Free Google Professional-Data-Engineer Exam Questions

Become Google Certified with updated Professional-Data-Engineer exam questions and correct answers

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Total 472 Questions | Updated On: Jan 28, 2026

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Question 1

You are designing a data warehouse in BigQuery to analyze sales data for a telecommunication service

provider. You need to create a data model for customers, products, and subscriptions All customers, products,

and subscriptions can be updated monthly, but you must maintain a historical record of all data. You plan to

use the visualization layer for current and historical reporting. You need to ensure that the data model is

simple, easy-to-use. and cost-effective. What should you do?

A : Create a normalized model with tables for each entity. Use snapshots before updates to track historical data

B : Create a normalized model with tables for each entity. Keep all input files in a Cloud Storage bucket to track historical data

C : Create a denormalized model with nested and repeated fields Update the table and use snapshots to track historical data

D : Create a denormalized, append-only model with nested and repeated fields Use the ingestion timestamp to track historical data.

Answer: D

Question 2

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?

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

B : 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.

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

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

Answer: A

Question 3

You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

A : a SideInput that returns a Boolean if the element is corrupt.

B : a ParDo transform in Cloud Dataflow to discard corrupt elements

C : a Partition transform in Cloud Dataflow to separate valid data from corrupt data.

D : a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.

Answer: B

Question 4

You want to archive data in Cloud Storage. Because some data is very sensitive, you want to use the “Trust No One” (TNO) approach to encrypt your data to prevent the cloud provider staff from decrypting your data. What should you do?

A : gcloud kms keys create to create a symmetric key. Then use gcloud kms encrypt to encrypt each archival file with the key and unique additional authenticated data (AAD). Use gsutil cp to upload each encrypted file to the Cloud Storage bucket, and keep the AAD outside of Google Cloud.

B : gcloud kms keys create to create a symmetric key. Then use gcloud kms encrypt to encrypt each archival file with the key. Use gsutil cp to upload each encrypted file to the Cloud Storage bucket. Manually destroy the key previously used for encryption, and rotate the key once and rotate the key once.

C : ecify customer-supplied encryption key (CSEK) in the .boto configuration file. Use gsutil cp to upload each archival file to the Cloud Storage bucket. Save the CSEK in Cloud Memorystore as permanent storage of the secret.

D : ecify customer-supplied encryption key (CSEK) in the .boto configuration file. Use gsutil cp to upload each archival file to the Cloud Storage bucket. Save the CSEK in a different project that only the security team can access.

Answer: B

Question 5

Which methods can be used to reduce the number of rows processed by BigQuery?

A : litting tables into multiple tables; putting data in partitions

B : litting tables into multiple tables; putting data in partitions; using the LIMIT clause

C : tting data in partitions; using the LIMIT clause

D : litting tables into multiple tables; using the LIMIT clause

Answer: A

Page: 1 / 95

Total 472 Questions | Updated On: Jan 28, 2026

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