GCP Professional Data Engineer Sample Practice Exam Questions -PDE - 001 ( 2026 )

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.

36. You have developed a machine learning model to predict sales for a retail company using historical data. Recently, the company expanded its online presence, significantly changing its sales patterns. The model's accuracy has decreased since this change. What approach should you take to improve the model's performance?

1. Discard the old model and develop a new one exclusively with online sales data.

2. Adjust the model's hyperparameters without retraining to adapt to the new sales data.

3. Continue using the current model, as it will adapt to new sales patterns over time.

4. Retrain the model with a combination of historical data and recent online sales data.

37. You are designing a data processing solution for an e-commerce company. The company generates large amounts of transactional and clickstream data that need to be ingested in real-time, processed, and analyzed for trends and insights. The processed data should also be available for ad-hoc querying. Given the need for real-time processing, scalability, and data querying, which of the following Google Cloud services should you primarily use to design this solution?

1. Cloud Datastore

2. Cloud Storage

3. Cloud BigTable

4. Cloud Pub/Sub and Cloud Dataflow

38. You are working as a data engineer in an e-commerce company. The company wants to design a database schema for a new application that will store user profiles, products, and transactions. The application requires high write speed for the transactions and complex SQL queries for analytical reports. Which storage technology and schema design would you recommend?

1. Cloud BigQuery with star schema

2. Cloud Spanner with normalized schema

3. Cloud Firestore with snowflake schema

4. Cloud Bigtable with denormalized schema

39. You are designing a data pipeline for a news agency that publishes stories globally. The agency requires real-time analytics on their published stories' views, likes, and comments. They want to visualize this data on an interactive dashboard and want to keep the data for historical analysis. What set of Google Cloud products would you recommend?

1. Use Cloud Pub/Sub for data ingestion, Cloud Functions for processing, Firestore for storage, and Data Studio for visualization.

2. Use Cloud Functions for data ingestion, Cloud Dataproc for processing, BigQuery for storage, and Looker for visualization.

3. Use Cloud Pub/Sub for data ingestion, Cloud Dataflow for processing, and BigQuery for storage and visualization.

4. Use Cloud Pub/Sub for data ingestion, Cloud Dataflow for processing, BigQuery for storage, and Data Studio for visualization.

40. A startup developing a real-time facial recognition software has chosen to use Google Cloud Platform for their infrastructure needs. The model is expected to make predictions on a stream of video data with high throughput. Which hardware accelerator and serving infrastructure would be the most appropriate to use for this scenario?

1. AI Platform Predictions with TPU

2. AI Platform Predictions with CPU

3. Cloud Run with TPU

4. AI Platform Predictions with GPU

41. You're designing a pipeline to ingest a vast volume of structured log data into BigQuery for analysis. The log data is generated by multiple services running in Compute Engine and is currently stored in Cloud Storage. You need a solution that can handle the large volume, ensures the data's availability immediately for querying, and minimizes cost. Which method should you use to ingest this data into BigQuery?

1. Use Cloud Storage Transfer Service to move data from Cloud Storage to BigQuery.

2. Use Cloud Dataflow to stream data from Cloud Storage to BigQuery.

3. Use bq load command-line tool to load data from Cloud Storage to BigQuery.

4. Use BigQuery Data Transfer Service to schedule daily transfers from Cloud Storage to BigQuery.

42. Your organization is deploying a complex data processing application in Google Cloud Platform (GCP). The data used by the application is sensitive, so it's crucial that the data is encrypted both at rest and in transit. Additionally, the organization wants to retain full control over the encryption keys. What should you use to meet these requirements?

1. Hardware Security Module (HSM)

2. Google Cloud KMS (Key Management Service)

3. Google-Managed Encryption Keys (GMEK)

4. Customer-Supplied Encryption Keys (CSEK)

43. As a data engineer, you are responsible for maintaining a data pipeline that processes IoT data using Google Cloud Dataflow. The pipeline was initially designed to handle a lower volume of data, but now the incoming data volume has significantly increased. You need to adjust the pipeline to efficiently process the larger volumes of data. Which action would be the most effective in handling the increased data volume?

1. Enable autoscaling in your Dataflow pipeline.

2. Use a larger disk size for your Dataflow worker machines.

3. Convert your streaming pipeline to a batch pipeline.

4. Increase the memory of your Dataflow worker machines.

44. Your company is developing a smart assistant for mobile devices. This assistant needs to process user commands given through voice and images, like identifying objects in a picture or transcribing and executing voice commands. Which combination of Google Cloud's pre-built ML models and APIs would you use for this task?

1. Cloud Natural Language API + Cloud Translation API

2. Cloud Natural Language API + Cloud Speech-to-Text API

3. Cloud Translation API + Cloud Video Intelligence API

4. Cloud Speech-to-Text API + Cloud Vision API

45. Your company is using Google Cloud Storage (GCS) to store sensitive data. You need to ensure that a new remote team member can securely access this data without exposing it to unnecessary risks. What is the best practice to achieve this?

1. Share the storage bucket's public URL with the team member and restrict access based on their IP address.

2. Create a dedicated service account for the team member and provide them with the service account key for direct access.

3. Grant the team member necessary permissions using Identity and Access Management (IAM) and enforce access through a secure VPN connection.

4. Set the storage bucket to public and monitor access logs to ensure only the team member is accessing it.

46. You are a data engineer for a global company that has operations in several countries. Your company generates a significant amount of data on a daily basis and has a complex data pipeline for managing it. The company uses different Cloud Storage buckets located in various regions to store data. The company's current data pipeline architecture involves ingesting raw data, transforming it, and then storing it in BigQuery for analysis. As the company is scaling, there have been delays in data availability and issues with resource utilization. Which of the following solutions should you implement to ensure optimal resource utilization and timely data availability?

1. Increase the number of virtual machines that process the data.

2. Change the data storage from Cloud Storage to Cloud SQL.

3. Change the data storage location to a single region to ensure data consistency.

4. Implement Cloud Dataflow for efficient processing and transformation of both streaming and batch data.

47. Your organization is looking to migrate a large-scale, read-heavy analytical workload from an on-premises data warehouse to Google Cloud. The data is used for complex queries and business intelligence reporting. You need a solution that offers high throughput for read operations and integrates well with data analytics tools. What should you do?

1. Migrate to Compute Engine with Hadoop clusters.

2. Migrate to Firestore.

3. Migrate to BigQuery.

4. Migrate to Cloud SQL for MySQL.

48. Your company is creating a global distributed system that requires strong consistency, the ability to handle a large volume of read and write operations, and the capacity to store multi-structured data. The storage system should also be able to automatically scale up and down according to the varying workloads. Considering these requirements, which Google Cloud storage technology would you recommend?

1. Cloud SQL

2. Google Cloud Storage

3. Cloud Spanner

4. Cloud Bigtable

5. BigQuery

49. Your company has asked you to troubleshoot a machine learning model that has been underperforming. Upon reviewing the input data for the model, you notice that there are several assumptions about the data that have been violated. Which of the following is the most likely source of error?

1. The model was trained on data with a different distribution than the data it is predicting on.

2. The model was trained with a large amount of missing data.

3. The model was trained on data that has not been properly normalized or standardized.

4. The model was trained on data with a high degree of multicollinearity.

50. A multinational organization that uses Google Cloud has decided to shift its on-premises data warehouse to BigQuery. The existing system has around 50 TB of data and their connection to the internet has an upload speed of 100 Mbps. They plan to perform the migration in less than 4 weeks without interrupting the daily operations. They also want to keep the downtime during the final switchover to less than 6 hours. Which of the following strategies should they use to achieve this?

1. Use Cloud Dataflow to extract, transform, and load data into BigQuery.

2. Use Cloud VPN along with gsutil to securely transfer the data directly into BigQuery.

3. Use gsutil to upload data to a Cloud Storage bucket and then load data into BigQuery.

4. Use Cloud Storage Transfer Service to transfer data to a Cloud Storage bucket and then load data into BigQuery.

51. You are designing a system to process data from various sources, including batch and streaming data, which is located across multiple cloud platforms and on-premises. The system should be flexible, portable, and capable of staging and cataloging the data for discovery and analytics. What should you recommend?

1. Use AWS Lambda for data processing and AWS Glue Data Catalog for data cataloging.

2. Use Cloud Pub/Sub for data processing and Google Cloud Data Catalog for data cataloging.

3. Use Apache Beam with a suitable runner for data processing and Apache Atlas for data cataloging.

4. Use Cloud Dataflow for data processing and Google Cloud Data Catalog for data cataloging.

52. Your organization is developing an AI system to analyze a stream of high-velocity social media data. The system must be capable of handling structured and unstructured data. The data will initially be ingested, then analyzed using real-time analytics, and then will be used for historical data analysis in the future. Which storage system would be the most appropriate for this use case?

1. Cloud SQL

2. Bigtable

3. Cloud Spanner

4. Cloud Storage

53. As a data engineer for an e-commerce company, you have been tasked with designing a cloud-based storage system. The system should be able to store structured and semi-structured data, provide real-time insights, have low latency for small reads and writes, and be highly available. Which Google Cloud Storage system would be the best fit for this requirement?

1. Firestore

2. Cloud Storage

3. BigQuery

4. Cloud Spanner

54. Your company wants to train a machine learning model using Google Cloud ML Engine on data stored in Cloud Storage. They want to use custom TensorFlow code, and you have to provision resources for this task. However, the model has high memory requirements and needs to be trained quickly. Which configuration would best suit this scenario?

1. Configure the ML Engine with high-memory machine type and add GPUs.

2. Configure the ML Engine with high-CPU machine type.

3. Configure the ML Engine with basic GPU.

4. Configure the ML Engine with standard machine type and add GPUs.

55. As a data engineer, you have been asked to integrate data from a third-party vendor's RESTful API. The data from this API needs to be ingested in real-time, transformed, and then stored in BigQuery for real-time analysis. Which approach would you use to implement this?

1. Use Cloud Scheduler to trigger a Cloud Function every minute to pull data from the API, process it, and store it in BigQuery.

2. Use Cloud Pub/Sub to pull data from the API in real-time, and Cloud Dataflow to process and store the data in BigQuery.

3. Use Cloud Dataflow to continuously pull data from the API, process it, and store it in BigQuery.

4. Use Cloud Data Fusion to connect to the RESTful API, transform the data, and load it into BigQuery.

56. A fintech startup is leveraging Google Cloud to build its core banking system. The system should be able to handle high-volume monetary transactions, while ensuring strong data consistency and reliability. It also needs to recover gracefully in case of system failures. However, the team has varying opinions on the system requirements. What approach would best suit the system's needs?

1. The system should follow an idempotent approach for handling transactions.

2. The system should bypass ACID properties to improve performance.

3. The system should maintain ACID properties to ensure transaction consistency.

4. The system should be eventually consistent to handle high-volume transactions.

57. You are a data engineer designing a data processing solution for a telecommunication company with a network of IoT devices deployed at various edge locations. The solution needs to process data at the edge to minimize latency and bandwidth usage. Moreover, it should aggregate processed data on Google Cloud for further analysis. Which design would you propose?

1. Use Cloud IoT Edge for edge processing, Cloud Pub/Sub to transmit processed data to Google Cloud, and BigQuery for further analysis.

2. Use Cloud IoT Edge for edge processing, Cloud Storage for temporary data storage at the edge, and Dataproc for data analysis in the cloud.

3. Use Google Cloud IoT Core for device management, Cloud Dataflow for edge processing, and BigQuery for data analysis in the cloud.

4. Use Anthos deployed at edge locations for data processing, Cloud Pub/Sub for transmitting processed data to Google Cloud, and BigQuery for further analysis.

58. You are managing a complex data pipeline which processes large quantities of IoT data. The pipeline uses Cloud Pub/Sub for data ingestion and Cloud Dataflow for processing. You have been asked to ensure the pipeline's scalability and efficiency using Google Cloud Monitoring. What approach should you take?

1. Monitor only the CPU and memory usage of the Pub/Sub and Dataflow instances

2. Monitor the network latency between the Pub/Sub and Dataflow services

3. Use Cloud Monitoring to keep track of the number of active users of your application

4. Create a custom dashboard in Cloud Monitoring to track metrics such as Pub/Sub backlog, Dataflow job system lag, and CPU utilization

59. Your organization uses a complex data pipeline involving Cloud Pub/Sub, Cloud Dataflow, and BigQuery to process high volumes of data. Recently, the system is experiencing efficiency issues due to increased data load. As a data engineer, you are asked to troubleshoot and improve the data processing infrastructure to ensure its scalability and efficiency. Which of the following steps should you consider?

1. Replace BigQuery with Firestore for better read and write performance.

2. Monitor the data pipeline using Cloud Monitoring, focusing on key metrics such as Pub/Sub backlog, Dataflow system lag, and BigQuery slots utilization, and identify areas of improvement based on the gathered metrics.

3. Increase the number of Dataflow workers and BigQuery slots without assessing the nature of the workload.

4. Convert all data into a single format, such as CSV, before processing to simplify the pipeline.

60. Your organization operates a global e-commerce platform with high transaction volumes. You need to design a Cloud Spanner database schema that efficiently handles customer transactions while maintaining strong consistency and global scalability. How should you construct the primary key for the transactions table?

1. Construct the key as transaction-id#customer-id#timestamp.

2. Construct the key as customer-id#timestamp#transaction-id.

3. Construct the key as timestamp#transaction-id#customer-id.

4. Construct the key as product-id#customer-id#transaction-id.

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.