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Ensure Solution Quality
- 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.
- 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;
- 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;
Data Engineering on Google Cloud course
It is a 4-day course that gives hands-on experience to the candidates and allows them to build data processing systems on Google Cloud. It will also show you how to design data processing systems, analyze data and build end-to-end data pipelines and machine learning. In order to get a better understanding of the course, you need to complete the big data machine learning course or get equivalent experience. This course also aids you in developing applications using a programming language such as Python and covers the following objective:
- Processing batch and streaming data by using autoscaling data pipelines on Cloud Dataflow
- Enable insights from streaming data
- Predicting machine models using TensorFlow and Cloud ML
- Designing and building data processing systems on the Google Cloud Platform
- Influencing unstructured data using ML APIs on Cloud Dataproc
Google Professional-Data-Engineer Exam Overview:
| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Professional Data Engineer Exam |
| Exam Number: | Professional-Data-Engineer |
| Exam Duration: | 120 minutes |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 40 - 50 |
| Related Certifications: | Google Cloud Associate Cloud Engineer Google Cloud Professional Cloud Architect Google Cloud Professional Data Analyst |
| Exam Price: | USD 200 (plus tax where applicable) |
| Exam Format: | Multiple select, Multiple choice |
| Available Languages: | Japanese, English |
| Passing Score: | 700 / 1000 |
| Recommended Training: | Google Cloud Professional Data Engineer Learning Path Official Exam Guide |
| Exam Registration: | Google Cloud Certification Registration |
| Sample Questions: | Google Professional-Data-Engineer Sample Questions |
| Exam Way: | Online-proctored or onsite-proctored |
| Pre Condition: | No mandatory prerequisites; recommended 3+ years industry experience, including 1+ year designing and managing Google Cloud data solutions |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/data-engineer |
Understanding functional and technical aspects of Google Professional Data Engineer Exam Ensuring solution quality
The following will be discussed here:
- Designing for data and application portability (e.g., multi-cloud, data residency requirements)
- Data security (encryption, key management)
- Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children's Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))
- Mapping to current and future business requirements
- Identity and access management (e.g.,Cloud IAM)
- Ensuring reliability and fidelity
- Resizing and autoscaling resources
- Performing data preparation and quality control (e.g., Cloud Dataprep)
- Assessing, troubleshooting, and improving data representations and data processing infrastructure
- Building and running test suites
- Ensuring flexibility and portability
- Ensuring scalability and efficiency
- Pipeline monitoring (e.g., Stackdriver)
- Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
- Data staging, cataloging, and discovery
- Verification and monitoring
- Ensuring privacy (e.g., Data Loss Prevention API)
- Choosing between ACID, idempotent, eventually consistent requirements
- Designing for security and compliance
Google Professional-Data-Engineer Exam Syllabus Topics:
| Topic | Details |
|---|---|
| Topic 1 |
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| Topic 2 |
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| Topic 3 |
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| Topic 4 |
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| Topic 5 |
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Reference: https://cloud.google.com/certification/data-engineer
Google Certified Professional Data Engineer Sample Questions:
You are migrating your on-premises data warehouse to BigQuery. As part of the migration, you want to facilitate cross-team collaboration to get the most value out of the organization's data.
You need to design an architecture that would allow teams within the organization to securely publish, discover, and subscribe to read-only data in a self-service manner. You need to minimize costs while also maximizing data freshness. What should you do?
- A. Create authorized datasets to publish shared data in the subscribing team's project.
- B. Create a new dataset for sharing in each individual team's project. Grant the subscribing team the bigquery dataViewer role on the dataset.
- C. Use Analytics Hub to facilitate data sharing.
- D. Use BigQuery Data Transfer Service to copy datasets to a centralized BigQuery project for sharing.
Correct Answer: C 🗳️
Explanation: Only visible for TopExamCollection members. You can sign-up / login (it's free).
You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?
- A. Convert all daily log tables into date-partitioned tables
- B. Enable query caching so you can cache data from previous months
- C. Create separate views to cover each month, and query from these views
- D. Convert the sharded tables into a single partitioned table
Correct Answer: D 🗳️
Explanation: Only visible for TopExamCollection members. You can sign-up / login (it's free).
You are designing a stateful data processing pipeline that reads data from a Cloud Storage bucket and writes transformed data to a BigQuery table. The pipeline must be highly available and resilient to zonal failures within the us-central1 region. You need to configure a Dataflow pipeline ensuring minimal disruption during a zonal outage. What should you do?
- A. Launch the Dataflow job with the --zone=us-central1a parameter.
- B. Deploy the Dataflow job to a single zone within us-central1 and use a multi-regional Cloud Storage bucket to store its state.
- C. Launch the Dataflow job with the --region=us-central1 parameter.
- D. Deploy the Dataflow job to a single zone within us-central1 and configure it to use a regional persistent disk to store its state.
Correct Answer: C 🗳️
Explanation: Only visible for TopExamCollection members. You can sign-up / login (it's free).
You have two projects where you run BigQuery jobs:
- One project runs production jobs that have strict completion time
SLAs. These are high priority jobs that must have the required compute
resources available when needed. These jobs generally never go below a
300 slot utilization, but occasionally spike up an additional 500
slots.
- The other project is for users to run ad-hoc analytical queries. This project generally never uses more than 200 slots at a time. You want these ad-hoc queries to be billed based on how much data users scan rather than by slot capacity.
You need to ensure that both projects have the appropriate compute resources available. What should you do?
- A. Create two Enterprise Edition reservations, one for each of the projects. For the SLA project, set a baseline of 800 slots. For the ad-hoc project, enable autoscaling up to 200 slots.
- B. Create two Enterprise Edition reservations, one for each of the projects. For the SLA project, set a baseline of 300 slots and enable autoscaling up to 500 slots. For the ad-hoc project, set a reservation baseline of 0 slots and set the ignore idle slots flag to False.
- C. Create a single Enterprise Edition reservation for both projects. Set a baseline of 300 slots.
Enable autoscaling up to 700 slots. - D. Create two reservations, one for each of the projects. For the SLA project, use an Enterprise Edition with a baseline of 300 slots and enable autoscaling up to 500 slots. For the ad-hoc project, configure on-demand billing.
Correct Answer: D 🗳️
A shipping company has live package-tracking data that is sent to an Apache Kafka stream in real time. This is then loaded into BigQuery. Analysts in your company want to query the tracking data in BigQuery to analyze geospatial trends in the lifecycle of a package. The table was originally created with ingest-date partitioning. Over time, the query processing time has increased. You need to implement a change that would improve query performance in BigQuery.
What should you do?
- A. Implement clustering in BigQuery on the package-tracking ID column.
- B. Tier older data onto Cloud Storage files, and leverage extended tables.
- C. Implement clustering in BigQuery on the ingest date column.
- D. Re-create the table using data partitioning on the package delivery date.
Correct Answer: A 🗳️
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