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Last Updated: Oct 11, 2026
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Ensure Solution Quality
- Ensure Fidelity & Reliability: The applicants should be able to carry out data preparation & quality control (such as Cloud Dataprep), verify and monitor, as well as plan, execute, and stress test data recovery (including rerunning failed jobs, fault tolerance, and retrospective re-analysis performance). Besides that, they should be able to choose between idempotent ACID and eventual consistent prerequisites;
- Ensure Efficiency & Scalability: The potential candidates will be required to demonstrate their ability to build and run test suits as well as monitor pipeline, including Stackdriver. It also focuses on their skills related to assessing, improving, and troubleshooting data process infrastructure and data representations. This area will also require that the test takers demonstrate the capacity to resize and autoscale resources;
- Ensure Portability & Flexibility: The considerations for this domain include the design for application and data portability, including data residency prerequisites and Multiple-Cloud. It also coves data staging, discovery, and cataloging, as well as mapping to future and current business prerequisites.
- Design for Compliance & Security: The consideration for this topic includes identity & access management such as Cloud IAM. You should also know about data security (including key management and encryption) and privacy assurance (such as Data Loss Prevention API). This part also covers the skills needed in legal compliance, including Health Insurance Portability & Accountability Act, FedRAMP, Children’s Online Privacy Protection Act, and General Data Protection Regulation;
The Professional Data Engineer exam is the industry-standard exam that proves the candidate’s ability to do data-driven decision-making by assembling, transforming, and publishing data. If you are rooting for a career in data engineering, you should take this test. It will lead you to attain the Professional Data Engineer certification issued by Google.
Reference: https://cloud.google.com/certification/data-engineer
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The candidates must develop practical skills in the exam topics to succeed. These objectives are highlighted below:
Design Data Processing Systems
- Select the Relevant Storage Technologies: The considerations for this area include mapping storage systems to the business needs, data modeling, distributed systems, as well as tradeoffs, involving transactions, throughput, and latency;
- Migrate Data Processing & Data Warehousing: This section includes validating migrations, migration from on-premises to Cloud, and awareness of the current state & how to migrate designs to the future state.
- Design Data Processing Solutions: This topic includes the individuals’ expertise in planning, distributed systems usage, choice of infrastructure, hybrid Cloud & edge computing, system availability & fault tolerance. You should also know about the architecture options, including message queues, message brokers, service-oriented architecture, middleware, and serverless function;
- Design Data Pipeline: The focus for this subsection includes data visualization & publishing and batch & streaming data (Cloud Dataproc, Cloud Dataflow, Cloud Sub/Pub, Hadoop ecosystem, Apache Spark, Apache Beam, and Apache Kafka). It also focuses on online versus batch prediction and job orchestration & automation;
Google Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Ensuring solution quality and reliability | 17% | - Testing and validating data systems
|
| Designing data processing systems | 20% | - Designing for business requirements
|
| Maintaining and automating data workloads | 18% | - Resource optimization
|
| Operationalizing machine learning models | 20% | - Deploying and maintaining ML models
|
| Building and operationalizing data processing systems | 25% | - Deploying and managing systems
|
Google Certified Professional Data Engineer Sample Questions:
You are implementing security best practices on your data pipeline. Currently, you are manually executing jobs as the Project Owner. You want to automate these jobs by taking nightly batch files containing non- public information from Google Cloud Storage, processing them with a Spark Scala job on a Google Cloud Dataproc cluster, and depositing the results into Google BigQuery.
How should you securely run this workload?
- A. Use a user account with the Project Viewer role on the Cloud Dataproc cluster to read the batch files and write to BigQuery
- B. Restrict the Google Cloud Storage bucket so only you can see the files
- C. Grant the Project Owner role to a service account, and run the job with it
- D. Use a service account with the ability to read the batch files and to write to BigQuery
Correct Answer: D 🗳️
For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?
- A. Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.
- B. Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.
- C. Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.
- D. Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.
Correct Answer: C 🗳️
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You are on the data governance team and are implementing security requirements. You need to encrypt all your data in BigQuery by using an encryption key managed by your team. You must implement a mechanism to generate and store encryption material only on your on-premises hardware security module (HSM). You want to rely on Google managed solutions. What should you do?
- A. Create the encryption key in the on-premises HSM. Create BigQuery resources and encrypt data while ingesting them into BigQuery.
- B. Create the encryption key in the on-premises HSM and link it to a Cloud External Key Manager (Cloud EKM) key. Associate the created Cloud KMS key while creating the BigQuery resources.
- C. Create the encryption key in the on-premises HSM, and import it into Cloud Key Management Service (Cloud HSM) key. Associate the created Cloud HSM key while creating the BigQuery resources.
- D. Create the encryption key in the on-premises HSM, and import it into a Cloud Key Management Service (Cloud KMS) key. Associate the created Cloud KMS key while creating the BigQuery resources.
Correct Answer: B 🗳️
Business owners at your company have given you a database of bank transactions. Each row contains the user ID, transaction type, transaction location, and transaction amount. They ask you to investigate what type of machine learning can be applied to the data. Which three machine learning applications can you use? (Choose three.)
- A. Supervised learning to predict the location of a transaction.
- B. Unsupervised learning to predict the location of a transaction.
- C. Reinforcement learning to predict the location of a transaction.
- D. Unsupervised learning to determine which transactions are most likely to be fraudulent.
- E. Supervised learning to determine which transactions are most likely to be fraudulent.
- F. Clustering to divide the transactions into N categories based on feature similarity.
Correct Answer: A,D,F 🗳️
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You need to migrate a Redis database from an on-premises data center to a Memorystore for Redis instance. You want to follow Google recommended practices and perform the migration for minimal cost, time and effort. What should you do?
- A. Make a secondary instance of the Redis database on a Compute Engine instance and then perform a live cutover.
- B. Write a shell script to migrate the Redis data and create a new Memorystore for Redis instance.
- C. Make an RDB backup of the Redis database, use the gsutil utility to copy the RDB file into a Cloud Storage bucket, and then import the RDB file into the Memorystore for Redis instance.
- D. Create a Dataflow job to read the Redis database from the on-premises data center and write the data to a Memorystore for Redis instance.
Correct Answer: C 🗳️
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