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 04, 2026

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Question 1

You stream order data by using a Dataflow pipeline, and write the aggregated result to Memorystore. You

provisioned a Memorystore for Redis instance with Basic Tier. 4 GB capacity, which is used by 40 clients for

read-only access. You are expecting the number of read-only clients to increase significantly to a few hundred

and you need to be able to support the demand. You want to ensure that read and write access availability is

not impacted, and any changes you make can be deployed quickly. What should you do?

A : Create multiple new Memorystore for Redis instances with Basic Tier (4 GB capacity) Modify theDataflow pipeline and new clients to use all instances

B : Create a new Memorystore for Redis instance with Standard Tier Set capacity to 4 GB and read replicato No read replicas (high availability only). Delete the old instance.

C : Create a new Memorystore for Memcached instance Set a minimum of three nodes, and memory pernode to 4 GB. Modify the Dataflow pipeline and all clients to use the Memcached instance Delete theold instance.

D : Create a new Memorystore for Redis instance with Standard Tier Set capacity to 5 GB and createmultiple read replicas Delete the old instance.

Answer: D

Question 2

You work for a large ecommerce company. You are using Pub/Sub to ingest the clickstream data to Google Cloud for analytics. You observe that when a new subscriber connects to an existing topic to analyze data, they are unable to subscribe to older data for an upcoming yearly sale event in two months, you need a solution that, once implemented, will enable any new subscriber to read the last 30 days of data. What should you do?

A : Create a new topic, and publish the last 30 days of data each time a new subscriber connects to an existing topic.

B : Set the topic retention policy to 30 days.

C : Set the subscriber retention policy to 30 days.

D : Ask the source system to re-push the data to Pub/Sub, and subscribe to it.

Answer: B

Question 3

A manufacturer of delivery drones is implementing a new data analysis pipeline to detect part failures before they occur. The drones have multiple sensors that send performance and environment data to an analytics pipeline. Currently, data is sent to a REST API endpoint. The REST API endpoint that receives data cannot always keep up with the pace data is arriving. When that happens, data is lost. Machine learning engineers have asked you to change the ingestion process to reduce this data loss. What would you do?

A : Write data to a Cloud Pub/Sub topic instead of a REST API endpoint and have the ingestion application read from the topic.

B : Write data to a Cloud Storage bucket instead of a REST API endpoint and have the ingestion application read from the bucket.

C : Write data to a Cloud SQL Postgres database endpoint and have the ingestion application query the database.

D : Create a Hadoop cluster in Compute Engine using managed instance groups and write data to an Hbase database and have the application query the database.

Answer: A

Question 4

MJTelco’s Google Cloud Dataflow pipeline is now ready to start receiving data from the 50,000 installations. You want to allow Cloud Dataflow to scale its compute power up as required. Which Cloud Dataflow pipeline configuration setting should you update?

A : zone

B : number of workers

C : disk size per worker

D : maximum number of workers

Answer: A

Question 5

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?

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

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

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

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

Answer: B

Page: 1 / 95

Total 472 Questions | Updated On: Jan 04, 2026

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