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: Feb 19, 2026

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Question 1

A insurance claim review company provides expert opinion on contested insurance claims. The company uses Google Cloud for it's data analysis pipelines. Clients of the company upload documents to Cloud Storage. When a file is uploaded, the company wants to immediately move the files to a Classified Data bucket if the file contains personally identifying information. What method would you recommend to accomplish this?

A : Create a quarantine bucket for uploading, once a file is uploaded trigger a Cloud Function to call a custom built machine learning model trained to detect PII. If PII is detected, move the file to the Classified Data bucket.

B : Create a quarantine bucket for uploading, once a file is uploaded trigger a Cloud Function to call the Data Loss Prevention API to apply infotypes to detect PII. If PII is detected, move file to the Classified Data bucket.

C : Create a quarantine bucket for uploading, use Cloud Scheduler to run a job to run hourly that will call a custom built machine learning model trained to detect PII. If PII is detected, move file to the Classified Data bucket.

D : Create a quarantine bucket for uploading, use Cloud Scheduler to run a job to run hourly that will call the Data Loss Prevention API to apply infotypes to detect PII. If PII is detected, move file to the Classified Data bucket.

Answer: B

Question 2

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?

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

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

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

D : eate 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.

Answer: B

Question 3

You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the data. Which two actions should you take? (Choose two.)

A : nfigure your Cloud Dataflow pipeline to use local execution

B : crease the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions

C : crease the number of nodes in the Cloud Bigtable cluster

D : dify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable

E : dify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable

Answer: A,D

Question 4

Which of these statements about exporting data from BigQuery is false?

A : export more than 1 GB of data, you need to put a wildcard in the destination filename.

B : only supported export destination is Google Cloud Storage.

C : ta can only be exported in JSON or Avro format.

D : only compression option available is GZIP.

Answer: C

Question 5

A shipping company has live package-tracking data that is sent to an Apache Kafka stream in real time. This is then loaded into BigQuery. Analysts in your company want to query the tracking data in BigQuery to analyze geospatial trends in the lifecycle of a package. The table was originally created with ingest-date partitioning. Over time, the query processing time has increased. You need to implement a change that would improve query performance in BigQuery. What should you do?

A : plement clustering in BigQuery on the ingest date column.

B : plement clustering in BigQuery on the package-tracking ID column.

C : er older data onto Cloud Storage files, and leverage extended tables.

D : -create the table using data partitioning on the package delivery date.

Answer: A

Page: 1 / 95

Total 472 Questions | Updated On: Feb 19, 2026

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