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Professional-Data-Engineer Google Professional Data Engineer Exam Free Practice Exam Questions (2026 Updated)

Prepare effectively for your Google Professional-Data-Engineer Google Professional Data Engineer Exam certification with our extensive collection of free, high-quality practice questions. Each question is designed to mirror the actual exam format and objectives, complete with comprehensive answers and detailed explanations. Our materials are regularly updated for 2026, ensuring you have the most current resources to build confidence and succeed on your first attempt.

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Total 400 questions

MJTelco is building a custom interface to share data. They have these requirements:

They need to do aggregations over their petabyte-scale datasets.

They need to scan specific time range rows with a very fast response time (milliseconds).

Which combination of Google Cloud Platform products should you recommend?

A.

Cloud Datastore and Cloud Bigtable

B.

Cloud Bigtable and Cloud SQL

C.

BigQuery and Cloud Bigtable

D.

BigQuery and Cloud Storage

Your company has recently grown rapidly and now ingesting data at a significantly higher rate than it was previously. You manage the daily batch MapReduce analytics jobs in Apache Hadoop. However, the recent increase in data has meant the batch jobs are falling behind. You were asked to recommend ways the development team could increase the responsiveness of the analytics without increasing costs. What should you recommend they do?

A.

Rewrite the job in Pig.

B.

Rewrite the job in Apache Spark.

C.

Increase the size of the Hadoop cluster.

D.

Decrease the size of the Hadoop cluster but also rewrite the job in Hive.

You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

A.

Load the data every 30 minutes into a new partitioned table in BigQuery.

B.

Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery

C.

Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore

D.

Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.

Your company has a hybrid cloud initiative. You have a complex data pipeline that moves data between cloud provider services and leverages services from each of the cloud providers. Which cloud-native service should you use to orchestrate the entire pipeline?

A.

Cloud Dataflow

B.

Cloud Composer

C.

Cloud Dataprep

D.

Cloud Dataproc

Your car factory is pushing machine measurements as messages into a Pub/Sub topic in your Google Cloud project. A Dataflow streaming job. that you wrote with the Apache Beam SDK, reads these messages, sends acknowledgment lo Pub/Sub. applies some custom business logic in a Doffs instance, and writes the result to BigQuery. You want to ensure that if your business logic fails on a message, the message will be sent to a Pub/Sub topic that you want to monitor for alerting purposes. What should you do?

A.

Use an exception handling block in your Data Flow’s Doffs code to push the messages that failed to be transformed through a side outputand to a new Pub/Sub topic. Use Cloud Monitoring to monitor the topic/num_jnacked_messages_by_region metric on this new topic.

B.

Enable retaining of acknowledged messages in your Pub/Sub pull subscription. Use Cloud Monitoring to monitor thesubscription/num_retained_acked_messages metric on this subscription.

C.

Enable dead lettering in your Pub/Sub pull subscription, and specify a new Pub/Sub topic as the dead letter topic. Use Cloud Monitoring tomonitor the subscription/dead_letter_message_count metric on your pull subscription.

D.

Create a snapshot of your Pub/Sub pull subscription. Use Cloud Monitoring to monitor the snapshot/numessages metric on thissnapshot.

You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)

A.

Increase the number of max workers

B.

Use a larger instance type for your Cloud Dataflow workers

C.

Change the zone of your Cloud Dataflow pipeline to run in us-central1

D.

Create a temporary table in Cloud Bigtable that will act as a buffer for new data. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery

E.

Create a temporary table in Cloud Spanner that will act as a buffer for new data. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery

You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application’s interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.

What should you do?

A.

Create groups for your users and give those groups access to the dataset

B.

Integrate with a single sign-on (SSO) platform, and pass each user’s credentials along with the queryrequest

C.

Create a service account and grant dataset access to that account. Use the service account’s private key to access the dataset

D.

Create a dummy user and grant dataset access to that user. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset

You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:

The user profile: What the user likes and doesn’t like to eat

The user account information: Name, address, preferred meal times

The order information: When orders are made, from where, to whom

The database will be used to store all the transactional data of the product. You want to optimize the data schema. Which Google Cloud Platform product should you use?

A.

BigQuery

B.

Cloud SQL

C.

Cloud Bigtable

D.

Cloud Datastore

You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?

A.

Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.

B.

Add a new column called FullName to the Users table. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.

C.

Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.

D.

Use BigQuery to export the data for the table to a CSV file. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullName. Run a BigQuery load job to load the new CSV file into BigQuery.

You have a petabyte of analytics data and need to design a storage and processing platform for it. You must be able to perform data warehouse-style analytics on the data in Google Cloud and expose the dataset as files for batch analysis tools in other cloud providers. What should you do?

A.

Store and process the entire dataset in BigQuery.

B.

Store and process the entire dataset in Cloud Bigtable.

C.

Store the full dataset in BigQuery, and store a compressed copy of the data in a Cloud Storage bucket.

D.

Store the warm data as files in Cloud Storage, and store theactive data inBigQuery. Keep this ratio as 80% warm and 20% active.

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.

Configure your Cloud Dataflow pipeline to use local execution

B.

Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions

C.

Increase the number of nodes in the Cloud Bigtable cluster

D.

Modify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable

E.

Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable

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, andinconsistent 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.

Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?

A.

The CSV data loaded in BigQuery is not flagged as CSV.

B.

The CSV data has invalid rows that were skipped on import.

C.

The CSV data loaded in BigQuery is not using BigQuery’s default encoding.

D.

The CSV data has not gone through an ETL phase before loading into BigQuery.

Your company is migrating its on-premises data warehousing solution to BigQuery. The existing data warehouse uses trigger-based change data capture (CDC) to apply daily updates from transactional database sources Your company wants to use BigQuery to improve its handling of CDC and to optimize the performance of the data warehouse Source system changes must be available for query m near-real time using tog-based CDC streams You need to ensure that changes in the BigQuery reporting table are available with minimal latency and reduced overhead. What should you do? Choose 2 answers

A.

Perform a DML INSERT UPDATE, or DELETE to replicate each CDC record in the reporting table m real time.

B.

Periodically DELETE outdated records from the reporting tablePeriodically use a DML MERGE to simultaneously perform DML INSERT. UPDATE, and DELETE operations in the reporting table

C.

Insert each new CDC record and corresponding operation type into a staging table in real time

D.

Insert each new CDC record and corresponding operation type into the reporting table in real time and use a materialized view to expose only the current version of each unique record.

You orchestrate ETL pipelines by using Cloud Composer One of the tasks in the Apache Airflow directed acyclic graph (DAG) relies on a third-party service. You want to be notified when the task does not succeed. What should you do?

A.

Configure a Cloud Monitoring alert on the sla_missed metric associated with the task at risk to trigger a notification.

B.

Assign a function with notification logic to the sla_miss_callback parameter for the operator responsible for the task at risk.

C.

Assign a function with notification logic to the on_retry_callback parameter for the operator responsible for the task at risk.

D.

Assign a function with notification logic to the on_failure_callback parameter for the operator responsible for the task at risk.

You operate a logistics company, and you want to improve event delivery reliability for vehicle-based sensors. You operate small data centers around the world to capture these events, but leased lines that provide connectivity from your event collection infrastructure to your event processing infrastructure are unreliable, with unpredictable latency. You want to address this issue in the most cost-effective way. What should you do?

A.

Deploy small Kafka clusters in your data centers to buffer events.

B.

Have the data acquisition devices publish data to Cloud Pub/Sub.

C.

Establish a Cloud Interconnect between all remote data centers and Google.

D.

Write a Cloud Dataflow pipeline that aggregates all data in session windows.

Government regulations in your industry mandate that you have to maintain an auditable record of access to certain types of datA. Assuming that all expiring logs will be archived correctly, where should you store data that is subject to that mandate?

A.

Encrypted on Cloud Storage with user-supplied encryption keys. A separate decryption key will be given to each authorized user.

B.

In a BigQuery dataset that is viewable only by authorized personnel, with the Data Access log used toprovide the auditability.

C.

In Cloud SQL, with separate database user names to each user. The Cloud SQL Admin activity logs will be used to provide the auditability.

D.

In a bucket on Cloud Storage that is accessible only by an AppEngine service that collects user information and logs the access before providing a link to the bucket.

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.

You are testing a Dataflow pipeline to ingest and transform text files. The files are compressed gzip, errors are written to a dead-letter queue, and you are using Sidelnputs to join data You noticed that the pipeline is taking longer to complete than expected, what should you do to expedite the Dataflow job?

A.

Switch to compressed Avro files

B.

Reduce the batch size

C.

Retry records that throw an error

D.

Use CoGroupByKey instead of the Sidelnput

You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients’ personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?

A.

Create an authorized view in BigQuery to restrict access to tables with sensitive data.

B.

Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.

C.

Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.

D.

Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention API. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.

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Total 400 questions
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