Free Access Google.Professional-Data-Engineer.v2025-09-04.q126 Practice Test (Page 2)

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 non-key columns. What should you do?

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

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

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

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

Question 2

Your company built a TensorFlow neutral-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly.
What method can you employ to address this?

A.Threading

B.Serialization

C.Dropout Methods

D.Dimensionality Reduction

Question 3

You need to set access to BigQuery for different departments within your company. Your solution should comply with the following requirements:
* Each department should have access only to their data.
* Each department will have one or more leads who need to be able to create and update tables and provide them to their team.
* Each department has data analysts who need to be able to query but not modify data.
How should you set access to the data in BigQuery?

A.Create a table for each department. Assign the department leads the role of Owner, and assign the data analysts the role of Editor on the project the table is in.

B.Create a dataset for each department. Assign the department leads the role of OWNER, and assign the data analysts the role of WRITER on their dataset.

C.Create a table for each department. Assign the department leads the role of Editor, and assign the data analysts the role of Viewer on the project the table is in.

D.Create a dataset for each department. Assign the department leads the role of WRITER, and assign the data analysts the role of READER on their dataset.

Question 4

You plan to deploy Cloud SQL using MySQL. You need to ensure high availability in the event of a zone failure. What should you do?

A.Create a Cloud SQL instance in a region, and configure automatic backup to a Cloud Storage bucket in the same region.

B.Create a Cloud SQL instance in one zone, and create a failover replica in another zone within the same region.

C.Create a Cloud SQL instance in one zone, and configure an external read replica in a zone in a different region.

D.Create a Cloud SQL instance in one zone, and create a read replica in another zone within the same region.

Question 5

You have spent a few days loading data from comma-separated values (CSV) files into the Google BigQuery table CLICK_STREAM. The column DT stores the epoch time of click events. For convenience, you chose a simple schema where every field is treated as the STRING type. Now, you want to compute web session durations of users who visit your site, and you want to change its data type to the TIMESTAMP. You want to minimize the migration effort without making future queries computationally expensive. What should you do?

A.Delete the table CLICK_STREAM, and then re-create it such that the column DT is of the TIMESTAMP type. Reload the data.

B.Add a column TS of the TIMESTAMP type to the table CLICK_STREAM, and populate the numeric values from the column TS for each row. the column TS instead of the column DT from now on.

C.Create a view CLICK_STREAM_V, where strings from the column DT are cast into TIMESTAMP values. the view CLICK_STREAM_V instead of the table CLICK_STREAM from now on.

D.Add two columns to the table CLICK STREAM: TS of the TIMESTAMP type and IS_NEW of the BOOLEAN type. Reload all data in append mode. For each appended row, set the value of IS_NEW to true. For future queries, the column TS instead of the column DT, with the WHERE clause ensuring that the value of IS_NEW must be true.

E.Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to cast strings from the column DT into TIMESTAMP values. Run the query into a destination table NEW_CLICK_STREAM, in which the column TS is the TIMESTAMP type. the table NEW_CLICK_STREAM instead of the table CLICK_STREAM from now on. In the future, new data is loaded into the table NEW_CLICK_STREAM.

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