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

You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30–90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

A : -create the tables using DDL. Partition the tables by a column containing a TIMESTAMP or DATE Type.

B : commend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.

C : dify your pipeline to maintain the last 30–90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.

D : ite an Apache Beam pipeline that creates a BigQuery table per day. Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.

Answer: C

Question 2

You operate a database that stores stock trades and an application that retrieves average stock price for a given company over an adjustable window of time. The data is stored in Cloud Bigtable where the datetime of the stock trade is the beginning of the row key. Your application has thousands of concurrent users, and you notice that performance is starting to degrade as more stocks are added. What should you do to improve the performance of your application?

A : ange the row key syntax in your Cloud Bigtable table to begin with the stock symbol.

B : ange the row key syntax in your Cloud Bigtable table to begin with a random number per second.

C : ange the data pipeline to use BigQuery for storing stock trades, and update your application.

D : Cloud Dataflow to write summary of each day’s stock trades to an Avro file on Cloud Storage. Update your application to read from Cloud Storage and Cloud Bigtable to compute the responses.

Answer: A

Question 3

You have a data processing application that runs on Google Kubernetes Engine (GKE). Containers need to be

launched with their latest available configurations from a container registry. Your GKE nodes need to have

GPUs. local SSDs, and 8 Gbps bandwidth. You want to efficiently provision the data processing infrastructure

and manage the deployment process. What should you do?

A : Use Compute Engi.no startup scriots to pull container Images, and use gloud commands to provision theinfrastructure.

B : Use GKE to autoscale containers, and use gloud commands to provision the infrastructure.

C : Use Cloud Build to schedule a job using Terraform build to provision the infrastructure and launch with the most current container images.

D : Use Dataflow to provision the data pipeline, and use Cloud Scheduler to run the job.

Answer: C

Question 4

You are responsible for writing your company’s ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?

A : gLatin using Pig

B : veQL using Hive

C : va using MapReduce

D : thon using MapReduce

Answer: D

Question 5

Your company is currently setting up data pipelines for their campaign. For all the Google Cloud Pub/Sub streaming data, one of the important business requirements is to be able to periodically identify the inputs and their timings during their campaign. Engineers have decided to use windowing and transformation in Google Cloud Dataflow for this purpose. However, when testing this feature, they find that the Cloud Dataflow job fails for the all streaming insert. What is the most likely cause of this problem?

A : ey have not assigned the timestamp, which causes the job to fail

B : ey have not set the triggers to accommodate the data coming in late, which causes the job to fail

C : ey have not applied a global windowing function, which causes the job to fail when the pipeline is created

D : ey have not applied a non-global windowing function, which causes the job to fail when the pipeline is created

Answer: C

Page: 1 / 95

Total 472 Questions | Updated On: Feb 19, 2026

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