Google Professional Data Engineer Exam Practice Test Instant Access

Question 1

You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?

AUse Cloud SQL for storage. Add secondary indexes to support query patterns.

BUse Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.

CUse Cloud Spanner for storage. Add secondary indexes to support query patterns.

DUse Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.

Answer : D

Question 2

You need to copy millions of sensitive patient records from a relational database to BigQuery. The total size of the database is 10 TB. You need to design a solution that is secure and time-efficient. What should you do?

AExport the records from the database as an Avro file. Upload the file to GCS using gsutil, and then load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.

BExport the records from the database as an Avro file. Copy the file onto a Transfer Appliance and send it to Google, and then load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.

CExport the records from the database into a CSV file. Create a public URL for the CSV file, and then use Storage Transfer Service to move the file to Cloud Storage. Load the CSV file into BigQuery using the BigQuery web UI in the GCP Console.

DExport the records from the database as an Avro file. Create a public URL for the Avro file, and then use Storage Transfer Service to move the file to Cloud Storage. Load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.

Answer : A

Question 3

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?

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

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

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

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

Answer : B

https://cloud.google.com/logging/docs/view/logs-explorer-interface?cloudshell=true

Question 4

You are using Cloud Bigtable to persist and serve stock market data for each of the major indices. To serve the trading application, you need to access only the most recent stock prices that are streaming in How should you design your row key and tables to ensure that you can access the data with the most simple query?

ACreate one unique table for all of the indices, and then use the index and timestamp as the row key design

BCreate one unique table for all of the indices, and then use a reverse timestamp as the row key design.

CFor each index, have a separate table and use a timestamp as the row key design

DFor each index, have a separate table and use a reverse timestamp as the row key design

Answer : A

Question 5

You are using BigQuery and Data Studio to design a customer-facing dashboard that displays large quantities of aggregated dat

a. You expect a high volume of concurrent users. You need to optimize tie dashboard to provide quick visualizations with minimal latency. What should you do?

AUse BigQuery BI Engine with materialized views

BUse BigQuery BI Engine with streaming data.

CUse BigQuery Bl Engine with authorized views

DUse BigQuery Bl Engine with logical reviews

Answer : B

Question 6

Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?

AAn hourly watermark

BAn event time trigger

CThe with Allowed Lateness method

DA processing time trigger

Answer : D

When collecting and grouping data into windows, Beam uses triggers to determine when to emit the aggregated results of each window.

Processing time triggers. These triggers operate on the processing time -- the time when the data element is processed at any given stage in the pipeline.

Event time triggers. These triggers operate on the event time, as indicated by the timestamp on each data element. Beam's default trigger is event time-based.

Question 7

You want to store your team's shared tables in a single dataset to make data easily accessible to various analysts. You want to make this data readable but unmodifiable by analysts. At the same time, you want to provide the analysts with individual workspaces in the same project, where they can create and store tables for their own use, without the tables being accessible by other analysts. What should you do?

AGive analysts the BigQuery Data Viewer role at the project level Create one other dataset, and give the analysts the BigQuery Data Editor role on that dataset.

BGive analysts the BigQuery Data Viewer role at the project level Create a dataset for each analyst, and give each analyst the BigQuery Data Editor role at the project level.

CGive analysts the BigQuery Data Viewer role on the shared dataset. Create a dataset for each analyst, and give each analyst the BigQuery Data Editor role at the dataset level for their assigned dataset

DGive analysts the BigQuery Data Viewer role on the shared dataset Create one other dataset and give the analysts the BigQuery Data Editor role on that dataset.

Answer : C

The BigQuery Data Viewer role allows users to read data and metadata from tables and views, but not to modify or delete them. By giving analysts this role on the shared dataset, you can ensure that they can access the data for analysis, but not change it. The BigQuery Data Editor role allows users to create, update, and delete tables and views, as well as read and write data. By giving analysts this role at the dataset level for their assigned dataset, you can provide them with individual workspaces where they can store their own tables and views, without affecting the shared dataset or other analysts' datasets. This way, you can achieve both data protection and data isolation for your team.Reference:

BigQuery IAM roles and permissions

Basic roles and permissions

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