GCP Professional Data Engineer Sample Practice Exam Questions -PDE - 001 ( 2025 )

Boost your success rate in the Google Cloud Certified Professional Data Engineer exam ( PDE - 001 ) with this set of GCP Professional Data Engineer Sample Questions tailored to the latest GCP blueprint. These practice questions replicate real exam conditions and are ideal for anyone preparing with GCP Professional Data Engineer dumps, full-length practice exams, or Google Cloud Data Engineer certification tests. Covering data processing, ML models, storage, and security, these exam questions build hands-on skills required for the actual test. Whether you’re reviewing GCP Data Engineer exam dumps or practicing via mock tests, this guide strengthens your fundamentals and exam strategy to pass with confidence on your first try.

GCP Professional Data Engineer Sample Questions List :

1. You're working as a data engineer for an e-commerce company that needs to process large amounts of real-time and batch data. The company's goal is to build a machine learning model based on the historical and real-time data to predict customer purchasing behavior. Which GCP service would be the best choice for this use case?

1. Pub/Sub

2. BigQuery

3. Dataflow

4. Cloud Storage

2. Your organization uses Google Cloud Storage (GCS) extensively for storing various types of data, including logs, images, and documents. With the growing data, the storage costs are increasing. You need to optimize these costs without affecting data accessibility. What should you do?

1. Compress all data stored in GCS to reduce size and cost.

2. Migrate all data to the Standard Storage class to ensure uniformity.

3. Delete all data that has not been accessed in the last 30 days.

4. Implement object lifecycle policies to transition data to Nearline, Coldline, or Archive Storage based on access patterns.

3. Your organization has recently migrated their on-premises Hadoop cluster to Google Cloud's Dataproc. The initial data migration was handled by the Transfer Appliance and the subsequent updates are managed by Cloud Dataflow. As a data engineer, you need to validate that the migration was successful and the processing on Dataproc mirrors that of the on-premises Hadoop setup. What's the best approach?

1. Recreate the Hadoop cluster on another region in GCP and compare the results.

2. Use Cloud Logging to compare the system logs of both the environments.

3. Run the same processing tasks on both Hadoop and Dataproc and compare the outputs.

4. Compare the overall size of data in Hadoop and Dataproc.

5. Use Cloud Monitoring to check the CPU utilization of Dataproc matches with the on-premises Hadoop.

4. Your organization requires a high throughput system that will handle billions of events per day, sent from thousands of IoT devices. Messages need to be processed as they are received, in real-time, and the system should be capable of triggering specific serverless functions based on the type of event. The design should prioritize system scalability, real-time processing and ensure reliable message delivery. As a data engineer, what architecture would you recommend?

1. Use Cloud Storage for message delivery, and Cloud Run to trigger serverless actions based on the messages.

2. Use Cloud Bigtable for real-time message delivery, and App Engine for serverless actions.

3. Use Cloud Pub/Sub for message delivery, and Cloud Functions to trigger serverless actions based on the messages.

4. Use Cloud Dataflow for real-time message delivery, and Cloud Functions for serverless actions.

5. You are developing a data pipeline for a company that needs to process incoming data from IoT devices. The pipeline must be highly available, support instant failover, and handle millions of events per second with low latency. The data must be processed in order and potential duplication should be minimized. Which of the following Google Cloud services should be used to design the system?

1. Cloud Pub/Sub and Cloud Dataflow

2. BigQuery and Cloud Dataprep

3. Cloud Datastore and App Engine

4. Cloud Pub/Sub and Cloud Functions

6. You're a data engineer in a financial organization. The company has built a machine learning model for fraud detection, deployed on Google AI Platform. The model needs continuous evaluation since fraudulent patterns can evolve over time. The prediction input and output are saved in BigQuery. Which approach should you use for continuous evaluation?

1. Use Cloud Composer to schedule a workflow that compares the model's predictions with actual outcomes daily.

2. Use Cloud Scheduler to trigger BigQuery ML to evaluate the model's performance daily.

3. Use Cloud Functions to evaluate the model's performance every time a prediction is made.

4. Use Data Studio to create a report that compares the model's predictions with actual outcomes.

7. As a data engineer, you've been tasked with setting up a pipeline to ingest large volumes of raw data from IoT devices into Google Cloud. The pipeline involves Pub/Sub for data ingestion, Dataflow for processing, and BigQuery for storage and analysis. Data reliability and fidelity are crucial for the system. To ensure this, what should be your primary strategy?

1. Use Cloud Functions instead of Dataflow for data processing, as they can handle any volume of data.

2. Increase the number of BigQuery slots to ensure that all incoming data can be processed immediately.

3. Implement a retry mechanism in Pub/Sub to ensure that no data is lost during the ingestion process.

4. Implement a real-time data quality check within the Dataflow pipeline to identify and handle anomalies.

8. Your organization handles a large amount of unstructured data including images, video, and raw text files. The data is stored in Google Cloud Storage (GCS) and is accessed infrequently, but when needed, it requires quick retrieval times. Your organization is looking to cut down costs on GCS without compromising on retrieval time. Which of the following options should you suggest?

1. Switch from Standard storage to Nearline storage

2. Switch from Standard storage to multi-region storage

3. Switch from Standard storage to Archive storage

4. Switch from Standard storage to Coldline storage

9. You are a data engineer in a healthcare organization. Your organization wants to predict disease outbreak in different geographical regions. You have a huge amount of unstructured data (patient notes, doctor reports, etc.) and limited time. You've decided to leverage Google Cloud's pre-built ML models to handle this task. Which Google Cloud service would you choose?

1. AutoML Tables

2. Natural Language API

3. Vision API

4. Speech-to-Text API

10. Your company uses Google BigQuery for analyzing large datasets. The current query execution times are longer than expected, impacting report generation. You need to optimize query performance while keeping costs in check. Which of the following strategies should you adopt?

1. Store all data in a single large table to avoid joins.

2. Implement more JOIN operations to distribute the load across multiple tables.

3. Partition tables based on a suitable column and use partition pruning in queries.

4. Use BigQuery Reservations to allocate more slots to your project.

11. Your company developed a machine learning model for facial recognition to be used in a security system with cameras deployed in multiple remote locations with limited internet connectivity. The model should make predictions at the edge due to latency and bandwidth concerns. Which of the following serving infrastructures would be most suitable for this requirement?

1. Serve the model using Google Cloud AI Platform Prediction with a standard machine type.

2. Serve the model using Cloud Functions with the model stored in Google Cloud Storage.

3. Serve the model using Cloud Run with the model stored in Google Cloud Storage.

4. Use TensorFlow Lite to convert the model and deploy it on the edge devices.

12. Your company is receiving real-time IoT device data from various geographic locations. The device data includes structured telemetry data and unstructured video streams. This data needs to be processed, stored for real-time and historical analytics, and occasional ML modeling. Which of the following designs would best handle these requirements?

1. Use Cloud IoT Core to ingest both telemetry data and video streams, Cloud Dataflow for processing, BigQuery for analytics, and AI Platform for ML modeling.

2. Use Cloud Pub/Sub to ingest telemetry data, Cloud Storage for video streams, Cloud Dataflow for processing, and BigQuery for analytics.

3. Use Cloud IoT Core to ingest telemetry data, Cloud Storage for video streams, Cloud Dataflow for processing, and BigQuery for analytics.

4. Use Cloud Pub/Sub to ingest both telemetry data and video streams, Cloud Dataflow for processing, and BigQuery for analytics and ML.

13. As a data engineer, you have built a real-time analytics pipeline using Pub/Sub for data ingestion, Dataflow for processing, and BigQuery for analysis. The system must have high reliability and fidelity and be capable of recovering from failures. What approach should you take for data recovery and fault tolerance in this scenario?

1. Increase the number of Dataflow worker instances to ensure high availability.

2. Create duplicate pipelines and switch to the secondary pipeline in case of failure.

3. Enable Dataflow's built-in fault-tolerance features, ensure data retention in Pub/Sub, and regularly run failed jobs.

4. Regularly backup all the raw data in Cloud Storage and restore from there in case of failures.

14. Your organization has a PostgreSQL database hosted on-premises, supporting a critical application. The database is around 8 TB in size and has moderate growth. You want to migrate it to Google Cloud to improve scalability and manageability while controlling costs. What should you do?

1. Migrate the database to Cloud Bigtable.

2. Migrate the database to Cloud Spanner.

3. Migrate the database to Cloud SQL for PostgreSQL.

4. Migrate the database to Firestore.

15. You are developing a chatbot for an international travel agency. The chatbot should be able to interact with customers, understand their travel inquiries, and suggest appropriate travel packages. The chatbot should also be able to converse in multiple languages. Which Google Cloud service would be the most suitable for this task?

1. Cloud Natural Language API

2. AutoML Text Classification

3. Dialogflow coupled with Cloud Translation API

4. AutoML Translation

16. You are working on a data engineering project where you need to ingest streaming data and perform real-time analysis. The data comes in high volumes, and the processing needs to scale based on the data volume. You have chosen to use Google Cloud Platform for this project. What should you do to meet these requirements?

1. Use Cloud Storage for data ingestion and Dataproc for real-time processing.

2. Use BigQuery alone for both data ingestion and real-time processing.

3. Use Cloud SQL for data ingestion and Dataflow for real-time processing.

4. Use Cloud Pub/Sub for data ingestion and Cloud Dataflow for real-time processing.

17. You are working with a globally distributed team on a data science project. The project's datasets are stored in a regional Google Cloud Storage bucket in the US. You've noticed that your colleagues in Asia and Europe are experiencing latency when accessing the datasets. To ensure scalability and efficiency in data access, which of the following approaches should you implement?

1. Move all data to a local server in each region

2. Increase the number of instances in the Google Kubernetes Engine

3. Replicate the data to multiple regional buckets and use Cloud Load Balancer for routing

4. Use a multi-regional storage class for your bucket

18. You are working with a global organization that uses an on-premises SQL Server data warehouse with 500 TB of data. The company wants to migrate its data warehouse to Google Cloud with minimal downtime and wants a solution that is cost-effective, provides high availability, durability, and near real-time analysis. What should be your recommended approach?

1. Use Datastream for the initial data migration, and Cloud Bigtable for analysis.

2. Use Cloud SQL with customer-managed encryption keys for the migration.

3. Use the Transfer Appliance for the initial load, then load the data into BigQuery.

4. Use Transfer Service for on-premises to transfer the data to Google Cloud Storage, then use BigQuery to analyze the data.

19. Your organization handles a mix of sensitive and non-sensitive data. The sensitive data needs to be retained for five years, while the non-sensitive data needs to be retained for only one year. After these periods, the data should be automatically deleted. Both data types are used infrequently after the first month. How would you design a cost-effective storage solution using Google Cloud Storage (GCS) to handle this requirement?

1. Use a single GCS bucket in the Standard storage class with lifecycle rules to delete objects after 1 and 5 years.

2. Use two separate GCS buckets, one for each data type, both in the Standard storage class, with lifecycle rules to delete objects after 1 and 5 years respectively.

3. Use two separate GCS buckets, one for each data type, both in the Nearline storage class, with lifecycle rules to delete objects after 1 and 5 years respectively.

4. Use a single GCS bucket in the Nearline storage class with lifecycle rules to delete objects after 1 and 5 years.

20. You are managing a cloud environment where BigQuery is extensively used for data analytics. Recently, you observed an increase in the cost due to a large number of complex queries. You want to optimize the cost without compromising query performance. What should you do?

1. Migrate the data to Cloud SQL.

2. Increase the number of slots in BigQuery Reservations.

3. Use Cloud Dataprep for data transformation.

4. Implement BigQuery partitioned tables.

21. Your company has an extensive Google Cloud Dataflow pipeline that processes real-time data from various sources. You are asked to minimize the latency of the pipeline while maximizing the resource utilization. You have already optimized the pipeline code for performance. Which of the following strategies should you adopt next?

1. Use autoscaling and balance the number of worker machines according to CPU and memory utilization.

2. Use a large number of low-memory worker machines.

3. Use autoscaling and set the maximum number of worker machines as high as possible.

4. Use a fixed number of worker machines that equals the number of cores in your most powerful machine.

22. You are designing a data processing solution in Google Cloud Platform for a system that ingests large volumes of streaming data. The data needs to be processed in real-time and then stored for later analysis. What is the most effective solution to implement this requirement?

1. Use Cloud Functions to process each data point in real-time and then save it in Firestore.

2. Utilize Cloud Pub/Sub for data ingestion, followed by storing the data directly in Cloud SQL for real-time processing.

3. Directly stream data into BigQuery and use its built-in capabilities for real-time analysis.

4. Use Cloud Dataflow for real-time processing and then store the processed data in BigQuery.

23. You are designing a data pipeline for a streaming service that has user interaction logs stored in Cloud Storage. The pipeline needs to process these logs and store the processed data in a way that allows complex SQL queries and real-time analytics. Additionally, the company wants to visualize key metrics on an interactive dashboard. What combination of Google Cloud products would you recommend?

1. Use Cloud Functions for processing, BigQuery for storage and analytics, and Looker for visualization.

2. Use Cloud Dataproc for processing, Firestore for storage and analytics, and Data Studio for visualization.

3. Use Cloud Dataflow for processing, Cloud Spanner for storage and analytics, and Looker for visualization.

4. Use Cloud Dataflow for processing, BigQuery for storage and analytics, and Data Studio for visualization.

24. A multinational e-commerce company is aiming to track user behaviors on its platform to provide more personalized recommendations. The data comes in continuously, and the analytics team wants to be able to analyze the latest user interactions as quickly as possible. As a data engineer, which Google Cloud product would be the most appropriate solution to this requirement?

1. Cloud Dataproc

2. Cloud Bigtable

3. Cloud Dataflow

4. Cloud Pub/Sub

25. Your organization has developed a machine learning model to provide real-time product recommendations to users on your e-commerce website. The model must serve predictions for millions of users concurrently with low latency. Which of the following serving infrastructures would be most suitable for this requirement?

1. Use Cloud AI Platform Prediction with a standard machine type.

2. Serve the model using Cloud Run with the model stored in Cloud Storage.

3. Serve the model using Cloud Functions with the model stored in Google Cloud Storage.

4. Use Cloud AI Platform Prediction with a custom prediction routine and high-memory machine type.

26. Your company developed a machine learning model for facial recognition to be used in a security system with cameras deployed in multiple remote locations with limited internet connectivity. The model should make predictions at the edge due to latency and bandwidth concerns. Which of the following serving infrastructures would be most suitable for this requirement?

1. Use TensorFlow Lite to convert the model and deploy it on the edge devices.

2. Serve the model using Google Cloud AI Platform Prediction with a standard machine type.

3. Serve the model using Cloud Functions with the model stored in Google Cloud Storage.

4. Serve the model using Cloud Run with the model stored in Google Cloud Storage.

27. Your organization's BigQuery environment has been experiencing slower than expected query response times. This slowdown is affecting several critical reporting tasks. You need to identify the cause of these delays to optimize query performance. What should you do?

1. Immediately increase the number of BigQuery slots allocated to your project.

2. Reduce the data retention period in BigQuery to decrease the total volume of data.

3. Split your larger tables into smaller ones to reduce the data scanned per query.

4. Use the BigQuery Query Plan Explanation to analyze execution details of slow queries.

28. Your company plans to build an AI-powered analytics platform that should be capable of ingesting and processing large amounts of structured and unstructured data. The platform should also be flexible and portable to align with potential future changes in business requirements, such as migration to a different cloud provider or back to an on-premises solution. Which solution would best suit these requirements?

1. Utilize Cloud AutoML for the AI models, and Cloud Dataflow for data processing.

2. Utilize Cloud AI Platform for the AI models, and Cloud Dataflow for data processing.

3. Implement TensorFlow for the AI models, and Apache Beam with a suitable runner for data processing.

4. Implement TensorFlow for the AI models, and Cloud Dataflow for data processing.

29. You are a data engineer in a healthcare company that uses Google Cloud Platform. Your team has developed a machine learning model to predict patient outcomes. To ensure compliance with healthcare regulations, the model should only be accessible by certain team members. Which of the following would be the best way to control access to the model?

1. Use Cloud KMS to encrypt the model and share the decryption keys only with authorized team members.

2. Use Cloud Identity-Aware Proxy to control access to the model.

3. Store the model in Cloud Storage and limit access using IAM policies.

4. Use VPC Service Controls to isolate the model in a secure perimeter.

30. Your company is deploying a new data pipeline on Google Cloud Dataflow. The pipeline is expected to process both batch and real-time data from various sources. As a data engineer, you are tasked with designing a strategy for quality control and testing of the data pipeline. Which of the following should be the core part of your strategy?

1. Implement Google Cloud Data Catalog for data discovery and metadata management.

2. Use Google Cloud's operations suite for monitoring the Dataflow pipeline.

3. Add data validation and sanitization steps using Apache Beam's PTransform in the Dataflow pipeline.

4. Implement Cloud Data Loss Prevention (DLP) to protect sensitive data.

31. Your organization is in the process of setting up an ETL pipeline to process large volumes of structured data, which is stored in Google Cloud Storage. The processed data should be ready for use in BigQuery for further analysis. You also want the pipeline to be flexible to accommodate changes in the data processing stages. Which of the following GCP services would you recommend?

1. Cloud Dataproc for ETL operations and Cloud Dataflow to load the data into BigQuery.

2. Cloud Pub/Sub for ETL operations and Cloud Dataflow to load the data into BigQuery.

3. Cloud Dataflow for both ETL operations and loading the data into BigQuery.

4. Cloud Dataprep for ETL operations and Cloud Dataflow to load the data into BigQuery.

32. As a data engineer, you've been tasked with improving a supervised learning model that's been deployed on Google Cloud's AI Platform. The model's current evaluation metrics indicate a high bias problem. In order to troubleshoot and address this issue, which of the following steps should you consider?

1. Increase the complexity of the model.

2. Use a smaller training dataset.

3. Decrease the complexity of the model.

4. Use Cloud Monitoring to track the model's performance metrics.

33. Your organization has a set of machine learning models that need to be served for both online interactive predictions and batch predictions. The models were trained using Tensorflow and the serving infrastructure should be scalable, resilient, and capable of handling a high volume of queries. Additionally, cost optimization is a critical factor for your organization. Which of the following Google Cloud Platform services would be the best fit for these requirements?

1. Use AI Platform Predictions for both online and batch predictions.

2. Use Cloud Functions for online predictions and AI Platform Predictions for batch predictions.

3. Use Cloud Run for online predictions and AI Platform Predictions for batch predictions.

4. Use AI Platform Predictions for online predictions and Cloud Dataflow for batch predictions.

34. You are tasked to build a data pipeline for a financial institution, which requires sensitive data to be pseudonymized before being loaded into BigQuery for analysis. The volume of data is massive and the pseudonymization process needs to be efficient. Which of the following solutions would you recommend?

1. Use Cloud Pub/Sub to ingest the data, Dataflow to pseudonymize the data, and then load it into BigQuery.

2. Use Cloud Storage to ingest the data, Dataflow to pseudonymize the data, and then load it into BigQuery.

3. Load the data directly into BigQuery, then use SQL queries to pseudonymize the data in-place.

4. Use Cloud Storage to ingest the data, Cloud DLP to pseudonymize the data, and then load it into BigQuery.

35. You are designing a data ingestion system on Google Cloud that is expected to handle high volumes of streaming data. The system must provide reliable and accurate processing of the data. What should be your primary strategy?

1. Increase the number of BigQuery slots to the maximum at all times to ensure that all incoming data can be processed immediately.

2. Use Cloud Functions for processing the data as they automatically scale based on the number of incoming requests.

3. Use a single large instance for data processing to avoid potential issues with distributed processing.

4. Implement idempotent processing in your data pipeline to ensure that repeated processing of the same data does not lead to incorrect results.

FAQs

1. What is the GCP Professional Data Engineer certification?

The GCP Professional Data Engineer certification validates your ability to design, build, and manage data processing systems on Google Cloud Platform.

2. Is GCP Data Engineer certification worth it in 2025?

Yes, it is highly valued and recognized in the cloud industry for professionals aiming to work with big data and machine learning.

3. Who should take the Google Cloud Data Engineer certification?

IT professionals working in data engineering, big data analytics, or cloud solutions who want to demonstrate expertise in GCP.

4. What is the role of a Google Professional Data Engineer?

They design data processing systems, build and maintain data pipelines, and ensure data quality and security on Google Cloud.

5. GCP Data Engineer vs AWS Data Engineer – which is better?

Both are excellent, but GCP is often chosen for real-time analytics and AI/ML workloads, while AWS offers broader enterprise tooling.

6. How many questions are there in the GCP Data Engineer exam?

The exam typically includes 50–60 multiple-choice and multiple-select questions.

7. What is the format of the Google Cloud Data Engineer exam?

It's a 2-hour, multiple-choice/multiple-select proctored exam.

8. What topics are covered in the GCP Data Engineer certification?

Topics include data storage, data processing, data security, machine learning, and operations.

9. Are there hands-on labs in the GCP Data Engineer exam?

No, the exam is theoretical, but real-world scenarios and architecture-based questions are included.

10. Is the GCP Data Engineer exam multiple choice or practical?

It is multiple-choice/multiple-select only, with no coding or lab exercises.

11. What is the cost of the GCP Professional Data Engineer exam?

The exam costs $200 USD (plus taxes, where applicable).

12. Are there any prerequisites for GCP Data Engineer certification?

No official prerequisites, but experience with GCP and data processing is highly recommended.

13. Can beginners take the GCP Data Engineer exam?

Yes, but they should have strong preparation and hands-on practice with GCP data tools.

14. How much experience is needed for Google Data Engineer certification?

Google recommends at least 1 year of experience with GCP and 3+ years of industry experience in data-related roles.

15. What is the passing score for the GCP Data Engineer exam?

Google does not publish the exact score, but candidates estimate 70% is needed to pass.

16. How is the GCP Data Engineer exam scored?

It's scored on a scale with pass/fail status provided after completion.

17. What is the retake policy for the Google Cloud Data Engineer exam?

You can retake it after 14 days, and additional wait time applies after multiple failures.

18. What happens if I fail the GCP Professional Data Engineer exam?

You must wait for the required period and pay the full exam fee to retake.

19. How to prepare for the GCP Professional Data Engineer certification?

Use CertiMaan’s practice tests and the official Google Cloud Skill Boost learning path for guided preparation.

20. What are the best study materials for GCP Data Engineer exam?

CertiMaan offers exam-focused dumps, while Google’s official documentation and Skill Boost courses provide structured learning.

21. Are there free resources for GCP Data Engineer exam preparation?

Yes, CertiMaan provides free sample questions, and Google Cloud offers free tier labs and documentation.

22. How long does it take to prepare for the GCP Data Engineer certification?

Typically, 4–6 weeks of focused preparation is enough, depending on your background.

23. How long is the GCP Data Engineer certification valid?

The certification is valid for 2 years from the date of passing.

24. Does the Google Data Engineer certification expire?

Yes, it expires after 2 years unless renewed.

25. How do I renew my GCP Professional Data Engineer certification?

You must retake and pass the latest version of the exam before it expires.

26. What jobs can I get with a GCP Professional Data Engineer certification?

Roles include Data Engineer, Cloud Data Architect, ML Engineer, and Big Data Specialist.

27. What is the average salary of a GCP Certified Data Engineer?

The average salary ranges between $120,000 to $160,000 per year in the U.S.

28. Which companies hire GCP Professional Data Engineers?

Google, Deloitte, Accenture, Infosys, and other cloud-focused companies frequently hire GCP Data Engineers.

29. Is the GCP Data Engineer certification good for a career in data?

Yes, it’s highly respected and can significantly boost your data career opportunities in cloud-focused environments.