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Google Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Designing data processing systems | 20% | - Designing for regulatory and security requirements
|
| Topic 2: Ensuring solution quality and reliability | 17% | - Troubleshooting and optimization
|
| Topic 3: Maintaining and automating data workloads | 18% | - Automation and repeatability
|
| Topic 4: Operationalizing machine learning models | 20% | - Preparing data for ML
|
| Topic 5: Building and operationalizing data processing systems | 25% | - Building data pipelines
|
Professional-Data-Engineer Exam FAQ: Updates, Speed, Simulation
Three versions of the same study materials: a printable, expert-prepared PDF with free demo download and instant access; a Desktop Test Engine for Windows with customizable simulation and timed modes that works offline; and an Online Test Engine for any browser on Windows, Mac, Android, and iOS with test history and performance review. All 433 practice questions are identical across versions. Every purchase includes 365 days of free updates, a 50% renewal discount afterward, and unlimited computer installations.
The Google Certified Professional Data Engineer exam contains 40 - 50 to complete within 120 minutes. Timed simulation in the ExamTorrent Desktop Test Engine teaches that rhythm before exam day does.
The official Google Certified Professional Data Engineer outline defines 5 domains. The leading three are Ensuring solution quality and reliability (17%), Operationalizing machine learning models (20%), and Building and operationalizing data processing systems (25%). The full list is in the topics section above, and ExamTorrent's 433 practice questions cover each one.
No mandatory prerequisites; recommended 3+ years industry experience, including 1+ year designing and managing Google Cloud data solutions Since requirements change over time, confirm the latest on the official Google exam page before scheduling.
The Google Certified Professional Data Engineer exam requires 700 / 1000 to pass, and registration costs USD 200 (plus tax where applicable). A failed attempt means paying the same fee again — a readiness check with the ExamTorrent engines before booking is the cheaper option.
The Professional-Data-Engineer exam is a Google certification exam validating the Google Certified Professional Data Engineer syllabus shown above. It belongs to these credential paths: Google Cloud Associate Cloud Engineer, Google Cloud Professional Cloud Architect, Google Cloud Professional Data Analyst. Candidates in 2026 start with ExamTorrent's 433 practice questions, available as a printable PDF, a Windows Desktop Test Engine, and an Online Test Engine — three versions of the same materials.
Google recommends these training resources for the Google Certified Professional Data Engineer exam:
Follow the training with steady self-testing — the 433 practice questions from ExamTorrent help you apply theory and fill any gaps the training leaves.
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Google Certified Professional Data Engineer Sample Questions:
You issue a new batch job to Dataflow. The job starts successfully, processes a few elements, and then suddenly fails and shuts down. You navigate to the Dataflow monitoring interface where you find errors related to a particular DoFn in your pipeline. What is the most likely cause of the errors?
- A. Exceptions in worker code
- B. Graph or pipeline construction
- C. Insufficient permissions
- D. Job validation
Correct Answer: A 🗳️
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You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?
- A. Use Cloud Dataflow to run your transformations. Monitor the total execution time for a sampling of jobs. Configure the job to use non-default Compute Engine machine types when needed.
- B. Use Cloud Dataflow to run your transformations. Monitor the job system lag with Stackdriver. Use the default autoscaling setting for worker instances.
- C. Use Cloud Dataproc to run your transformations. Monitor CPU utilization for the cluster. Resize the number of worker nodes in your cluster via the command line.
- D. Use Cloud Dataproc to run your transformations. Use the diagnose command to generate an operational output archive. Locate the bottleneck and adjust cluster resources.
Correct Answer: B 🗳️
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An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application. They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?
- A. Cloud SQL
- B. Cloud Datastore
- C. BigQuery
- D. Cloud BigTable
Correct Answer: A 🗳️
You are migrating a table to BigQuery and are deciding on the data model. Your table stores information related to purchases made across several store locations and includes information like the time of the transaction, items purchased, the store ID, and the city and state in which the store is located. You frequently query this table to see how many of each item were sold over the past 30 days and to look at purchasing trends by state, city, and individual store. How would you model this table for the best query performance?
- A. Top-level cluster by store ID first, then city, then state.
- B. Top-level cluster by state first, then city, then store ID.
- C. Partition by transaction time; cluster by store ID first, then city, then state.
- D. Partition by transaction time; cluster by state first, then city, then store ID.
Correct Answer: D 🗳️
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You have a BigQuery table that ingests data directly from a Pub/Sub subscription. The ingested data is encrypted with a Google-managed encryption key. You need to meet a new organization policy that requires you to use keys from a centralized Cloud Key Management Service (Cloud KMS) project to encrypt data at rest. What should you do?
- A. Create a new BigQuery table by using customer-managed encryption keys (CMEK), and migrate the data from the old BigQuery table.
- B. Create a new Pub/Sub topic with CMEK and use the existing BigQuery table by using Google- managed encryption key.
- C. Use Cloud KMS encryption key with Dataflow to ingest the existing Pub/Sub subscription to the existing BigQuery table.
- D. Create a new BigQuery table and Pub/Sub topic by using customer-managed encryption keys (CMEK), and migrate the data from the old BigQuery table.
Correct Answer: A 🗳️
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