Google Certified Professional Data Engineer : Professional-Data-Engineer

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Ensuring Solution Quality

The last section of the certification exam evaluates the ability of the learners to design for security & compliance, including identity & access management, legal compliance, data security, and privacy ensuring. Moreover, they should be able to ensure flexibility & portability, reliability & fidelity, as well as scalability & efficiency.

Reference: https://cloud.google.com/certification/data-engineer

Career Path

Completing the exam associated with the Google Professional Data Engineer certification provides you with a great validation of your skills in designing, building, operationalizing, securing, and monitoring data processing systems. The job roles that you can take up after getting certified include a Google Cloud Data Engineer, an Operations Engineer, a Cloud Infrastructure Engineer, a DevOps Infrastructure Engineer, a Cloud Database Engineer, a Google Cloud IAM Engineer, a DataOps Engineer, a Big Data Engineer, a Google Cloud Platform Data Architect, and more. The average salary that you can expect to earn with this certificate is around $125,550 per year. However, the real remuneration will depend on a specific job title, location of an individual, and his/her working experience.

Google Professional Data Engineer Practice Test Questions, Google Professional Data Engineer Exam Practice Test Questions

The Google Professional Data Engineer certification is designed to evaluate the candidates’ skills in designing data processing systems and ensuring solution quality. It is also created to measure their competence in building and operationalizing data processing systems and operationalizing ML models. The potential applicants must complete a single exam to get certified.

Google Professional-Data-Engineer Exam Syllabus Topics:

Google Certified Professional Data Engineer Sample Questions:

Question #1
Your company receives both batch- and stream-based event data. You want to process the data using Google Cloud Dataflow over a predictable time period. However, you realize that in some instances data can arrive late or out of order. How should you design your Cloud Dataflow pipeline to handle data that is late or out of order?

A. Set a single global window to capture all the data.
B. Ensure every datasource type (stream or batch) has a timestamp, and use the timestamps to define the logic for lagged data.
C. Set sliding windows to capture all the lagged data.
D. Use watermarks and timestamps to capture the lagged data.

Question #2
You need to modernize your existing on-premises data strategy. Your organization currently uses:
- Apache Hadoop clusters for processing multiple large data sets,
including on-premises Hadoop Distributed File System (HDFS) for data
replication.
- Apache Airflow to orchestrate hundreds of ETL pipelines with
thousands of job steps.
You need to set up a new architecture in Google Cloud that can handle your Hadoop workloads and requires minimal changes to your existing orchestration processes. What should you do?

A. Use Dataproc to migrate your Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Use Cloud Data Fusion to visually design and deploy your ETL pipelines.
B. Use Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Orchestrate your pipelines with Cloud Composer.
C. Use Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Convert your ETL pipelines to Dataflow.
D. Use Bigtable for your large workloads, with connections to Cloud Storage to handle any HDFS use cases. Orchestrate your pipelines with Cloud Composer.

Question #3
You are migrating an application that tracks library books and information about each book, such as author or year published, from an on-premises data warehouse to BigQuery. In your current relational database, the author information is kept in a separate table and joined to the book information on a common key. Based on Google's recommended practice for schema design, how would you structure the data to ensure optimal speed of queries about the author of each book that has been borrowed?

A. Keep the schema the same, create a view that joins all of the tables, and always query the view
B. Create a table that includes information about the books and authors, but nest the author fields inside the author column
C. Keep the schema the same, maintain the different tables for the book and each of the attributes, and query as you are doing today
D. Create a table that is wide and includes a column for each attribute, including the author's first name, last name, date of birth, etc

Question #4
Your chemical company needs to manually check documentation for customer order. You use a pull subscription in Pub/Sub so that sales agents get details from the order. You must ensure that you do not process orders twice with different sales agents and that you do not add more complexity to this workflow.
What should you do?

A. Use Pub/Sub exactly-once delivery in your pull subscription.
B. Create a transactional database that monitors the pending messages.
C. Create a new Pub/Sub push subscription to monitor the orders processed in the agent's system.
D. Use a Deduphcate PTransform in Dataflow before sending the messages to the sales agents.

Question #5
You are building a real-time prediction engine that streams files, which may contain personally identifiable information (PII) data, into Cloud Storage and eventually into BigQuery. You want to ensure that the sensitive data is masked but still maintains referential integrity, because names and emails are often used as join keys. How should you use the Cloud Data Loss Prevention (Cloud DLP) API to ensure that the PII data is not accessible by unauthorized individuals?

A. Create a pseudonym by replacing the Pll data with cryptogenic tokens, and store the non- tokenized data in a locked-down button.
B. Scan every table in BigQuery, and mask the data it finds that has Pll
C. Redact all Pll data, and store a version of the unredacted data in a locked-down bucket
D. Create a pseudonym by replacing Pll data with a cryptographic format-preserving token

Solutions: