: date, data_point

D.

Rowkey: data_pointColumn data: device_id, date

E.

Rowkey: date#data_pointColumn data: device_id